Adaptive Imperialist Competitive Algorithm (AICA)
|
|
- Eric Randall Blankenship
- 5 years ago
- Views:
Transcription
1 Adaptive Imperialist Competitive Algorithm () Marjan Abdechiri Elec., comp. & IT Department, Qazvin Azad University, Qazvin, Iran, Karim Faez Electrical Engineering Department, Amirkabir University of Technology, Tehran, Iran, Helena Bahrami Elec., comp. & IT Department, Qazvin Azad University, Qazvin, Iran, Abstract The novel Imperialist Competitive Algorithm () that was recently introduced has a good performance in some optimization problems. The inspired by sociopolitical process of imperialistic competition of human being in the real world. In this paper, a new Adaptive Imperialist Competitive Algorithm () is proposed. In the proposed algorithm, for an effective search, the Absorption Policy changed dynamically to adapt the angle of colonies movement towards imperialist s position. The is easily stuck into a local optimum when solving high-dimensional multi-model numerical optimization problems. To overcome this shortcoming, we use probabilistic model that utilize the information of colonies positions to balance the exploration and exploitation abilities of the imperialistic competitive algorithm. Using this mechanism, exploration capability will enhance. Some famous unconstraint benchmark functions used to test the performance. Also, we use the Algorithm to adjust the weights of a three-layered Perceptron neural network to predict the maximum worth of the stocks change in Tehran s Bourse Market. Simulation results show this strategy can improve the performance of the algorithm significantly. Keywords-Imperialist Competitive Algorithm; absorption policy; density probabilistic model. I. INTRODUCTION The global optimization problem is applicable in every field of science, engineering and business. So far, many Evolutionary Algorithms (EA) [1,2], have been proposed for solving the global optimization problem. Inspired by the natural evolution, EA analogizes the evolution process of biological population, which can adapt the changing environments to the finding of the optimum of the optimization problem through evolving a population of candidate solutions. Some Evolutionary Algorithms for optimization problem are: the Genetic Algorithm () [2,3,4,5,6,7], at first proposed by Holland, in 1962 [4], Particle Swarm Optimization algorithms () [8,9] that at first proposed by Kennedy and Eberhart [8], in 1995, Simulated Annealing (SA) [10,11,12], Cultural Evolutionary algorithms (CE) [13,14] at first was developed by Reynolds, in the early 1990s [14] and etc. The optimization methods are extensively used to adjust the weights of multi-layered Neural Networks. While gradient descent is a very popular optimization method, it plagued by slow convergence and susceptibility to local minima. Therefore, other approaches to improve NN training introduced. These methods include global optimization algorithms, such as Simulated Annealing [15], Genetic Algorithms [16,17], Particle Swarm Optimization algorithms [18,19,20] and other Evolutionary Algorithms. Recently, a new algorithm has been proposed by Atashpaz-Gargari and lucas [21], in 2007 that has inspired from a socio-human phenomenon. In this paper, we have proposed a new algorithm called Adaptive Imperialist Competitive Algorithm () that uses the probability density function to adapt the angle of colonies movement towards imperialist s position during iterations dynamically. This mechanism, enhance the global search capability of the algorithm. This idea increases the performance of the algorithm effectively in solving the optimization problems. We examined the proposed algorithm in several standard benchmark functions that usually tested in Evolutionary Algorithms. Also, we use the Algorithm to adjust the weights of a three-layered Perceptron Neural Network to predict the maximum worth of the stocks change in Tehran s Bourse Market[22]. The results of applying the proposed algorithm on benchmark functions and Neural Network to predict the maximum worth of the stocks change in Tehran s Bourse Market indicated that the convergence speed and the quality of obtained solution in compare with, using a Sugeno function as inertia weight decline curve[23] and algorithm show a good performance. The rest of this paper organized as follows. Section two, provides an introduction the algorithm. In section three, Adaptive Imperialistic Competitive Algorithm is proposed. Fourth section is devoted to the empirical results of proposed algorithm implementation and its compression with the results obtained by, and algorithms. The last section concludes the paper. II. INTRODUCTION OF IMPERIALIST COMPETITIVE ALGORITHMS () In this section, we introduce algorithm and chaos theory. Proc. 9th IEEE Int. Conf. on Cognitive Informatics (ICCI 10) F. Sun, Y. Wang, J. Lu, B. Zhang, W. Kinsner & L.A. Zadeh (Eds.) /10/$ IEEE
2 A. Imperialist Competitive Algorithm () Imperialist Competitive Algorithm () is a new evolutionary algorithm in the Evolutionary Computation field based on the human's socio-political evolution. The algorithm starts with an initial random population called countries. Some of the best countries in the population selected to be the imperialists and the rest form the colonies of these imperialists. In an N dimensional optimization problem, a country is a array. This array defined as below The cost of a country is found by evaluating the cost function f at the variables. Then The algorithm starts with N initial countries and the best of them (countries with minimum cost) chosen as the imperialists. The remaining countries are colonies that each belong to an empire. The initial colonies belong to imperialists in convenience with their powers. To distribute the colonies among imperialists proportionally, the normalized cost of an imperialist is defined as follow Where, is the cost of nth imperialist and is its normalized cost. Each imperialist that has more cost value, will have less normalized cost value. Having the normalized cost, the power of each imperialist is calculated as below and based on that the colonies distributed among the imperialist countries. On the other hand, the normalized power of an imperialist is assessed by its colonies. Then, the initial number of colonies of an empire will be Where, is initial number of colonies of nth empire and is the number of all colonies. To distribute the colonies among imperialist, of the colonies is selected randomly and assigned to their imperialist. The imperialist countries absorb the colonies towards themselves using the absorption policy. The absorption policy shown in Fig.1, makes the main core of this algorithm and causes the countries move towards to their minimum optima. The imperialists absorb these colonies towards themselves with respect to their power that described in (6). The total power of each imperialist is determined by the power of its both parts, the empire power plus percents of its average colonies power. Where is the total cost of the nth empire and is a positive number which is considered to be less than one. In the absorption policy, the colony moves towards the imperialist by x unit. The direction of movement is the vector from colony to imperialist, as shown in Fig.1, in this figure, the distance between the imperialist and colony shown by d and x is a random variable with uniform distribution. Where is greater than 1 and is near to 2. So, a proper choice can be. In our implementation is respectively. (8) In algorithm, to search different points around the imperialist, a random amount of deviation is added to the direction of colony movement towards the imperialist. In Fig. 1, this deflection angle is shown as, which is chosen randomly and with an uniform distribution. While moving toward the imperialist countries, a colony may reach to a better position, so the colony position changes according to position of the imperialist. Figure1. Moving colonies toward their imperialist In this algorithm, the imperialistic competition has an important role. During the imperialistic competition, the weak empires will lose their power and their colonies. To model this competition, firstly we calculate the probability of possessing all the colonies by each empire considering the total cost of empire. Where, is the total cost of nth empire and is the normalized total cost of nth empire. Having the normalized total cost, the possession probability of each empire is calculated as below
3 after a while all the empires except the most powerful one will collapse and all the colonies will be under the control of this unique empire. III. THE PROPOSED IMPERIALIST COMPETITIVE ALGORITHM The algorithm like many Evolutionary Algorithms suffers the lack of ability to global search properly in the problem space. During the search process, the algorithm may trap into local optima and it is possible to get far from the global optima. This causes the premature convergence. In this paper, a new method suggested that balance the exploration and exploitation abilities of the proposed algorithm, using colonies positions information. In the algorithm absorption policy that mentioned in the previous section, the colonies move towards imperialists with an angle, which is a random variable. The colonies movement because of the constant parameter has a monotonic nature, so the colonies movement could not be adapted with the search process. Therefore, if the algorithm traps in the local optima, it cannot leave it and move towards the global optima. For solving this problem, and make balance between the explorative and exploitative search, we define the parameter adaptively, and dynamically adjust the movement of colonies to the imperialists during the search process. A. the definition of adaptive movement angle in the absorption policy As mentioned before in algorithm the colonies move towards the imperialist by a random amount of deviation. The parameter is this deviation. In this paper, we extract the statistical information about the search space from the current population of solutions to provide an adaptive movement angle. We proposed a probabilistic model, to modify the global search capability. The probabilistic model P(x) that we use here is a Gaussian distribution model [24,25,26,27]. The joint probability distribution of all the countries, is given by the product of the marginal probabilities of the countries: Where The average, μ, and the standard deviation,, for the colony countries of each empire is approximated as below: In each iteration, the country densities compute using the probabilistic model in Eq(11). If the countries density in the current iteration is more than the previous iteration, then with 85% the previous angle of the movement of the countries towards their empires will be shrunk and with 15% the mentioned angle will be expanded., is the current angle of movement., is the previous angle and is the step size of shrinking and expanding the angle of movement. The value of this step size is varying between and 0.1. Otherwise, if the countries density in the current iteration is less than the previous iteration, then with 85% the previous angle of the movement of the countries towards their empires will be expanded and with 15% the mentioned angle will be shrunk. If the countries density in the current iteration is more than the previous iteration, it means that may be the countries are converging to an optimum point. So, in Eq. (15), depending on the density of the countries distribution, we set the angle of movement so that each country can escape from the dense area with 15% and with 85% the country will move towards its empire with a shrinking angle. In the cases that the countries converge to a local optima, this method will help to escape from falling into the local optima s trap with possibility of 15%. In this way, we add explorative search ability to the algorithm. In Eq. (16), if the countries density in the current iteration is less than the previous iteration, each country with possibility of 15% will move towards its empire with a shrinking angle and with 85% the country will move towards its empire with an expanding angle. This way, provides a more efficient search in all over the search space of the problem. The results show that the quality of solutions and the speed of convergence of imperialist competitive algorithm with adaptive absorption policy is better than to, using a Sugeno function as inertia weight and algorithms. This is observable in analysis and conclusion section. (1) Initialize the empires and their colonies positions randomly. (2) Compute the adaptive (colonies movement angle towards the imperialist s position) using the probabilistic model. (3) Compute the total cost of all empires (Related to the power of both the imperialist and its colonies). (4) Pick the weakest colony (colonies) from the weakest empire and give it (them) to the empire that has the most likelihood to possess it (Imperialistic competition). (5) Eliminate the powerless empires. (6) If there is just one empire, then stop else continue. (7) Check the termination conditions. Figure2. The algorithm.
4 IV. ANALYSIS AND CONSIDERATION OF EMPIRL RESULTS In this paper, the proposed algorithm, that called Adaptive Imperialist Competitive Algorithm (), applied to some well-known benchmark functions and a three-layered Perceptron Neural Network to update its weights, in order to verify the algorithm performance and compared with and using a Sugeno function as inertia weight and algorithms. These benchmarks presented in Table1. Sphere Rosenbrock Rastrigin Griewank Ackley TABLE I. BENCHMARKS FOR SIMULATION Mathematical representation (x)=-20exp(-0.2 exp( e Range (-100,100) (-100,100) (-10,10) (-600,600) (-32,32) testing. The neural network trained by C,, and algorithms and the results compared with each other. The results of these experiments presented in Table 2 and 3. In the Fig.3, which belongs to Sphere it is observable that the quality of global optima solution and the convergence velocity towards the optima point has improved in compare with the other three algorithms. In the log plot of the Sphere function, at the first 20 iterations, algorithm has better convergence speed than the and algorithms but after that iteration the won the competition comparative Result for Sphere michalewicz (x)= - (0,) 10-6 We made simulations for considering the rate of convergence and the quality of the proposed algorithm optima solution, in comparison to, using a Sugeno function as inertia weight and algorithms that all the benchmarks tested by 30 dimensions separately. The average of optimum value for 20 trails obtained. In these experiments, all the simulations done during 1000 generations for Sphere and Rosenbrock uni-modal functions and Rastrigin, Griwank, Ackley and michalewicz multimodal functions. In these simulations for and algorithms, we set the parameters,= The number of imperialists and the colonies are set respectively to 8 and 80. In algorithm the parameters and are fix to 1.5 and the number of the particle is 80. Determining this amount for c1 and c2 we have given equal chance to social and cognition components take part in search process. In the population size is 80, the mutation and crossover rate are respectively set to 0.01 and 0.5. We applied the trained neural network with,, and algorithms on the data of TEHRAN's bourse market. The inputs of this network are the volume of changed stocks, the last price, the least price and the most prices in different times. The output of this network is the approximation of the most prices of the changed stocks in TEHRAN's bourse market. In these simulations, we used a three-layered Perseptron Neural Network containing an input layer with 7 nodes, a hidden layer with 5 nodes and an output layer with one node. The dataset include of 1155 instances. Using Holdout method (The holdout method splits the data into two mutually exclusive sets, sometimes referred to as the training and test sets) we apply 80% of instance data for training the Neural Network and the remaining 20 % for 10-8 Figure3. The cost of Sphere function In Rosenbrock uni-modal function the speed of convergence of algorithm is better than, and algorithm until the 200th iteration. After the 200th iteration, the velocity and quality of optima solution recovered in algorithm comparative Result for Rosenbrock Figure4. The cost of Rosenbrock function. As we can see in Fig.5, for Rasrigin multi-modal function the algorithm has better performance rather than the and algorithms. The proposed algorithm has shown a good performance in this function and has been able to escape from the local peaks and reach to global optima.
5 10 3 comparative Result for Rastrigin comparative Result for Ackley Cost 10 0 Cost Figure 5. The cost of Rastrigin function. In Fig.6, Michalewicz multi-modal function, the porposed algorithm has shown good performance comparative Result for Michalewicz -22 Figure 6. The cost of Michalewicz function. In Fig.7, Griewank multi-modal function the proposed algorithm has had remarkable improved in this function both in optima solution quality and in convergence speed rather than the, and algorithms comparative Result for Griewank 0 Figure8. The cost of Ackley function. In Fig.8, Ackley multi-modal function, the proposed algorithm has better performance in this function both in optima solution quality and in convergence speed rather than the, and algorithms reach to a better optima. In table 2, the average of optimum value for 20 trails, which is obtained from proposed algorithm,, and are shown. The benchmarks, were tested by 30 dimensions and the stop condition was 1000 generations. The numerical results show that the proposed algorithm has recovered the global optima solution remarkably. TABEL II. Average optimum value for 20 trails for benchmarks. Sphere Rosenbrock Rastrigin Michalewicz Griewank Ackley In Fig9, comparison of Mean Square Error (MSE) of Neural Network trained by,, and indicated that the proposed algorithm trained very well rather than the other algorithms Mean Square Error MSE Figure7. The cost of Griewank function Epoch # Figure 9. The comparison of Mean Square Errors (MSE). Table 2, shows the result of,, and training algorithms mean square errors. As it is observable, the
6 algorithm has the least MSE in compare with the other algorithms. Train Error Test Error TABLE II. COMPARE RESULTS Train correlation Test correlation Time of training (second) V. CONCLUSION In this paper, an improved imperialist algorithm called Adaptive Imperialist Competitive Algorithm () introduced. The proposed algorithm uses the probability density function to adapt the angle of colonies movement towards imperialist s position during iterations dynamically. This mechanism, enhance the global search capability of the algorithm. This idea balances the exploration and exploitation abilities of the proposed algorithm, using colonies positions information. We examined the proposed algorithm in several standard benchmark functions that usually tested in Evolutionary Algorithms. Also, we use the Algorithm to adjust the weights of a three-layered Perceptron Neural Network to predict the maximum worth of the stocks change in Tehran s Bourse Market. Experimental results show that the proposed algorithm is a promising method with good global convergence performance than the, and algorithms. In the future, we will work on the affect of the different probability models on the performance of the proposed algorithm. REFERENCES [1]H. Sarimveis and A. Nikolakopoulos, "A Line Up Evolutionary Algorithm for Solving Nonlinear Constrained Optimization Problems", Computers & Operations Research, 32(6):pp , [2]H. M uhlenbein, M. Schomisch, J.Born, "The Parallel Genetic Algorithm as Function Optimizer", Proceedings of The Fourth International Conference on Genetic Algorithms, University of California, San diego, pp ,1991. [3]C. Bing-rui and F. Xia-ting, "Self-adapting Chaos-genetic Hybrid Algorithm with Mixed congruential Method", Forth International Conference, pp ,2008. [4]J.H. Holland. "ECHO: Explorations of Evolution in a Miniature World", In J.D. Farmer and J. Doyne, editors, Proceedings of the Second Conference on Artificial Life, [5] M. Gao, J. Xu, J. Tian and H. Wu, "Path Planning for Mobile Robot based on Chaos Genetic Algorithm", Forth International Conference, pp ,2008. [6]M. Melanie, "An Introduction to Genetic Algorithms", Massachusett"s: MIT Press, [7]May RM, "Simple mathematical models with very complicated dynamics,. Nature 1976;261:459. [8]J. Kennedy and R.C. Eberhart, "Particle swarm optimization", in: Proceedings of IEEE International Conference on Neural Networks, Piscataway: IEEE, pp , [9]X. Yang, J. Yuan, J. Yuan and H. Mao," A modified particle swarm optimizer with dynamic adaptation", Applied Mathematics and Computation, Volume 189, Issue 2, pp , [10]B.E. Rosen and J.M. Goodwin, "Optimizing Neural Networks Using Very Fast Simulated Annealing. Neural", Parallel & Scientific Computations, pp , [11]L.A. Ingber, "Simulated annealing: practice versus theory", J. Math. Comput. Modell. 18 (11), pp.29 57, [12]M.F. Cardoso, R.L. Salcedo, S.F. Azevedo, D. Barbosa, "A simulated annealing approach to the solution of minlp problems", Comput. Chem. Eng. 21 (12),pp , [13]B. Franklin and M. Bergerman, "Cultural Algorithms: Concepts and Experiments", In Proceedings of the IEEE Congress on Evolutionary Computation, volume 2, pp , [14] X. Jin and R.G. Reynolds, "Using Knowledge-Based Evolutionary Computation to Solve Nonlinear Constraint Optimization Problems: A Cultural Algorithm Approach", In Proceedings of the IEEE Congress on Evolutionary Computation, volume 3, pp , [15]B.E. Rosen and J.M. Goodwin, "Optimizing Neural Networks Using Very Fast Simulated Annealing", Neural, Parallel & Scientific Computations, pp , [16]C.L. Wu, K.W. Chau, "A flood forecasting neural network model with genetic algorithm", International Journal of Environment and Pollution 28(3 4) pp ,(2006). [17]N. Muttil, K.W. Chau, "Neural network and genetic programming for modelling coastal algal blooms", International Journal of Environment and Pollution 28 (3 4) pp , [18] J. Kennedy and R.C. Eberhart, "Particle swarm optimization", in: Proceedings of IEEE International Conference on Neural Networks, Piscataway: IEEE, pp , [19]K. Lei, Y. Qiu and Y. He, "A New Adaptive Well-Chosen Inertia Weight Strategy to Automatically Harmonize Global and Local Search Ability in Particle Swarm Optimization", ISScAA, [20]Y. Da, X.R. Ge, "An improved -based ANN with simulated annealing technique", Neurocomput. Lett. 63 pp , [21]E. Atashpaz-Gargari and C. Lucas, "Imperialist Competitive Algorithm: An Algorithm for Optimization Inspired by Imperialistic Competition", IEEE Congress on Evolutionary Computation (CEC 2007).pp , [22] The dataset for training the Neural Network. [23]S. Kirtrick and C. D. Gelatt and M. P. Vecchi, Optimization by Simulated Annealing, Science, Vol 220, Number 4598, pp , [24]A. Papoulis, Probability, Random Variables and Stochastic Processes, McGraw-Hill, [25]Randall C. Smith, Peter Cheeseman, On the Representation and Estimation of Spatial Uncertainty, the International Journal of Robotics Research,Vol.5, No.4, Winter [26]T.K. Paul and H. Iba, Linear and Combinatorial Optimizations by Estimation of Distribution Algorithms, 9th MPS Symposium on Evolutionary Computation, IPSJ, Japan, [27]Yaakov Bar-Shalom, X. Rong Li, Thiagalingam Kirubarajan, Estimation with Applications to Tracking and Navigation, John Wiley & Sons, 2001.
Imperialist Competitive Algorithm using Chaos Theory for Optimization (CICA)
2010 12th International Conference on Computer Modelling and Simulation Imperialist Competitive Algorithm using Chaos Theory for Optimization (CICA) Helena Bahrami Dept. of Elec., comp. & IT, Qazvin Azad
More informationQCA & CQCA: Quad Countries Algorithm and Chaotic Quad Countries Algorithm
Journal of Theoretical and Applied Computer Science Vol. 6, No. 3, 2012, pp. 3-20 ISSN 2299-2634 http://www.jtacs.org QCA & CQCA: Quad Countries Algorithm and Chaotic Quad Countries Algorithm M. A. Soltani-Sarvestani
More informationHybrid Particle Swarm-Based-Simulated Annealing Optimization Techniques
Hybrid Particle Swarm-Based-Simulated Annealing Optimization Techniques Nasser Sadati Abstract Particle Swarm Optimization (PSO) algorithms recently invented as intelligent optimizers with several highly
More informationParticle Swarm Optimization
Dario Schor, M.Sc., EIT schor@ieee.org Space Systems Department Magellan Aerospace Winnipeg Winnipeg, Manitoba 1 of 34 Optimization Techniques Motivation Optimization: Where, min x F(x), subject to g(x)
More informationModified Particle Swarm Optimization
Modified Particle Swarm Optimization Swati Agrawal 1, R.P. Shimpi 2 1 Aerospace Engineering Department, IIT Bombay, Mumbai, India, swati.agrawal@iitb.ac.in 2 Aerospace Engineering Department, IIT Bombay,
More informationIMPROVING THE PARTICLE SWARM OPTIMIZATION ALGORITHM USING THE SIMPLEX METHOD AT LATE STAGE
IMPROVING THE PARTICLE SWARM OPTIMIZATION ALGORITHM USING THE SIMPLEX METHOD AT LATE STAGE Fang Wang, and Yuhui Qiu Intelligent Software and Software Engineering Laboratory, Southwest-China Normal University,
More informationA Hybrid Fireworks Optimization Method with Differential Evolution Operators
A Fireworks Optimization Method with Differential Evolution Operators YuJun Zheng a,, XinLi Xu a, HaiFeng Ling b a College of Computer Science & Technology, Zhejiang University of Technology, Hangzhou,
More informationMeta- Heuristic based Optimization Algorithms: A Comparative Study of Genetic Algorithm and Particle Swarm Optimization
2017 2 nd International Electrical Engineering Conference (IEEC 2017) May. 19 th -20 th, 2017 at IEP Centre, Karachi, Pakistan Meta- Heuristic based Optimization Algorithms: A Comparative Study of Genetic
More informationA Gaussian Firefly Algorithm
A Gaussian Firefly Algorithm Sh. M. Farahani, A. A. Abshouri, B. Nasiri and M. R. Meybodi Abstract Firefly algorithm is one of the evolutionary optimization algorithms, and is inspired by fireflies behavior
More informationA Novel Hybrid Imperialist Competitive Algorithm for Global Optimization
Australian Journal of Basic and Applied Sciences, 7(8): 330-341, 2013 ISSN 1991-8178 A Novel Hybrid Imperialist Competitive Algorithm for Global Optimization Ban A. Mitras and Jalal A. Sultan 1 Operations
More informationParticle Swarm Optimization Artificial Bee Colony Chain (PSOABCC): A Hybrid Meteahuristic Algorithm
Particle Swarm Optimization Artificial Bee Colony Chain (PSOABCC): A Hybrid Meteahuristic Algorithm Oğuz Altun Department of Computer Engineering Yildiz Technical University Istanbul, Turkey oaltun@yildiz.edu.tr
More informationResearch Article Path Planning Using a Hybrid Evolutionary Algorithm Based on Tree Structure Encoding
e Scientific World Journal, Article ID 746260, 8 pages http://dx.doi.org/10.1155/2014/746260 Research Article Path Planning Using a Hybrid Evolutionary Algorithm Based on Tree Structure Encoding Ming-Yi
More informationGA is the most popular population based heuristic algorithm since it was developed by Holland in 1975 [1]. This algorithm runs faster and requires les
Chaotic Crossover Operator on Genetic Algorithm Hüseyin Demirci Computer Engineering, Sakarya University, Sakarya, 54187, Turkey Ahmet Turan Özcerit Computer Engineering, Sakarya University, Sakarya, 54187,
More informationAn improved PID neural network controller for long time delay systems using particle swarm optimization algorithm
An improved PID neural network controller for long time delay systems using particle swarm optimization algorithm A. Lari, A. Khosravi and A. Alfi Faculty of Electrical and Computer Engineering, Noushirvani
More informationBinary Differential Evolution Strategies
Binary Differential Evolution Strategies A.P. Engelbrecht, Member, IEEE G. Pampará Abstract Differential evolution has shown to be a very powerful, yet simple, population-based optimization approach. The
More informationA Novel Hybrid Self Organizing Migrating Algorithm with Mutation for Global Optimization
International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-3, Issue-6, January 2014 A Novel Hybrid Self Organizing Migrating Algorithm with Mutation for Global Optimization
More informationOptimization of Benchmark Functions Using Artificial Bee Colony (ABC) Algorithm
IOSR Journal of Engineering (IOSRJEN) e-issn: 2250-3021, p-issn: 2278-8719 Vol. 3, Issue 10 (October. 2013), V4 PP 09-14 Optimization of Benchmark Functions Using Artificial Bee Colony (ABC) Algorithm
More informationScheduling Scientific Workflows using Imperialist Competitive Algorithm
212 International Conference on Industrial and Intelligent Information (ICIII 212) IPCSIT vol.31 (212) (212) IACSIT Press, Singapore Scheduling Scientific Workflows using Imperialist Competitive Algorithm
More informationModified Particle Swarm Optimization with Novel Modulated Inertia for Velocity Update
Modified Particle Swarm Optimization with Novel Modulated Inertia for Velocity Update Abdul Hadi Hamdan #1, Fazida Hanim Hashim #2, Abdullah Zawawi Mohamed *3, W. M. Diyana W. Zaki #4, Aini Hussain #5
More informationPARTICLE SWARM OPTIMIZATION (PSO) [1] is an
Proceedings of International Joint Conference on Neural Netorks, Atlanta, Georgia, USA, June -9, 9 Netork-Structured Particle Sarm Optimizer Considering Neighborhood Relationships Haruna Matsushita and
More informationAutomatic differentiation based for particle swarm optimization steepest descent direction
International Journal of Advances in Intelligent Informatics ISSN: 2442-6571 Vol 1, No 2, July 2015, pp. 90-97 90 Automatic differentiation based for particle swarm optimization steepest descent direction
More informationArgha Roy* Dept. of CSE Netaji Subhash Engg. College West Bengal, India.
Volume 3, Issue 3, March 2013 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com Training Artificial
More informationHandling Multi Objectives of with Multi Objective Dynamic Particle Swarm Optimization
Handling Multi Objectives of with Multi Objective Dynamic Particle Swarm Optimization Richa Agnihotri #1, Dr. Shikha Agrawal #1, Dr. Rajeev Pandey #1 # Department of Computer Science Engineering, UIT,
More informationThe Design of Pole Placement With Integral Controllers for Gryphon Robot Using Three Evolutionary Algorithms
The Design of Pole Placement With Integral Controllers for Gryphon Robot Using Three Evolutionary Algorithms Somayyeh Nalan-Ahmadabad and Sehraneh Ghaemi Abstract In this paper, pole placement with integral
More informationA *69>H>N6 #DJGC6A DG C<>C::G>C<,8>:C8:H /DA 'D 2:6G, ()-"&"3 -"(' ( +-" " " % '.+ % ' -0(+$,
The structure is a very important aspect in neural network design, it is not only impossible to determine an optimal structure for a given problem, it is even impossible to prove that a given structure
More informationHybrid PSO-SA algorithm for training a Neural Network for Classification
Hybrid PSO-SA algorithm for training a Neural Network for Classification Sriram G. Sanjeevi 1, A. Naga Nikhila 2,Thaseem Khan 3 and G. Sumathi 4 1 Associate Professor, Dept. of CSE, National Institute
More informationInternational Conference on Modeling and SimulationCoimbatore, August 2007
International Conference on Modeling and SimulationCoimbatore, 27-29 August 2007 OPTIMIZATION OF FLOWSHOP SCHEDULING WITH FUZZY DUE DATES USING A HYBRID EVOLUTIONARY ALGORITHM M.S.N.Kiran Kumara, B.B.Biswalb,
More informationChaos Genetic Algorithm Instead Genetic Algorithm
The International Arab Journal of Information Technology, Vol. 12, No. 2, March 215 163 Chaos Genetic Algorithm Instead Genetic Algorithm Mohammad Javidi and Roghiyeh Hosseinpourfard Faculty of Mathematics
More informationCHAPTER 2 CONVENTIONAL AND NON-CONVENTIONAL TECHNIQUES TO SOLVE ORPD PROBLEM
20 CHAPTER 2 CONVENTIONAL AND NON-CONVENTIONAL TECHNIQUES TO SOLVE ORPD PROBLEM 2.1 CLASSIFICATION OF CONVENTIONAL TECHNIQUES Classical optimization methods can be classified into two distinct groups:
More informationGenetic-PSO Fuzzy Data Mining With Divide and Conquer Strategy
Genetic-PSO Fuzzy Data Mining With Divide and Conquer Strategy Amin Jourabloo Department of Computer Engineering, Sharif University of Technology, Tehran, Iran E-mail: jourabloo@ce.sharif.edu Abstract
More informationExperimental Study on Bound Handling Techniques for Multi-Objective Particle Swarm Optimization
Experimental Study on Bound Handling Techniques for Multi-Objective Particle Swarm Optimization adfa, p. 1, 2011. Springer-Verlag Berlin Heidelberg 2011 Devang Agarwal and Deepak Sharma Department of Mechanical
More informationTHE Artificial Neural Network (ANN) is constructed of
1 Training Neural Networks Based on Imperialist Competitive Algorithm for Predicting Earthquake Intensity Mohsen Moradi arxiv:1704.04095v1 [cs.ne] 13 Feb 2017 Abstract In this study we determined neural
More informationCloud Computing Resource Planning Based on Imperialist Competitive Algorithm
Cumhuriyet Üniversitesi Fen Fakültesi Fen Bilimleri Dergisi (CFD), Cilt:36, No: 4 Özel Sayı (205) ISSN: 300-949 Cumhuriyet University Faculty of Science Science Journal (CSJ), Vol. 36, No: 4 Special Issue
More informationA HYBRID ALGORITHM BASED ON PARTICLE SWARM OPTIMIZATION
INTERNATIONAL JOURNAL OF INFORMATION AND SYSTEMS SCIENCES Volume 1, Number 3-4, Pages 275-282 2005 Institute for Scientific Computing and Information A HYBRID ALGORITHM BASED ON PARTICLE SWARM OPTIMIZATION
More informationTraffic Signal Control Based On Fuzzy Artificial Neural Networks With Particle Swarm Optimization
Traffic Signal Control Based On Fuzzy Artificial Neural Networks With Particle Swarm Optimization J.Venkatesh 1, B.Chiranjeevulu 2 1 PG Student, Dept. of ECE, Viswanadha Institute of Technology And Management,
More informationQUANTUM BASED PSO TECHNIQUE FOR IMAGE SEGMENTATION
International Journal of Computer Engineering and Applications, Volume VIII, Issue I, Part I, October 14 QUANTUM BASED PSO TECHNIQUE FOR IMAGE SEGMENTATION Shradha Chawla 1, Vivek Panwar 2 1 Department
More informationProvide a Method of Scheduling In Computational Grid Using Imperialist Competitive Algorithm
IJCSNS International Journal of Computer Science and Network Security, VOL.16 No.6, June 2016 75 Provide a Method of Scheduling In Computational Grid Using Imperialist Competitive Algorithm Mostafa Pahlevanzadeh
More informationPARTICLE SWARM OPTIMIZATION (PSO)
PARTICLE SWARM OPTIMIZATION (PSO) J. Kennedy and R. Eberhart, Particle Swarm Optimization. Proceedings of the Fourth IEEE Int. Conference on Neural Networks, 1995. A population based optimization technique
More informationSolving the Graph Bisection Problem with Imperialist Competitive Algorithm
2 International Conference on System Engineering and Modeling (ICSEM 2) IPCSIT vol. 34 (2) (2) IACSIT Press, Singapore Solving the Graph Bisection Problem with Imperialist Competitive Algorithm Hodais
More informationReconfiguration Optimization for Loss Reduction in Distribution Networks using Hybrid PSO algorithm and Fuzzy logic
Bulletin of Environment, Pharmacology and Life Sciences Bull. Env. Pharmacol. Life Sci., Vol 4 [9] August 2015: 115-120 2015 Academy for Environment and Life Sciences, India Online ISSN 2277-1808 Journal
More informationSimulated Tornado Optimization
Simulated Tornado Optimization S. Hossein Hosseini, Tohid Nouri, Afshin Ebrahimi, and S. Ali Hosseini ICT Research Center, Faculty of Electrical Engineering, Sahand University of Technology, Tabriz, Iran
More informationPrediction of traffic flow based on the EMD and wavelet neural network Teng Feng 1,a,Xiaohong Wang 1,b,Yunlai He 1,c
2nd International Conference on Electrical, Computer Engineering and Electronics (ICECEE 215) Prediction of traffic flow based on the EMD and wavelet neural network Teng Feng 1,a,Xiaohong Wang 1,b,Yunlai
More informationA MULTI-SWARM PARTICLE SWARM OPTIMIZATION WITH LOCAL SEARCH ON MULTI-ROBOT SEARCH SYSTEM
A MULTI-SWARM PARTICLE SWARM OPTIMIZATION WITH LOCAL SEARCH ON MULTI-ROBOT SEARCH SYSTEM BAHAREH NAKISA, MOHAMMAD NAIM RASTGOO, MOHAMMAD FAIDZUL NASRUDIN, MOHD ZAKREE AHMAD NAZRI Department of Computer
More informationCooperative Coevolution using The Brain Storm Optimization Algorithm
Cooperative Coevolution using The Brain Storm Optimization Algorithm Mohammed El-Abd Electrical and Computer Engineering Department American University of Kuwait Email: melabd@auk.edu.kw Abstract The Brain
More informationSmall World Particle Swarm Optimizer for Global Optimization Problems
Small World Particle Swarm Optimizer for Global Optimization Problems Megha Vora and T.T. Mirnalinee Department of Computer Science and Engineering S.S.N College of Engineering, Anna University, Chennai,
More informationNeural Network Weight Selection Using Genetic Algorithms
Neural Network Weight Selection Using Genetic Algorithms David Montana presented by: Carl Fink, Hongyi Chen, Jack Cheng, Xinglong Li, Bruce Lin, Chongjie Zhang April 12, 2005 1 Neural Networks Neural networks
More informationAn Island Based Hybrid Evolutionary Algorithm for Optimization
An Island Based Hybrid Evolutionary Algorithm for Optimization Changhe Li and Shengxiang Yang Department of Computer Science, University of Leicester University Road, Leicester LE1 7RH, UK {cl160,s.yang}@mcs.le.ac.uk
More informationHybrid Particle Swarm and Neural Network Approach for Streamflow Forecasting
Math. Model. Nat. Phenom. Vol. 5, No. 7, 010, pp. 13-138 DOI: 10.1051/mmnp/01057 Hybrid Particle Swarm and Neural Network Approach for Streamflow Forecasting A. Sedki and D. Ouazar Department of Civil
More informationResearch on time optimal trajectory planning of 7-DOF manipulator based on genetic algorithm
Acta Technica 61, No. 4A/2016, 189 200 c 2017 Institute of Thermomechanics CAS, v.v.i. Research on time optimal trajectory planning of 7-DOF manipulator based on genetic algorithm Jianrong Bu 1, Junyan
More informationEvolutionary Algorithms For Neural Networks Binary And Real Data Classification
Evolutionary Algorithms For Neural Networks Binary And Real Data Classification Dr. Hanan A.R. Akkar, Firas R. Mahdi Abstract: Artificial neural networks are complex networks emulating the way human rational
More informationArtificial Bee Colony (ABC) Optimization Algorithm for Solving Constrained Optimization Problems
Artificial Bee Colony (ABC) Optimization Algorithm for Solving Constrained Optimization Problems Dervis Karaboga and Bahriye Basturk Erciyes University, Engineering Faculty, The Department of Computer
More informationPerformance Assessment of DMOEA-DD with CEC 2009 MOEA Competition Test Instances
Performance Assessment of DMOEA-DD with CEC 2009 MOEA Competition Test Instances Minzhong Liu, Xiufen Zou, Yu Chen, Zhijian Wu Abstract In this paper, the DMOEA-DD, which is an improvement of DMOEA[1,
More informationDE/EDA: A New Evolutionary Algorithm for Global Optimization 1
DE/EDA: A New Evolutionary Algorithm for Global Optimization 1 Jianyong Sun, Qingfu Zhang and Edward P.K. Tsang Department of Computer Science, University of Essex, Wivenhoe Park, Colchester, CO4 3SQ,
More informationLecture 4. Convexity Robust cost functions Optimizing non-convex functions. 3B1B Optimization Michaelmas 2017 A. Zisserman
Lecture 4 3B1B Optimization Michaelmas 2017 A. Zisserman Convexity Robust cost functions Optimizing non-convex functions grid search branch and bound simulated annealing evolutionary optimization The Optimization
More informationWhat Makes A Successful Society?
What Makes A Successful Society? Experiments With Population Topologies in Particle Swarms Rui Mendes and José Neves Departamento de Informática Universidade do Minho Portugal Abstract. Previous studies
More informationExperiments with Firefly Algorithm
Experiments with Firefly Algorithm Rogério B. Francisco 1,2, M. Fernanda P. Costa 2, Ana Maria A. C. Rocha 3 1 Escola Superior de Tecnologia e Gestão de Felgueiras, 4610-156 Felgueiras, Portugal rbf@estgf.ipp.pt
More informationACONM: A hybrid of Ant Colony Optimization and Nelder-Mead Simplex Search
ACONM: A hybrid of Ant Colony Optimization and Nelder-Mead Simplex Search N. Arun & V.Ravi* Assistant Professor Institute for Development and Research in Banking Technology (IDRBT), Castle Hills Road #1,
More informationUsing Genetic Algorithms to optimize ACS-TSP
Using Genetic Algorithms to optimize ACS-TSP Marcin L. Pilat and Tony White School of Computer Science, Carleton University, 1125 Colonel By Drive, Ottawa, ON, K1S 5B6, Canada {mpilat,arpwhite}@scs.carleton.ca
More informationComparative Study of Meta-heuristics Optimization Algorithm using Benchmark Function
International Journal of Electrical and Computer Engineering (IJECE) Vol. 7, No. 3, June 2017, pp. 1643~1650 ISSN: 2088-8708, DOI: 10.11591/ijece.v7i3.pp1643-1650 1643 Comparative Study of Meta-heuristics
More informationA Comparative Analysis on the Performance of Particle Swarm Optimization and Artificial Immune Systems for Mathematical Test Functions.
Australian Journal of Basic and Applied Sciences 3(4): 4344-4350 2009 ISSN 1991-8178 A Comparative Analysis on the Performance of Particle Swarm Optimization and Artificial Immune Systems for Mathematical
More informationA New Approach for Finding the Global Optimal Point Using Subdividing Labeling Method (SLM)
A New Approach for Finding the Global Optimal Point Using Subdividing Labeling Method (SLM) MasoumehVali Department of Mathematics, Dolatabad Branch, Islamic Azad University, Isfahan, Iran E-mail: vali.masoumeh@gmail.com
More informationEnhanced Symbiotic Organisms Search (ESOS) for Global Numerical Optimization Doddy Prayogo Dept. of Civil Engineering Petra Christian University Surab
Enhanced Symbiotic Organisms Search (ESOS) for Global Numerical Optimization Doddy Prayogo Dept. of Civil Engineering Petra Christian University Surabaya, Indonesia prayogo@petra.ac.id Foek Tjong Wong
More informationCHAPTER 6 ORTHOGONAL PARTICLE SWARM OPTIMIZATION
131 CHAPTER 6 ORTHOGONAL PARTICLE SWARM OPTIMIZATION 6.1 INTRODUCTION The Orthogonal arrays are helpful in guiding the heuristic algorithms to obtain a good solution when applied to NP-hard problems. This
More informationImproving Tree-Based Classification Rules Using a Particle Swarm Optimization
Improving Tree-Based Classification Rules Using a Particle Swarm Optimization Chi-Hyuck Jun *, Yun-Ju Cho, and Hyeseon Lee Department of Industrial and Management Engineering Pohang University of Science
More informationHPSOM: A HYBRID PARTICLE SWARM OPTIMIZATION ALGORITHM WITH GENETIC MUTATION. Received February 2012; revised June 2012
International Journal of Innovative Computing, Information and Control ICIC International c 2013 ISSN 1349-4198 Volume 9, Number 5, May 2013 pp. 1919 1934 HPSOM: A HYBRID PARTICLE SWARM OPTIMIZATION ALGORITHM
More informationOPTIMIZATION OF OBJECT TRACKING BASED ON ENHANCED IMPERIALIST COMPETITIVE ALGORITHM
OPTIMIZATION OF OBJECT TRACKING BASED ON ENHANCED IMPERIALIST COMPETITIVE ALGORITHM 1 Luhutyit Peter Damuut and 1 Jakada Dogara Full Length Research Article 1 Department of Mathematical Sciences, Kaduna
More informationParticle Swarm Optimization
Particle Swarm Optimization Gonçalo Pereira INESC-ID and Instituto Superior Técnico Porto Salvo, Portugal gpereira@gaips.inesc-id.pt April 15, 2011 1 What is it? Particle Swarm Optimization is an algorithm
More informationNon-deterministic Search techniques. Emma Hart
Non-deterministic Search techniques Emma Hart Why do local search? Many real problems are too hard to solve with exact (deterministic) techniques Modern, non-deterministic techniques offer ways of getting
More informationGENETIC ALGORITHM VERSUS PARTICLE SWARM OPTIMIZATION IN N-QUEEN PROBLEM
Journal of Al-Nahrain University Vol.10(2), December, 2007, pp.172-177 Science GENETIC ALGORITHM VERSUS PARTICLE SWARM OPTIMIZATION IN N-QUEEN PROBLEM * Azhar W. Hammad, ** Dr. Ban N. Thannoon Al-Nahrain
More informationDERIVATIVE-FREE OPTIMIZATION
DERIVATIVE-FREE OPTIMIZATION Main bibliography J.-S. Jang, C.-T. Sun and E. Mizutani. Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence. Prentice Hall, New Jersey,
More informationUnidimensional Search for solving continuous high-dimensional optimization problems
2009 Ninth International Conference on Intelligent Systems Design and Applications Unidimensional Search for solving continuous high-dimensional optimization problems Vincent Gardeux, Rachid Chelouah,
More informationA Particle Swarm Optimization Algorithm for Solving Flexible Job-Shop Scheduling Problem
2011, TextRoad Publication ISSN 2090-4304 Journal of Basic and Applied Scientific Research www.textroad.com A Particle Swarm Optimization Algorithm for Solving Flexible Job-Shop Scheduling Problem Mohammad
More informationISSN: [Keswani* et al., 7(1): January, 2018] Impact Factor: 4.116
IJESRT INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY AUTOMATIC TEST CASE GENERATION FOR PERFORMANCE ENHANCEMENT OF SOFTWARE THROUGH GENETIC ALGORITHM AND RANDOM TESTING Bright Keswani,
More informationGRAPH COLOURING PROBLEM BASED ON DISCRETE IMPERIALIST COMPETITIVE ALGORITHM
GRAPH COLOURING PROBLEM BASED ON DISCRETE IMPERIALIST COMPETITIVE ALGORITHM Hojjat Emami 1 and Shahriar Lotfi 2 1 Department of Computer Engineering, Islamic Azad University, Miyandoab Branch, Miyandoab,
More informationConstraints in Particle Swarm Optimization of Hidden Markov Models
Constraints in Particle Swarm Optimization of Hidden Markov Models Martin Macaš, Daniel Novák, and Lenka Lhotská Czech Technical University, Faculty of Electrical Engineering, Dep. of Cybernetics, Prague,
More informationMulti-Objective Optimization Using Genetic Algorithms
Multi-Objective Optimization Using Genetic Algorithms Mikhail Gaerlan Computational Physics PH 4433 December 8, 2015 1 Optimization Optimization is a general term for a type of numerical problem that involves
More informationThe Modified IWO Algorithm for Optimization of Numerical Functions
The Modified IWO Algorithm for Optimization of Numerical Functions Daniel Kostrzewa and Henryk Josiński Silesian University of Technology, Akademicka 16 PL-44-100 Gliwice, Poland {Daniel.Kostrzewa,Henryk.Josinski}@polsl.pl
More informationAN EFFICIENT COST FUNCTION FOR IMPERIALIST COMPETITIVE ALGORITHM TO FIND BEST CLUSTERS
AN EFFICIENT COST FUNCTION FOR IMPERIALIST COMPETITIVE ALGORITHM TO FIND BEST CLUSTERS 1 MOJGAN GHANAVATI, 2 MOHAMAD REZA GHOLAMIAN, 3 BEHROUZ MINAEI, 4 MEHRAN DAVOUDI 2 Professor, Iran University of Science
More informationEffect of the PSO Topologies on the Performance of the PSO-ELM
2012 Brazilian Symposium on Neural Networks Effect of the PSO Topologies on the Performance of the PSO-ELM Elliackin M. N. Figueiredo and Teresa B. Ludermir Center of Informatics Federal University of
More informationOffspring Generation Method using Delaunay Triangulation for Real-Coded Genetic Algorithms
Offspring Generation Method using Delaunay Triangulation for Real-Coded Genetic Algorithms Hisashi Shimosaka 1, Tomoyuki Hiroyasu 2, and Mitsunori Miki 2 1 Graduate School of Engineering, Doshisha University,
More informationProviding new meta-heuristic algorithm for optimization problems inspired by humans behavior to improve their positions
Providing new meta-heuristic algorithm for optimization problems inspired by humans behavior to improve their positions Azar,Adel 1 ; Seyedmirzaee, Seyedmoslem* 2 1- Professor of management, Tarbiatmodares
More informationEE 553 Term Project Report Particle Swarm Optimization (PSO) and PSO with Cross-over
EE Term Project Report Particle Swarm Optimization (PSO) and PSO with Cross-over Emre Uğur February, 00 Abstract In this work, Particle Swarm Optimization (PSO) method is implemented and applied to various
More informationAN NOVEL NEURAL NETWORK TRAINING BASED ON HYBRID DE AND BP
AN NOVEL NEURAL NETWORK TRAINING BASED ON HYBRID DE AND BP Xiaohui Yuan ', Yanbin Yuan 2, Cheng Wang ^ / Huazhong University of Science & Technology, 430074 Wuhan, China 2 Wuhan University of Technology,
More informationIMPROVED ARTIFICIAL FISH SWARM ALGORITHM AND ITS APPLICATION IN OPTIMAL DESIGN OF TRUSS STRUCTURE
IMPROVED ARTIFICIAL FISH SWARM ALGORITHM AD ITS APPLICATIO I OPTIMAL DESIG OF TRUSS STRUCTURE ACAG LI, CHEGUAG BA, SHUJIG ZHOU, SHUAGHOG PEG, XIAOHA ZHAG College of Civil Engineering, Hebei University
More informationARMA MODEL SELECTION USING PARTICLE SWARM OPTIMIZATION AND AIC CRITERIA. Mark S. Voss a b. and Xin Feng.
Copyright 2002 IFAC 5th Triennial World Congress, Barcelona, Spain ARMA MODEL SELECTION USING PARTICLE SWARM OPTIMIZATION AND AIC CRITERIA Mark S. Voss a b and Xin Feng a Department of Civil and Environmental
More informationIEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, VOL. 5, NO. 1, FEBRUARY
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, VOL. 5, NO. 1, FEBRUARY 2001 41 Brief Papers An Orthogonal Genetic Algorithm with Quantization for Global Numerical Optimization Yiu-Wing Leung, Senior Member,
More informationModified Shuffled Frog-leaping Algorithm with Dimension by Dimension Improvement
35 JOURNAL OF COMPUTERS, VOL. 9, NO. 1, OCTOBER 14 Modified Shuffled Frog-leaping Algorithm with Dimension by Dimension Improvement Juan Lin College of Computer and Information Science, Fujian Agriculture
More informationAn Improved Tree Seed Algorithm for Optimization Problems
International Journal of Machine Learning and Computing, Vol. 8, o. 1, February 2018 An Improved Tree Seed Algorithm for Optimization Problems Murat Aslan, Mehmet Beskirli, Halife Kodaz, and Mustafa Servet
More informationFeature Selection using Modified Imperialist Competitive Algorithm
Feature Selection using Modified Imperialist Competitive Algorithm S. J. Mousavirad Department of Computer and Electrical Engineering University of Kashan Kashan, Iran jalalmoosavirad@gmail.com Abstract
More informationOpen Access Research on the Prediction Model of Material Cost Based on Data Mining
Send Orders for Reprints to reprints@benthamscience.ae 1062 The Open Mechanical Engineering Journal, 2015, 9, 1062-1066 Open Access Research on the Prediction Model of Material Cost Based on Data Mining
More informationA Naïve Soft Computing based Approach for Gene Expression Data Analysis
Available online at www.sciencedirect.com Procedia Engineering 38 (2012 ) 2124 2128 International Conference on Modeling Optimization and Computing (ICMOC-2012) A Naïve Soft Computing based Approach for
More informationBenchmark Functions for the CEC 2008 Special Session and Competition on Large Scale Global Optimization
Benchmark Functions for the CEC 2008 Special Session and Competition on Large Scale Global Optimization K. Tang 1, X. Yao 1, 2, P. N. Suganthan 3, C. MacNish 4, Y. P. Chen 5, C. M. Chen 5, Z. Yang 1 1
More information[Kaur, 5(8): August 2018] ISSN DOI /zenodo Impact Factor
GLOBAL JOURNAL OF ENGINEERING SCIENCE AND RESEARCHES EVOLUTIONARY METAHEURISTIC ALGORITHMS FOR FEATURE SELECTION: A SURVEY Sandeep Kaur *1 & Vinay Chopra 2 *1 Research Scholar, Computer Science and Engineering,
More informationWater cycle algorithm with fuzzy logic for dynamic adaptation of parameters
Water cycle algorithm with fuzzy logic for dynamic adaptation of parameters Eduardo Méndez 1, Oscar Castillo 1 *, José Soria 1, Patricia Melin 1 and Ali Sadollah 2 Tijuana Institute of Technology, Calzada
More informationInternational Journal of Digital Application & Contemporary research Website: (Volume 1, Issue 7, February 2013)
Performance Analysis of GA and PSO over Economic Load Dispatch Problem Sakshi Rajpoot sakshirajpoot1988@gmail.com Dr. Sandeep Bhongade sandeepbhongade@rediffmail.com Abstract Economic Load dispatch problem
More informationReal Coded Genetic Algorithm Particle Filter for Improved Performance
Proceedings of 2012 4th International Conference on Machine Learning and Computing IPCSIT vol. 25 (2012) (2012) IACSIT Press, Singapore Real Coded Genetic Algorithm Particle Filter for Improved Performance
More informationCT79 SOFT COMPUTING ALCCS-FEB 2014
Q.1 a. Define Union, Intersection and complement operations of Fuzzy sets. For fuzzy sets A and B Figure Fuzzy sets A & B The union of two fuzzy sets A and B is a fuzzy set C, written as C=AUB or C=A OR
More informationTracking Changing Extrema with Particle Swarm Optimizer
Tracking Changing Extrema with Particle Swarm Optimizer Anthony Carlisle Department of Mathematical and Computer Sciences, Huntingdon College antho@huntingdon.edu Abstract The modification of the Particle
More informationAn Evolutionary Algorithm for Minimizing Multimodal Functions
An Evolutionary Algorithm for Minimizing Multimodal Functions D.G. Sotiropoulos, V.P. Plagianakos and M.N. Vrahatis University of Patras, Department of Mamatics, Division of Computational Mamatics & Informatics,
More informationThe movement of the dimmer firefly i towards the brighter firefly j in terms of the dimmer one s updated location is determined by the following equat
An Improved Firefly Algorithm for Optimization Problems Amarita Ritthipakdee 1, Arit Thammano, Nol Premasathian 3, and Bunyarit Uyyanonvara 4 Abstract Optimization problem is one of the most difficult
More information