Surrogate-assisted Self-accelerated Particle Swarm Optimization
|
|
- Gertrude Norman
- 6 years ago
- Views:
Transcription
1 Surrogate-assisted Self-accelerated Particle Swarm Optimization Kambiz Haji Hajikolaei 1, Amir Safari, G. Gary Wang ±, Hirpa G. Lemu, ± School of Mechatronic Systems Engineering, Simon Fraser University, V3T0A3 Vancouver, BC Canada, Mechanical and Structural Engineering Department, University of Stavanger, 4036 Stavanger, Norway Surrogate-assisted self-accelerated particle swarm optimization () is a major modification of an original PSO which uses all previously evaluated particles aiming to increase the computational efficiency. A newly in-house developed metamodeling approach named high dimensional model representation with principal component analysis (PCA- HDMR), which was specifically established for so called high-dimensional, expensive, blackbox (HEB) problems, is used to approximate a function using all particles calculated during the optimization process. Then, based on the minimum of the constructed metamodel, a term called metamodeling acceleration is added to the velocity update formula in the original PSO algorithm. The proposed optimization algorithm performance is investigated using several benchmark problems with different number of variables and the results are also compared with original PSO results. Preliminary results show a considerable performance improvement in terms of number of function evaluations as well as achieved global optimum specifically for high-dimensional problems. Nomenclature = Surrogate-assisted self-accelerated particle swarm optimization RS-HDMR = Random sampling high dimensional model representation PCA-HDMR = High dimensional model representation with principal component analysis MDO = Multi-disciplinary design optimization HEB = High-dimensional, expensive, black-box D = Dimension m = number of used basis functions N = number of sample points I. Introduction omputational performance of optimization methods and strategies plays a major role in design optimization of C engineering problems with multidisciplinary nature, i.e., multidisciplinary design optimization (MDO). MDO is a field of engineering optimization that simultaneously addresses a number of disciplines to solve real design problems. In this area, however, a majority of problems involve so called high-dimensional, expensive, black-box (HEB) functions, which typically need complex finite element analyses (FEA) and/or computational fluid dynamics (CFD) for analyses/simulations. To find the optimal values of the design variables for such problems, searching through a considerably high-dimensional design space is required, resulting in difficulties called curse of dimensionality. On the other hand, the high dimensionality of design variables presents an exponential difficulty for both problem modeling and optimization 1. Research Assistant, Product Design & Optimization Laboratory, School of Mechatronic Systems Engineering, Simon Fraser University, V3T0A3 Vancouver, BC Canada PhD Candidate, Mechanical Design & Simulation Research Group, Mechanical and Structural Engineering Department, University of Stavanger, 4036 Stavanger, Norway ± Professor, School of Mechatronic Systems Engineering, Simon Fraser University, V3T0A3 Vancouver, BC Canada Associate Professor, Mechanical and Structural Engineering Department, University of Stavanger, 4036 Stavanger, Norway 1
2 At the same time, there are a variety of approaches attempt to tackle the mentioned kind of challenge dealing with optimization of high-dimensional problems from both modeling and optimization points of view. Some techniques are single level (hierarchical) methods, and other techniques are considered as multi-level (non-hierarchical) approaches 2. Among all these methods, random sampling high dimensional model representation (RS-HDMR) is one of the most commanding and efficient methods has been developed to build an HDMR model from random sample points with a linear combination of specified basis functions 3. In terms of optimization strategies, nature-inspired algorithms such as genetic algorithm (GA), particle swarm optimization (PSO), and ant colony optimization (ACO) have recently demonstrated their success as well as popularity in practice. Nevertheless, they cannot fully satisfy the design optimization need for the complex, high dimensional and expensive problems so that some minor/major modifications are demanded depending on the nature of difficulty. Among them, PSO is becoming a hotspot as a population-based technique to meet requirements on both computational efficiency and self-improvement capability. Hence, many researchers have strived to modify its feature by using various development strategies 4. In recent times, a large number of studies have paid attention to use metamodels for particle evaluation during PSO process in different applications 5-9. In this way, they have constructed surrogate-based approximations and utilized them in conjuction with PSO in an inexact pre-evaluation procedure so that by using this mixed evaluations (metamodels/high-fidelity functions) computational cost can be meaningfully reduced. This study, however, introduces a novel approach of surrogate-assisted PSO algorithms to internally develop its performance and to automatically make itself more appropriate specially for HEB problems. Application of high dimensional model representation with principal component analysis (PCA-HDMR) as a means of metamodeling of formerly evaluated particles during a PSO procedure is addressed in this work. On the one hand, there are totally random particles evaluated in initial population as well as different iterations of PSO so that applying RS-HDMR concept is appropriate. Because of the limited number of particles and non-controllable sampling (which would make it non-uniform), on the other hand, using a modified approach in approximating HEB problems named PCA- HDMR seems to be more feasible. The approach uses principal component analysis to help identifying the coefficients of basis functions in a way that minimizes the variation from the underlying (black-box) function 10. The metamodel constructed from unemployed evaluated particles is finally used to generate an extra term for the existing velocity update formula at each iteration. With application of this new framework, PSO accelerates and reaches to the global minimum by using significantly fewer number of function evaluations. The proposed method is applied to five benchmark functions and its good performance can be seen when compared with the original PSO. II. Overview of PCA-HDMR High Dimensional Model Representation (HDMR) is an approach suitable for high dimensional metamodeling problems, first inotroduced by Sobol 12 with the general form of:,,,, (1),,, where is a constant representing the zero-th order effecr of. and, represent first-order effect of variable and second-order effect variables,, respectively. In general, describes the l-th order effect of variables,,,. There are different types of HDMR such as ANOVA-HDMR 13, Cut-HDMR 13, RBF-HDMR 14, RS-HDMR 3, and so on, and there are pros and cons in using them. For this paper, the recently developed type, PCA-HDMR, is selected because it works with random sampling, even if the sampling is non-uniform. This property is vital in because of non-uniformity of sampling during the optimization iterations. PCA-HDMR has the general form of:, (2) 2
3 where and, are families of linearly independent bases for uni-variable and bi-variable functions on the 0,1 and 0,1, repectively. The form is common between RS-HDMR and PCA-HDMR and the difference is in the way of calculating the coefficients. PCA-HDMR matrix is a 1 matrix shown in Eq. 3. The basis functions,, are named for simplicity. The basis functions are calculated in all sample points and are subtracted by their average values over the sample points to construct the PCA-HDMR matrix. The last column of the matrix is related to the original black-box function value in the sample points. They are rescaled to be in 0,1 () and subtracted by their average values. 1 1, 2 2,,, (3) After applying singular value decomposition (SVD), the last linear transformation that corresponds to the minimum variation is selected. Considering that,,, as the vector of coefficients related to the transformation, the scaled approximation of the original function is found as: (4) By rescaling the function to the real range of black-box the function, an approximation of the function is obtained that is called PCA-HDMR metamodel. III. Methodology The first part of this section briefly reviews the original PSO. The proposed modified PSO is then presented in the second part. A. Original PSO PSO is one of the most well-known swarm algorithms which are inspired by the behaviour of organisms that interrelate in nature within the large groups. PSO originally developed by Kennedy and Eberhart in In general, the PSO algorithm consists of three main steps as follows 11 : Generating positions and velocities of particles Update the velocities Update the positions Each particle refers to a point in a multi-dimensional space whose dimensions are related to the numbers of design variables. The positions of these particles are changed in iterations corresponding to the velocities which are updated at each step. In an original PSO algorithm, the particles are manipulated according to the equations specified by dashed box in Figure 1, where: w is the inertia factor, v is velocity of i th particle in the current motion, c is self confidence factor, and 1 c 2 is swarm confidence factor. Also x i, k i k i P, and g Pk are position of i th particle in current motion, the best position of i th particle in current and all previous moves, and position of the particle with the best global fitness at current move k, respectively. B. PCA-HDMR driven accelerated PSO () Since it is pretty straightway to amend PSO by directly appending other mechanisms such as proper metamodels to simultaneously evaluate some of the particles without need to the expensive function evaluation, PSO has the potential to become a self-modified intelligent structure for enhancing the computational efficiency by itself. As shown in Figure 1, the proposed surrogate-assisted self-accelerated PSO () algorithm is a paradigm constructed based on using the previous experience of the algorithm to define an extra velocity updating term. Based on that, the PCA-HDMR builds an appropriate metamodel from all previous particles evaluated by expensive function from the beginning of the optimization process until the current step. Then, the algorithm calculates new velocities to move the particles from positions in time k to new positions in time k+1 by using four terms instead of three previously defined parameters. In other words, new algorithm needs another value to calculate updated velocities: M so-called metamodel s minima. Its relevant coefficient is named metamodeling g k 3
4 acceleration factor. The coefficients indicate the effect of the current motion, particle own memory, swarm influence, and metamodel driven minima. The algorithm is repeated until a stop criterion reaches (which can be maximum epochs or a defined precision). It should be noted that no more function evaluation is needed for the new algorithm and the previously used sample points are reused for acceleration. Note that the PCA-HDMR needs a minimum number of function evaluations (NOC) for building the model 10. Therefore, and PSO are the same until the number of evaluated points reaches the NOC value. After that the new term is added to the PSO that makes. In the following section, five chosen problems which are a class of test problems with different dimension sizes are addressed to investigate the ability of the proposed optimization algorithm. Figure 1. Flowchart of the proposed algorithm IV. Results and Discussion In this section, the results obtained from is compared with those of the original PSO using five different benchmark functions. The functions are selected with different shapes and with different number of input variables, which are shown in Appendix. Every benchmark function is optimized using and PSO five times and the convergences are shown in the same graph. Figures 2-6 show the results related to the functions # 1-5, respectively. The curves with empty squares show the PSO convergence that are compared to curves with solid square representing approach convergence. The population size is equal to the number of input variables times four. Almost in all the runs related to all functions, shows better performance that PSO. The interesting results obtained from the figures is that applying the new approach has more significant effect in high dimensional 4
5 problems. When the number of input variables increases, the distances between empty square curves and solid square curves are increasing. This is a promising result and can be an opening to optimizing high dimensional, expensive and black-box (HEB) problems. 4 Objective Value Figure 2. The convergence comparison of different PSO approaches: Function # 1, 2-D test function 10 2 Objective Value Figure 3. The convergence comparison of different PSO approaches: Function # 2, 5-D test function 5
6 10 3 Objective Value Figure 4. The convergence comparison of different PSO approaches: Function # 3, 10-D test function 10 1 Objective Value Figure 5. The convergence comparison of different PSO approaches: Function # 4, 20-D test function 6
7 Objective Value Figure 6. The convergence comparison of different PSO approaches: Function # 5, 30-D test function For better illustration of the effect of using surrogate model acceleration in PSO performance, two random runs from the previously shown optimizations are presented in detail in Fig. 7. The runs are related to the function # 4, 20-D function. As shown in flowchart (Fig. 1), surrogate acceleration starts working after the number of evaluated sample points reachs the NOC. The highlighted sections in Fig. 7 show the transition from PSO to SASA- PSO for two independent runs. After completing iteration #12, the number of evaluated functions is reached to the related NOC and the results has a significant jump to better optiumum point at iteration #13. Figure 7. Impact of metamodeling acceleration term on the PSO trend (): Two sample runs of Function # 4, 20-D test function 7
8 V. Conclusion In this paper, a new approach to HDMR-based self-acceleration of an original PSO was introduced. The approach comes from the capability of PCA-HDMR algorithm in building metamodels with random non-uniform sampling. In the proposed Surrogated Assisted Self Accelerated PSO () method, the previously evaluated sample points that are disregarded in the original PSO are used for making the accelerator and no additional function evaluations is needed than the original PSO. The results show considerable improvement of the convergence rate and global optimization searching ability. The approach is more effective in high dimensional problems. More tests and applications will be performed to further test the new algorithm for HEB problems. References 1 Shan, S., and Wang, G., "Survey of modeling and optimization strategies to solve high-dimensional design problems with computationally-expensive black-box functions", Struct Multidisc Optim, vol. 41, pp , Yi, S., Shin, J., and Park, G., "Comparison of MDO methods with mathematical examples", Structural and Multidisciplinary Optimization, vol. 35, pp , Alis, O. F., and Rabitz, H., Efficient implementation of high dimensional model representations, J. of Math. Chem., Vol. 29, No. 2, pp , Chan, F. T. S., and K. Tiwari, M., Swarm intelligence: Focus on ant and particle swarm optimization, I-Tech Education and Publishing, Vienna, Austria, Praveen, C., and Duvigneau, R., Metamodel-assisted particle swarm optimization and application to aerodynamic shape optimization, Research project report at Unité de recherche INRIA Sophia Antipolis, France, Tang, Y., Chen, J., and Wei, J., A surrogate-based particle swarm optimization algorithm for solving optimization problems with expensive black box functions, Engineering Optimization, vol. 45, Issue 5, pp , Im, J-B, Ro, Y-H, Lee, S-Y, and Park, J., Hybrid simulated annealing with particle swarm optimization applied krigging meta model, Proceedings of the 48 th AIAA Structural Dynamics and Materials Conference, Honolulu, Hawaii, USA, April Im, J-B, and Park, J., Stochastic structural optimization using particle swarm optimization, surrogate models and Bayesian statistics, Chinese Journal of Aeronautics, Vol. 26, No. 1, pp , Santana, L. V., Coello, C. A., Hernandez, A. G., and Moises, J., Surrogate-based multi-objective particle swarm optimization, Proceedings of IEEE Swarm Intelligence Symposium, USA, September Hajikolaei, K. H., and Wang G. G., High Dimensional Model Representation with Principal Component Analysis: PCA- HDMR, Submitted to ASME Journal of Mechanical Design, Kennedy, J., and Eberhart, R., "Particle Swarm Optimization", Proceedings of IEEE International Conference on Neural Networks IV, pp , Sobol, I. M., "Sensitivity estimates for nonlinear mathematical models ", Math. Model. Comp., Vol. 1, Issue 4, pp , Rabitz, H., and Alis, O. F., " General foundation of high dimensional model representation ", J. of Math. Chem., Vol. 25, pp , Shan, S., and Wang, G. G., " Metamodeling for High Dimensional Simulation-based Design Problems ", ASME Trans., J. of Mech. Des.., Vol. 132, pp. 1-11, Appendix No. D Function Variable Ranges , , , 0 5, ,2,3,,30 2 2, 8
Center-Based Sampling for Population-Based Algorithms
Center-Based Sampling for Population-Based Algorithms Shahryar Rahnamayan, Member, IEEE, G.GaryWang Abstract Population-based algorithms, such as Differential Evolution (DE), Particle Swarm Optimization
More informationA NEW APPROACH TO SOLVE ECONOMIC LOAD DISPATCH USING PARTICLE SWARM OPTIMIZATION
A NEW APPROACH TO SOLVE ECONOMIC LOAD DISPATCH USING PARTICLE SWARM OPTIMIZATION Manjeet Singh 1, Divesh Thareja 2 1 Department of Electrical and Electronics Engineering, Assistant Professor, HCTM Technical
More informationUniversity, Surrey, BC, Canada Published online: 19 Jun To link to this article:
This article was downloaded by: [Simon Fraser University] On: 20 June 2013, At: 02:51 Publisher: Taylor & Francis Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office:
More informationMobile Robot Path Planning in Static Environments using Particle Swarm Optimization
Mobile Robot Path Planning in Static Environments using Particle Swarm Optimization M. Shahab Alam, M. Usman Rafique, and M. Umer Khan Abstract Motion planning is a key element of robotics since it empowers
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 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 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 informationComparison of Some Evolutionary Algorithms for Approximate Solutions of Optimal Control Problems
Australian Journal of Basic and Applied Sciences, 4(8): 3366-3382, 21 ISSN 1991-8178 Comparison of Some Evolutionary Algorithms for Approximate Solutions of Optimal Control Problems Akbar H. Borzabadi,
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 informationModule 1 Lecture Notes 2. Optimization Problem and Model Formulation
Optimization Methods: Introduction and Basic concepts 1 Module 1 Lecture Notes 2 Optimization Problem and Model Formulation Introduction In the previous lecture we studied the evolution of optimization
More informationParticle Swarm Optimization applied to Pattern Recognition
Particle Swarm Optimization applied to Pattern Recognition by Abel Mengistu Advisor: Dr. Raheel Ahmad CS Senior Research 2011 Manchester College May, 2011-1 - Table of Contents Introduction... - 3 - Objectives...
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 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 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 informationThree-Dimensional Off-Line Path Planning for Unmanned Aerial Vehicle Using Modified Particle Swarm Optimization
Three-Dimensional Off-Line Path Planning for Unmanned Aerial Vehicle Using Modified Particle Swarm Optimization Lana Dalawr Jalal Abstract This paper addresses the problem of offline path planning for
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 informationModel Parameter Estimation
Model Parameter Estimation Shan He School for Computational Science University of Birmingham Module 06-23836: Computational Modelling with MATLAB Outline Outline of Topics Concepts about model parameter
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 *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 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 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 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 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 informationA heuristic approach to find the global optimum of function
Journal of Computational and Applied Mathematics 209 (2007) 160 166 www.elsevier.com/locate/cam A heuristic approach to find the global optimum of function M. Duran Toksarı Engineering Faculty, Industrial
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 informationSIMULTANEOUS COMPUTATION OF MODEL ORDER AND PARAMETER ESTIMATION FOR ARX MODEL BASED ON MULTI- SWARM PARTICLE SWARM OPTIMIZATION
SIMULTANEOUS COMPUTATION OF MODEL ORDER AND PARAMETER ESTIMATION FOR ARX MODEL BASED ON MULTI- SWARM PARTICLE SWARM OPTIMIZATION Kamil Zakwan Mohd Azmi, Zuwairie Ibrahim and Dwi Pebrianti Faculty of Electrical
More informationStep Size Optimization of LMS Algorithm Using Particle Swarm Optimization Algorithm in System Identification
IJCSNS International Journal of Computer Science and Network Security, VOL.13 No.6, June 2013 125 Step Size Optimization of LMS Algorithm Using Particle Swarm Optimization Algorithm in System Identification
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 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 informationA Comparative Study of Genetic Algorithm and Particle Swarm Optimization
IOSR Journal of Computer Engineering (IOSR-JCE) e-issn: 2278-0661,p-ISSN: 2278-8727 PP 18-22 www.iosrjournals.org A Comparative Study of Genetic Algorithm and Particle Swarm Optimization Mrs.D.Shona 1,
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 informationRobust Descriptive Statistics Based PSO Algorithm for Image Segmentation
Robust Descriptive Statistics Based PSO Algorithm for Image Segmentation Ripandeep Kaur 1, Manpreet Kaur 2 1, 2 Punjab Technical University, Chandigarh Engineering College, Landran, Punjab, India Abstract:
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 informationA Native Approach to Cell to Switch Assignment Using Firefly Algorithm
International Journal of Engineering Inventions ISSN: 2278-7461, www.ijeijournal.com Volume 1, Issue 2(September 2012) PP: 17-22 A Native Approach to Cell to Switch Assignment Using Firefly Algorithm Apoorva
More informationCell-to-switch assignment in. cellular networks. barebones particle swarm optimization
Cell-to-switch assignment in cellular networks using barebones particle swarm optimization Sotirios K. Goudos a), Konstantinos B. Baltzis, Christos Bachtsevanidis, and John N. Sahalos RadioCommunications
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 informationAnt Colony Optimization: A New Stochastic Solver for Modeling Vapor-Liquid Equilibrium Data
Ant Colony Optimization: A New Stochastic Solver for Modeling Vapor-Liquid Equilibrium Data Jorge Adan Fernández-Vargas 1, Adrián Bonilla-Petriciolet *, Juan Gabriel Segovia- Hernández 1 and Salvador Hernández
More informationOptimal Design of a Parallel Beam System with Elastic Supports to Minimize Flexural Response to Harmonic Loading
11 th World Congress on Structural and Multidisciplinary Optimisation 07 th -12 th, June 2015, Sydney Australia Optimal Design of a Parallel Beam System with Elastic Supports to Minimize Flexural Response
More informationMulti-Objective Discrete Particle Swarm Optimisation Algorithm for. Integrated Assembly Sequence Planning and Assembly Line Balancing
Multi-Objective Discrete Particle Swarm Optimisation Algorithm for Integrated Assembly Sequence Planning and Assembly Line Balancing Mohd Fadzil Faisae Ab Rashid 1, 2, Windo Hutabarat 1 and Ashutosh Tiwari
More informationArtificial bee colony algorithm with multiple onlookers for constrained optimization problems
Artificial bee colony algorithm with multiple onlookers for constrained optimization problems Milos Subotic Faculty of Computer Science University Megatrend Belgrade Bulevar umetnosti 29 SERBIA milos.subotic@gmail.com
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 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 informationGRID SCHEDULING USING ENHANCED PSO ALGORITHM
GRID SCHEDULING USING ENHANCED PSO ALGORITHM Mr. P.Mathiyalagan 1 U.R.Dhepthie 2 Dr. S.N.Sivanandam 3 1 Lecturer 2 Post Graduate Student 3 Professor and Head Department of Computer Science and Engineering
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 informationFeeder Reconfiguration Using Binary Coding Particle Swarm Optimization
488 International Journal Wu-Chang of Control, Wu Automation, and Men-Shen and Systems, Tsai vol. 6, no. 4, pp. 488-494, August 2008 Feeder Reconfiguration Using Binary Coding Particle Swarm Optimization
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 informationFITTING PIECEWISE LINEAR FUNCTIONS USING PARTICLE SWARM OPTIMIZATION
Suranaree J. Sci. Technol. Vol. 19 No. 4; October - December 2012 259 FITTING PIECEWISE LINEAR FUNCTIONS USING PARTICLE SWARM OPTIMIZATION Pavee Siriruk * Received: February 28, 2013; Revised: March 12,
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 informationSolving Travelling Salesman Problem Using Variants of ABC Algorithm
Volume 2, No. 01, March 2013 ISSN 2278-1080 The International Journal of Computer Science & Applications (TIJCSA) RESEARCH PAPER Available Online at http://www.journalofcomputerscience.com/ Solving Travelling
More informationDesign optimization method for Francis turbine
IOP Conference Series: Earth and Environmental Science OPEN ACCESS Design optimization method for Francis turbine To cite this article: H Kawajiri et al 2014 IOP Conf. Ser.: Earth Environ. Sci. 22 012026
More informationMIXED VARIABLE ANT COLONY OPTIMIZATION TECHNIQUE FOR FEATURE SUBSET SELECTION AND MODEL SELECTION
MIXED VARIABLE ANT COLONY OPTIMIZATION TECHNIQUE FOR FEATURE SUBSET SELECTION AND MODEL SELECTION Hiba Basim Alwan 1 and Ku Ruhana Ku-Mahamud 2 1, 2 Universiti Utara Malaysia, Malaysia, hiba81basim@yahoo.com,
More informationFirst approach to solve linear system of equations by using Ant Colony Optimization
First approach to solve linear system equations by using Ant Colony Optimization Kamil Ksia z ek Faculty Applied Mathematics Silesian University Technology Gliwice Poland Email: kamiksi862@studentpolslpl
More informationNew developments in LS-OPT
7. LS-DYNA Anwenderforum, Bamberg 2008 Optimierung II New developments in LS-OPT Nielen Stander, Tushar Goel, Willem Roux Livermore Software Technology Corporation, Livermore, CA94551, USA Summary: This
More informationFREE SINGULARITY PATH PLANNING OF HYBRID PARALLEL ROBOT
Proceedings of the 11 th International Conference on Manufacturing Research (ICMR2013), Cranfield University, UK, 19th 20th September 2013, pp 313-318 FREE SINGULARITY PATH PLANNING OF HYBRID PARALLEL
More informationLarge Scale Structural Optimization using GENESIS, ANSYS and the Equivalent Static Load Method
Large Scale Structural Optimization using GENESIS, ANSYS and the Equivalent Static Load Method Hong Dong Vanderplaats Research & Development, Inc. 47100 Gardenbrook, Suite 115 Novi, MI 48375, USA Juan
More informationParameter Estimation of PI Controller using PSO Algorithm for Level Control
Parameter Estimation of PI Controller using PSO Algorithm for Level Control 1 Bindutesh V.Saner, 2 Bhagsen J.Parvat 1,2 Department of Instrumentation & control Pravara Rural college of Engineering, Loni
More informationData-Driven Evolutionary Optimization of Complex Systems: Big vs Small Data
Data-Driven Evolutionary Optimization of Complex Systems: Big vs Small Data Yaochu Jin Head, Nature Inspired Computing and Engineering (NICE) Department of Computer Science, University of Surrey, United
More informationMetamodeling Transfer Capability of Manitoba-Ontario Electrical Interconnections
Journal of Mechanics Engineering and Automation 1 (2011) 464-472 D DAVID PUBLISHING Metamodeling Transfer Capability of Manitoba-Ontario Electrical Interconnections Songqing Shan 1, Wenjie Zhang 2, Myrna
More informationComputational Optimization, Modelling and Simulation: Past, Present and Future
Procedia Computer Science Volume 29, 2014, Pages 754 758 ICCS 2014. 14th International Conference on Computational Science Computational Optimization, Modelling and Simulation: Past, Present and Future
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 informationHigh-speed Interconnect Simulation Using Particle Swarm Optimization
1 High-speed Interconnect Simulation Using Particle Swarm Optimization Subas Bastola and Chen-yu Hsu Intel Corporation subas.bastola@intel.com, chen-yu.hsu@intel.com Abstract Particle Swarm Optimization
More informationTopology Optimization of Multiple Load Case Structures
Topology Optimization of Multiple Load Case Structures Rafael Santos Iwamura Exectuive Aviation Engineering Department EMBRAER S.A. rafael.iwamura@embraer.com.br Alfredo Rocha de Faria Department of Mechanical
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 informationAdvances in Military Technology Vol. 11, No. 1, June Influence of State Space Topology on Parameter Identification Based on PSO Method
AiMT Advances in Military Technology Vol. 11, No. 1, June 16 Influence of State Space Topology on Parameter Identification Based on PSO Method M. Dub 1* and A. Štefek 1 Department of Aircraft Electrical
More informationSimplifying Handwritten Characters Recognition Using a Particle Swarm Optimization Approach
ISSN 2286-4822, www.euacademic.org IMPACT FACTOR: 0.485 (GIF) Simplifying Handwritten Characters Recognition Using a Particle Swarm Optimization Approach MAJIDA ALI ABED College of Computers Sciences and
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 informationDiscrete Particle Swarm Optimization for TSP based on Neighborhood
Journal of Computational Information Systems 6:0 (200) 3407-344 Available at http://www.jofcis.com Discrete Particle Swarm Optimization for TSP based on Neighborhood Huilian FAN School of Mathematics and
More informationThe Dynamic Response of an Euler-Bernoulli Beam on an Elastic Foundation by Finite Element Analysis using the Exact Stiffness Matrix
Journal of Physics: Conference Series The Dynamic Response of an Euler-Bernoulli Beam on an Elastic Foundation by Finite Element Analysis using the Exact Stiffness Matrix To cite this article: Jeong Soo
More informationApplying Neural Network Architecture for Inverse Kinematics Problem in Robotics
J. Software Engineering & Applications, 2010, 3: 230-239 doi:10.4236/jsea.2010.33028 Published Online March 2010 (http://www.scirp.org/journal/jsea) Applying Neural Network Architecture for Inverse Kinematics
More informationImproving Results and Performance of Collaborative Filtering-based Recommender Systems using Cuckoo Optimization Algorithm
Improving Results and Performance of Collaborative Filtering-based Recommender Systems using Cuckoo Optimization Algorithm Majid Hatami Faculty of Electrical and Computer Engineering University of Tabriz,
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 informationDesign Optimization of Building Structures Using a Metamodeling Method
Proceedings of the 3 rd International Conference on Civil, Structural and Transportation Engineering (ICCSTE'18) Niagara Falls, Canada June 10 12, 2018 Paper No. 137 DOI: 10.11159/iccste18.137 Design Optimization
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 informationImage Compression: An Artificial Neural Network Approach
Image Compression: An Artificial Neural Network Approach Anjana B 1, Mrs Shreeja R 2 1 Department of Computer Science and Engineering, Calicut University, Kuttippuram 2 Department of Computer Science and
More informationEfficient Robust Shape Optimization for Crashworthiness
10 th World Congress on Structural and Multidisciplinary Optimization May 19-24, 2013, Orlando, Florida, USA Efficient Robust Shape Optimization for Crashworthiness Milan Rayamajhi 1, Stephan Hunkeler
More informationSwarmOps for Java. Numeric & Heuristic Optimization Source-Code Library for Java The Manual Revision 1.0
Numeric & Heuristic Optimization Source-Code Library for Java The Manual Revision 1.0 By Magnus Erik Hvass Pedersen June 2011 Copyright 2009-2011, all rights reserved by the author. Please see page 4 for
More informationAn Approach to Polygonal Approximation of Digital CurvesBasedonDiscreteParticleSwarmAlgorithm
Journal of Universal Computer Science, vol. 13, no. 10 (2007), 1449-1461 submitted: 12/6/06, accepted: 24/10/06, appeared: 28/10/07 J.UCS An Approach to Polygonal Approximation of Digital CurvesBasedonDiscreteParticleSwarmAlgorithm
More informationAssessing Particle Swarm Optimizers Using Network Science Metrics
Assessing Particle Swarm Optimizers Using Network Science Metrics Marcos A. C. Oliveira-Júnior, Carmelo J. A. Bastos-Filho and Ronaldo Menezes Abstract Particle Swarm Optimizers (PSOs) have been widely
More informationMultimodal Information Spaces for Content-based Image Retrieval
Research Proposal Multimodal Information Spaces for Content-based Image Retrieval Abstract Currently, image retrieval by content is a research problem of great interest in academia and the industry, due
More informationVariable Neighborhood Particle Swarm Optimization for Multi-objective Flexible Job-Shop Scheduling Problems
Variable Neighborhood Particle Swarm Optimization for Multi-objective Flexible Job-Shop Scheduling Problems Hongbo Liu 1,2,AjithAbraham 3,1, Okkyung Choi 3,4, and Seong Hwan Moon 4 1 School of Computer
More informationParticle swarm optimization for mobile network design
Particle swarm optimization for mobile network design Ayman A. El-Saleh 1,2a), Mahamod Ismail 1, R. Viknesh 2, C. C. Mark 2, and M. L. Chan 2 1 Department of Electrical, Electronics, and Systems Engineering,
More informationConvolutional Code Optimization for Various Constraint Lengths using PSO
International Journal of Electronics and Communication Engineering. ISSN 0974-2166 Volume 5, Number 2 (2012), pp. 151-157 International Research Publication House http://www.irphouse.com Convolutional
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 informationA PSO-based Generic Classifier Design and Weka Implementation Study
International Forum on Mechanical, Control and Automation (IFMCA 16) A PSO-based Generic Classifier Design and Weka Implementation Study Hui HU1, a Xiaodong MAO1, b Qin XI1, c 1 School of Economics and
More informationA NEW METHODOLOGY FOR EMERGENT SYSTEM IDENTIFICATION USING PARTICLE SWARM OPTIMIZATION (PSO) AND THE GROUP METHOD OF DATA HANDLING (GMDH)
A NEW METHODOLOGY FOR EMERGENT SYSTEM IDENTIFICATION USING PARTICLE SWARM OPTIMIZATION (PSO) AND THE GROUP METHOD OF DATA HANDLING (GMDH) Mark S. Voss Dept. of Civil and Environmental Engineering Marquette
More informationImage Edge Detection Based on Cellular Neural Network and Particle Swarm Optimization
Transactions on Computer Science and Technology March 2014, Volume 3, Issue 1, PP.1-8 Image Edge Detection Based on Cellular Neural Network and Particle Swarm Optimization Zhengxia Wang #, Lili Li Department
More informationA High-Order Accurate Unstructured GMRES Solver for Poisson s Equation
A High-Order Accurate Unstructured GMRES Solver for Poisson s Equation Amir Nejat * and Carl Ollivier-Gooch Department of Mechanical Engineering, The University of British Columbia, BC V6T 1Z4, Canada
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 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 informationDesigning of Optimized Combinational Circuits Using Particle Swarm Optimization Algorithm
Advances in Computational Sciences and Technology ISSN 0973-6107 Volume 10, Number 8 (2017) pp. 2395-2410 Research India Publications http://www.ripublication.com Designing of Optimized Combinational Circuits
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 informationOPTIMUM CAPACITY ALLOCATION OF DISTRIBUTED GENERATION UNITS USING PARALLEL PSO USING MESSAGE PASSING INTERFACE
OPTIMUM CAPACITY ALLOCATION OF DISTRIBUTED GENERATION UNITS USING PARALLEL PSO USING MESSAGE PASSING INTERFACE Rosamma Thomas 1, Jino M Pattery 2, Surumi Hassainar 3 1 M.Tech Student, Electrical and Electronics,
More informationParticle Swarm Optimization Methods for Pattern. Recognition and Image Processing
Particle Swarm Optimization Methods for Pattern Recognition and Image Processing by Mahamed G. H. Omran Submitted in partial fulfillment of the requirements for the degree Philosophiae Doctor in the Faculty
More informationTHREE PHASE FAULT DIAGNOSIS BASED ON RBF NEURAL NETWORK OPTIMIZED BY PSO ALGORITHM
THREE PHASE FAULT DIAGNOSIS BASED ON RBF NEURAL NETWORK OPTIMIZED BY PSO ALGORITHM M. Sivakumar 1 and R. M. S. Parvathi 2 1 Anna University, Tamilnadu, India 2 Sengunthar College of Engineering, Tamilnadu,
More information1 Lab 5: Particle Swarm Optimization
1 Lab 5: Particle Swarm Optimization This laboratory requires the following: (The development tools are installed in GR B0 01 already): C development tools (gcc, make, etc.) Webots simulation software
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 informationThe Pennsylvania State University. The Graduate School. Department of Electrical Engineering COMPARISON OF CAT SWARM OPTIMIZATION WITH PARTICLE SWARM
The Pennsylvania State University The Graduate School Department of Electrical Engineering COMPARISON OF CAT SWARM OPTIMIZATION WITH PARTICLE SWARM OPTIMIZATION FOR IIR SYSTEM IDENTIFICATION A Thesis in
More informationOpportunistic Self Organizing Migrating Algorithm for Real-Time Dynamic Traveling Salesman Problem
Opportunistic Self Organizing Migrating Algorithm for Real-Time Dynamic Traveling Salesman Problem arxiv:1709.03793v1 [cs.ne] 12 Sep 2017 Shubham Dokania, Sunyam Bagga, and Rohit Sharma shubham.k.dokania@gmail.com,
More informationAn Optimization of Association Rule Mining Algorithm using Weighted Quantum behaved PSO
An Optimization of Association Rule Mining Algorithm using Weighted Quantum behaved PSO S.Deepa 1, M. Kalimuthu 2 1 PG Student, Department of Information Technology 2 Associate Professor, Department of
More informationImplementation and Comparison between PSO and BAT Algorithms for Path Planning with Unknown Environment
Implementation and Comparison between PSO and BAT Algorithms for Path Planning with Unknown Environment Dr. Mukesh Nandanwar 1, Anuj Nandanwar 2 1 Assistance Professor (CSE), Chhattisgarh Engineering College,
More information