A study of hybridizing Population based Meta heuristics

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Volume 119 No. 12 2018, 15989-15994 ISSN: 1314-3395 (on-line version) url: http://www.ijpam.eu ijpam.eu A study of hybridizing Population based Meta heuristics Dr.J.Arunadevi 1, R.

1 Volume 119 No , ISSN: (on-line version) url: ijpam.eu A study of hybridizing Population based Meta heuristics Dr.J.Arunadevi 1, R.Uma 2 1 Assistant Professor, PG Department of Computer Science and Research Center, R.D.Govt. Arts college, Sivaganga, Tamilnadu 2 Ph.D Research Scholar, PG Department of Computer Science and Research Center, R.D.Govt. Arts college, Sivaganga, Tamilnadu Abstract: Meta heuristics are those algorithms which are domain independent to solve the problem. Hybridizing Meta heuristics with Meta heuristics represents the beginning of hybridizing Meta heuristics. Later, it got widely used especially integrating nature-inspired Meta heuristics with local search methods. In this paper we have studied an exhausted review on the hybrid Meta heuristics. Here we discuss about the need for the hybridization, various methods for population based Meta heuristics. Keywords: Heuristics, Meta heuristics and Hybrid Meta heuristics 1. Introduction Meta heuristics are those procedures which are used to find the tactics for developing the heuristics. This heuristics are used to solve the optimization problems but not with guaranteed optimal solutions but provides adequate solution. Meta heuristics algorithms are those which provide an acceptable solution in a mean time [1]. Meta heuristics gain importance because it is defined and designed in a generic manner irrespective of the problem and it doesn t have any constraint on the formulation of the optimization problem. Hybrid Meta heuristics is the technique used for combining other strategies with the Meta heuristics to provide a proficient solution. Blum et al in [2] stats that hybrid Metaheuristics are those which uses the components from other algorithms in the optimization research area. In [3] 15989

2 Raidl and Puchinger says that many better solutions can be given by using the synergies between different approaches than the traditional algorithms. This paper concentrates on the various types of hybrid Meta heuristics and the mechanics behind them. 2. Hybrid Meta heuristics Meta heuristics are those algorithms which provide a near optimal solution in a reasonable time. The notion for the development of hybridization in this context is to improve the performance of the algorithm by the combination of other techniques with the Meta heuristics. In [4] the author says that the hybridization can be done among Meta heuristics algorithms itself, that is by combining two or more Meta heuristics. The other ways of hybridization includes Meta heuristics with the problem specific algorithms and the hybridization with operation research techniques or other artificial intelligent techniques. Human interaction hybridization also accounted by the author. The levels of hybridization must be considered. Order of execution and control strategies can be also considered for the parameters for hybridization. 3. Population based Meta heuristics In this class of Meta heuristics the algorithm work with a set of solutions, this solutions are modified or combine for the production of new solution, which is better than the previous one. The common feature of these types of methods is that they generate a population in search spaces and then they intend to improve this population. There are number of population based Meta heuristics are available. In this section let us discuss the advantages and disadvantages of these Meta heuristics 3.1 Genetic algorithms (GA) This is basically a search type of algorithm which works with a number of chromosomes. It creates population which consists of chromosomes and this algorithm works as per the mechanics of natural selection and natural evolution process and have faith in the survival of the fittest Advantages This is inherently parallel in nature and distributive It supports the multi objective phenomenon It works with the population of solutions so it is not to be trapped with local optimum Disadvantages Time taken for convergence Fine tuning the parameters in a trial and error base Designing of the fitness function to be done very carefully 15990

3 3.2 Particle swarm optimization (PSO) This algorithm consists of a swarm which consists of particles. This particles travel in the search space based on some formula. The travel of the particles is directed by its own best position and the global best position in the entire swarm. This algorithm is designed based on bird flocking [5] Advantages Momentum effect leads to quick convergence. Diversity and exploration over single swarm is good. It doesn t have any overlapping and mutation calculations Disadvantages It suffers in partial optimum in the high dimensional space Only the best particles gives information and it leads to one way communication Initial distribution of the particles affect the result 3.3 Ant Colony Optimization (ACO) ACO are the population based Meat heuristics which is used for finding the approximate solutions which is motivated by the foraging behavior of real ant colonies [6]. The working principle is based on the pheromone deposit by the ant to the other ants for identification of the path. This algorithm work with some population of ants which is used to find the shortest path from the starting point to the target by the higher concentration of the pheromone in the path Advantages The problem constraints can be dynamic, adaptive task allocation Convergence is guaranteed For travelling sales man problem it is relatively efficient Disadvantages Convergence time is not guaranteed Sensitivity is more Detecting threats in individual behavior is difficult Other population based meta heuristics such as differential evolution [7], Scatter search [8], Artificial fish swarm [9], Artificial Bee colony[10], Bacterial foraging[11], Shuffled Leaping- Frog Algorithm[12], Differential Search Algorithm [13] etc also have their own mechanics. 4. Need for hybridization 15991

4 In case of GA there is no guarantee for optimality we have the assured exponential convergence. There is a tradeoff between local search and then the global search. When considering PSO the swarm may converge prematurely because the global best particles converge to a single point [14]. In the PSO the position of the particle is important and it is based on the parameter limitation, which leads to the reduction in the diversity of the particle. If the global best particle doesn t change its position it leads to stagnation in the population which leads to local optimum. Because of the stochastic nature there is not a single way to achieve global optimum [15]. ACO employed with local search strategy could improve the quality of the solution [16]. 5. Conclusion This paper talks about the Meta heuristics based on the population methods for the operations. Here we discuss both the advantages and the disadvantages of the methods. The need of hybridization is well explained. Based on the literature survey we can find the levels of hybridization in the Meta heuristics. The researcher find that there is the need for hybridizing the population based methods is important since it can be time consuming for the convergence to give the near optimal or approximate solutions. The future work could concentrate on the exact place and technique to be used for hybridization in the population based Meta heuristics. References [1] Fred Glover and Kenneth Sörensen, Metaheuristics. Scholarpedia, 10(4): [2] Christian Blum, Jakob Puchinger, Günther Raidl, Andrea Roli. Hybrid metaheuristics in combinatorial optimization: A survey. Applied Soft Computing, Elsevier, 2011, 11 (6), pp [3] G unther R. Raidl and Jakob Puchinger, Combining (Integer) Linear Programming Techniques and Metaheuristics for Combinatorial Optimization, in Hybrid Metaheuristics An Emerging Approach to Optimization, studies in computational intelligence, Springer [4] G. R. Raidl, J. Puchinger, C. Blum, Metaheuristic hybrids, in: M. Gendreau, J. Y. Potvin (Eds.), Handbook of Metaheuristics, 2nd Edition, Vol. 146 of International Series in Operations Research & Management Science, Springer Verlag, Berlin, Germany, 2010, pp [5] Yudong Zhang, Shuihua Wang, and Genlin Ji, A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications, Mathematical Problems in Engineering, vol. 2015, Article ID , 38 pages, doi: /2015/

5 [6] Duan H. (2011) Ant Colony Optimization: Principle, Convergence and Application. In: Panigrahi B.K., Shi Y., Lim MH. (eds) Handbook of Swarm Intelligence. Adaptation, Learning, and Optimization, vol 8. Springer, Berlin, Heidelberg [7] Storn, R.; Price, K. (1997). "Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces". Journal of Global Optimization. 11: [8] RafaelMartía, ManuelLagunab, FredGloverb, Principles of scatter search, European Journal of Operational Research, Volume 169, Issue 2, 1 March 2006, Pages [9] Neshat, M., Sepidnam, G., Sargolzaei, M. et al. Artif Intell Rev (2014) 42: 965. [10] Pham, D. T., Ghanbarzadeh, A., Koc, E., Otri, S., Rahim, S., Zaidi, M. (2005). The bees algorithm. Technical report, Manufacturing Engineering Centre, Cardiff University, UK. [11] Das S., Biswas A., Dasgupta S., Abraham A. (2009) Bacterial Foraging Optimization Algorithm: Theoretical Foundations, Analysis, and Applications. In: Abraham A., Hassanien AE., Siarry P., Engelbrecht A. (eds) Foundations of Computational Intelligence Volume 3. Studies in Computational Intelligence, vol 203. Springer, Berlin, Heidelberg [12] Muzaffar Eusuff, Kevin Lansey & Fayzul Pasha (2007) Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization, Engineering Optimization, 38:2, [13] D.Ragunath, Dr.V.Venkatesa Kumar, Dr.M.Newlin Rajkumar, Distributed Heuristic Load Balancing System Using Replication Approach, International Journal of Innovations in Scientific and Engineering Research (IJISER), Vol.4, No.4, pp , [14] Bo Liu, Composite Differential Search Algorithm, Journal of Applied Mathematics, vol. 2014, Article ID , 15 pages, doi: /2014/ [15] Van den Bergh F. and Engelbrecht A.P., A Cooperative Approach to Particle Swarm Optimization, IEEE Transactions on Evolutionary Computation, 2004, pp [16] K. Premalatha, A.M. Natarajan, Hybrid PSO and GA for Global Maximization, Int. J. Open Problems Compt. Math., Vol. 2, No. 4, December 2009 ISSN [17] He, Jiang & Zhang, Jingyuan & Xuan, Jifeng & Ren, Zhilei & Hu, Yan. (2010). A Hybrid ACO algorithm for the Next Release Problem. 2nd International Conference on Software Engineering and Data Mining, SEDM

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International Journal of Current Research and Modern Education (IJCRME) ISSN (Online): & Impact Factor: Special Issue, NCFTCCPS -

International Journal of Current Research and Modern Education (IJCRME) ISSN (Online): & Impact Factor: Special Issue, NCFTCCPS - TO SOLVE ECONOMIC DISPATCH PROBLEM USING SFLA P. Sowmya* & Dr. S. P. Umayal** * PG Scholar, Department Electrical and Electronics Engineering, Muthayammal Engineering College, Rasipuram, Tamilnadu ** Dean