A hybrid particle swarm approach based on Tribes and tabu search for multi-objective optimization

Tuning the parameters of any evolutionary algorithm is considered as a very difficult task. In this paper, we present a new adaptive multi-objective technique which consists of a hybridization between a particular particle swarm optimization approach (Tribes) and tabu search (TS) technique. The main idea behind this hybridization is to combine the rapid convergence of Tribes with the high efficient exploitation of a local search technique based on TS. In addition, we propose three different places where the local search can be applied: TS applied on the particles of the archive, TS applied only on the best particle of each tribe and TS applied on each particle of the swarm. The aim of those propositions is to study the impact of the place where the local search is applied on the performance of our hybridized Tribes. The mechanisms proposed are validated using 10 different functions from specialized literature of multi-objective optimization. The obtained results show that using this kind of hybridization is justified as it is able to improve the quality of the solutions in the majority of cases.

[1]  Frans van den Bergh,et al.  An analysis of particle swarm optimizers , 2002 .

[2]  Carlos A. Coello Coello,et al.  Multi-Objective Particle Swarm Optimizers: An Experimental Comparison , 2009, EMO.

[3]  Rainer Laur,et al.  Adaptive parameter setting for a multi-objective particle swarm optimization algorithm , 2007, 2007 IEEE Congress on Evolutionary Computation.

[4]  Manuel Laguna,et al.  Tabu Search , 1997 .

[5]  M.N. Vrahatis,et al.  Particle swarm optimizers for Pareto optimization with enhanced archiving techniques , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[6]  Patrick Siarry,et al.  Tabu Search applied to global optimization , 2000, Eur. J. Oper. Res..

[7]  Bernhard Sendhoff,et al.  On Test Functions for Evolutionary Multi-objective Optimization , 2004, PPSN.

[8]  Patrick Siarry,et al.  A Study of the Efficiency of the Hybridization of a Particle Swarm Optimizer and Tabu Search , 2012 .

[9]  M. N. Vrahatis,et al.  Particle swarm optimization method in multiobjective problems , 2002, SAC '02.

[10]  Carlos A. Coello Coello,et al.  Micro-MOPSO: A Multi-Objective Particle Swarm Optimizer That Uses a Very Small Population Size , 2010, Multi-Objective Swarm Intelligent System.

[11]  R. Lyndon While,et al.  A review of multiobjective test problems and a scalable test problem toolkit , 2006, IEEE Transactions on Evolutionary Computation.

[12]  Khaled Ghédira,et al.  New Proposal for a Multi-objective Technique using Tribes and Tabu Search , 2010, ICINCO.

[13]  David W. Corne,et al.  Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy , 2000, Evolutionary Computation.

[14]  Lothar Thiele,et al.  A Tutorial on the Performance Assessment of Stochastic Multiobjective Optimizers , 2006 .

[15]  Carlos A. Coello Coello,et al.  Towards a More Efficient Multi-Objective Particle Swarm Optimizer , 2008 .

[16]  Yann Cooren.  Perfectionnement d'un algorithme adaptatif d'optimisation par essaim particulaire : application en génie médical et en électronique. (Improvement of an adaptive algorithm of Optimization by Swarm Particulaire : application in medical engineering and in electronics) , 2008 .

[17]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[18]  Carlos A. Coello Coello,et al.  A Study of Techniques to Improve the Efficiency of a Multi-Objective Particle Swarm Optimizer , 2007, Evolutionary Computation in Dynamic and Uncertain Environments.

[19]  Tapabrata Ray,et al.  A Swarm Metaphor for Multiobjective Design Optimization , 2002 .

[20]  A. Shamsai,et al.  Multi-objective Optimization , 2017, Encyclopedia of Machine Learning and Data Mining.

[21]  Carlos A. Coello Coello,et al.  Using Clustering Techniques to Improve the Performance of a Multi-objective Particle Swarm Optimizer , 2004, GECCO.

[22]  Jürgen Teich,et al.  Strategies for finding good local guides in multi-objective particle swarm optimization (MOPSO) , 2003, Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706).

[23]  Xavier Gandibleux,et al.  A survey and annotated bibliography of multiobjective combinatorial optimization , 2000, OR Spectr..

[24]  Carlos A. Coello Coello,et al.  Handling multiple objectives with particle swarm optimization , 2004, IEEE Transactions on Evolutionary Computation.

[25]  Andrzej Ameljańczyk,et al.  Multicriteria Optimization in Engineering Design , 1994 .

[26]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..

[27]  Russell C. Eberhart,et al.  Particle swarm with extended memory for multiobjective optimization , 2003, Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706).

[28]  C. Coello,et al.  Improving PSO-based Multi-Objective Optimization using Crowding , Mutation and �-Dominance , 2005 .

[29]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[30]  Jonathan E. Fieldsend,et al.  A MOPSO Algorithm Based Exclusively on Pareto Dominance Concepts , 2005, EMO.

[31]  C.A. Coello Coello,et al.  MOPSO: a proposal for multiple objective particle swarm optimization , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[32]  Andrzej Osyczka,et al.  7 – Multicriteria optimization for engineering design , 1985 .

[33]  Konstantinos E. Parsopoulos,et al.  MULTIOBJECTIVE OPTIMIZATION USING PARALLEL VECTOR EVALUATED PARTICLE SWARM OPTIMIZATION , 2003 .

[34]  Kalyanmoy Deb,et al.  Multi-objective Optimization , 2014 .

[35]  Andries Petrus Engelbrecht,et al.  Particle swarm optimization: an introduction and its recent developments , 2007, Annual Conference on Genetic and Evolutionary Computation.

[36]  Günter Rudolph,et al.  Capabilities of EMOA to Detect and Preserve Equivalent Pareto Subsets , 2007, EMO.

[37]  Janez Demsar,et al.  Statistical Comparisons of Classifiers over Multiple Data Sets , 2006, J. Mach. Learn. Res..

[38]  Carlos A. Coello Coello,et al.  An updated survey of GA-based multiobjective optimization techniques , 2000, CSUR.

[39]  Carlos A. Coello Coello,et al.  Improving PSO-Based Multi-objective Optimization Using Crowding, Mutation and epsilon-Dominance , 2005, EMO.