A new strategy for finding good local guides in MOPSO

This paper presents a new algorithm that extends Particle Swarm Optimization (PSO) to deal with multi-objective problems. It makes two main contributions. The first is that the square root distance (SRD) computation among particles and leaders is proposed to be the criterion of the local best selection. This new criterion can make all swarms explore the whole Pareto-front more uniformly. The second contribution is the procedure to update the archive members. When the external archive is full and a new member is to be added, an existing archive member with the smallest SRD value among its neighbors will be deleted. With this arrangement, the non-dominated solutions can be well distributed. Through the performance investigation, our proposed algorithm performed better than two well-known multi-objective PSO algorithms, MOPSO-σ and MOPSO-CD, in terms of different standard measures.

[1]  Jürgen Branke,et al.  Empirical comparison of MOPSO methods - Guide selection and diversity preservation - , 2009, 2009 IEEE Congress on Evolutionary Computation.

[2]  Aurora Trinidad Ramirez Pozo,et al.  A Comparison of methods for leader selection in many-objective problems , 2012, 2012 IEEE Congress on Evolutionary Computation.

[3]  Prospero C. Naval,et al.  An effective use of crowding distance in multiobjective particle swarm optimization , 2005, GECCO '05.

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

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

[6]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

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

[8]  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).

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

[10]  Christian Fonteix,et al.  Multicriteria optimization using a genetic algorithm for determining a Pareto set , 1996, Int. J. Syst. Sci..

[11]  Frank Kursawe,et al.  A Variant of Evolution Strategies for Vector Optimization , 1990, PPSN.

[12]  Jonathan E. Fieldsend,et al.  A Multi-Objective Algorithm based upon Particle Swarm Optimisation, an Efficient Data Structure and , 2002 .

[13]  Jason R. Schott.  Fault Tolerant Design Using Single and Multicriteria Genetic Algorithm Optimization. , 1995 .

[14]  Gary B. Lamont,et al.  Multiobjective evolutionary algorithms: classifications, analyses, and new innovations , 1999 .

[15]  M Reyes Sierra,et al.  Multi-Objective Particle Swarm Optimizers: A Survey of the State-of-the-Art , 2006 .

[16]  Manoj Kumar Tiwari,et al.  Swarm Intelligence, Focus on Ant and Particle Swarm Optimization , 2007 .

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

[18]  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).

[19]  Richard C. Chapman,et al.  Application of Particle Swarm to Multiobjective Optimization , 1999 .

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

[21]  Lothar Thiele,et al.  Comparison of Multiobjective Evolutionary Algorithms: Empirical Results , 2000, Evolutionary Computation.

[22]  Eckart Zitzler,et al.  Evolutionary algorithms for multiobjective optimization: methods and applications , 1999 .