A Study of Swarm Topologies and Their Influence on the Performance of Multi-Objective Particle Swarm Optimizers

It has been shown that swarm topologies influence the behavior of Particle Swarm Optimization (PSO). A large number of connections stimulates exploitation, while a low number of connections stimulates exploration. Furthermore, a topology with four links per particle is known to improve PSO’s performance. In spite of this, there are few studies about the influence of swarm topologies in Multi-Objective Particle Swarm Optimizers (MOPSOs). We analyze the influence of star, tree, lattice, ring and wheel topologies in the performance of the Speed-constrained Multi-objective Particle Swarm Optimizer (SMPSO) when adopting a variety of multi-objective problems, including the well-known ZDT, DTLZ and WFG test suites. Our results indicate that the selection of the proper topology does indeed improve the performance in SMPSO.

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

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

[3]  Kwok-wing Chau,et al.  Neural network river forecasting with multi-objective fully informed particle swarm optimization , 2015 .

[4]  Hisao Ishibuchi,et al.  Difficulties in specifying reference points to calculate the inverted generational distance for many-objective optimization problems , 2014, 2014 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making (MCDM).

[5]  R. Lyndon While,et al.  A Scalable Multi-objective Test Problem Toolkit , 2005, EMO.

[6]  T. Hatanaka,et al.  An experimental study for multi-objective optimization by particle swarm with graph based archive , 2012, 2012 Proceedings of SICE Annual Conference (SICE).

[7]  James Kennedy,et al.  Small worlds and mega-minds: effects of neighborhood topology on particle swarm performance , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[8]  Michael N. Vrahatis,et al.  Multi-Objective Particles Swarm Optimization Approaches , 2008 .

[9]  Enrique Alba,et al.  SMPSO: A new PSO-based metaheuristic for multi-objective optimization , 2009, 2009 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making(MCDM).

[10]  Marco Laumanns,et al.  Scalable Test Problems for Evolutionary Multiobjective Optimization , 2005, Evolutionary Multiobjective Optimization.

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