The role of /spl epsi/-dominance in multi objective particle swarm optimization methods

In this paper, the influence of /spl epsi/-dominance on multi-objective particle swarm optimization (MOPSO) methods is studied. The most important role of /spl epsi/-dominance is to bound the number of non-dominated solutions stored in the archive (archive size), which has influences on computational time, convergence and diversity of solutions. Here, /spl epsi/-dominance is compared with the existing clustering technique for fixing the archive size and the solutions are compared in terms of computational time, convergence and diversity. A new diversity metric is also suggested. The results show that the /spl epsi/-dominance method can find solutions much faster than the clustering technique with comparable and even in some cases better convergence and diversity.

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

[2]  Mihalis Yannakakis,et al.  On the approximability of trade-offs and optimal access of Web sources , 2000, Proceedings 41st Annual Symposium on Foundations of Computer Science.

[3]  Marco Laumanns,et al.  SPEA2: Improving the strength pareto evolutionary algorithm , 2001 .

[4]  Jürgen Teich,et al.  Comparison of data structures for storing Pareto-sets in MOEAs , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[5]  A. Farhang-Mehr,et al.  Diversity assessment of Pareto optimal solution sets: an entropy approach , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[6]  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).

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

[8]  Marco Laumanns,et al.  Scalable multi-objective optimization test problems , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[9]  Marco Laumanns,et al.  Archiving With Guaranteed Convergence And Diversity In Multi-objective Optimization , 2002, GECCO.

[10]  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).

[11]  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).

[12]  Jürgen Teich,et al.  Covering Pareto Sets by Multilevel Evolutionary Subdivision Techniques , 2003, EMO.

[13]  Xin Yao,et al.  Performance Scaling of Multi-objective Evolutionary Algorithms , 2003, EMO.

[14]  Michael N. Vrahatis,et al.  Recent approaches to global optimization problems through Particle Swarm Optimization , 2002, Natural Computing.

[15]  Jürgen Teich,et al.  Quad-trees: A Data Structure for Storing Pareto Sets in Multiobjective Evolutionary Algorithms with Elitism , 2005, Evolutionary Multiobjective Optimization.