On the effect of selection and archiving operators in many-objective particle swarm optimisation

The particle swarm optimisation (PSO) heuristic has been used for a number of years now to perform multi-objective optimisation, however its performance on many-objective optimisation (problems with four or more competing objectives) has been less well examined. Many-objective optimisation is well-known to cause problems for Pareto-based evolutionary optimisers, so it is of interest to see how well PSO copes in this domain, and how non-Pareto quality measures perform when integrated into PSO. Here we compare and contrast the performance of canonical PSO, using a wide range of many-objective quality measures, on a number of different parametrised test functions for up to 20 competing objectives. We examine the use of eight quality measures as selection operators for guides when truncated non-dominated archives of guides are maintained, and as maintenance operators, for choosing which solutions should be maintained as guides from one generation to the next. We find that the Controlling Dominance Area of Solutions approach performs exceptionally well as a quality measure to determine archive membership for global and local guides. As a selection operator, the Average Rank and Sum of Ratios measures are found to generally provide the best performance.

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

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

[3]  Rolf Drechsler,et al.  Multi-objective Optimisation Based on Relation Favour , 2001, EMO.

[4]  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).

[5]  Kalyanmoy Deb,et al.  A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II , 2000, PPSN.

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

[7]  Kiyoshi Tanaka,et al.  Controlling Dominance Area of Solutions in Multiobjective Evolutionary Algorithms and Performance Analysis on Multiobjective 0/1 Knapsack Problems , 2007 .

[8]  Carlos A. Coello Coello,et al.  Two novel approaches for many-objective optimization , 2010, IEEE Congress on Evolutionary Computation.

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

[10]  Aurora Pozo,et al.  Using Different Many-Objective Techniques in Particle Swarm Optimization for Many Objective Problems: An Empirical Study , 2011 .

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

[12]  Gary B. Lamont,et al.  Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation) , 2006 .

[13]  Gary B. Lamont,et al.  Evolutionary Algorithms for Solving Multi-Objective Problems , 2002, Genetic Algorithms and Evolutionary Computation.

[14]  David W. Corne,et al.  Techniques for highly multiobjective optimisation: some nondominated points are better than others , 2007, GECCO '07.

[15]  Soon-Thiam Khu,et al.  An Investigation on Preference Order Ranking Scheme for Multiobjective Evolutionary Optimization , 2007, IEEE Transactions on Evolutionary Computation.

[16]  Russell C. Eberhart,et al.  Multiobjective optimization using dynamic neighborhood particle swarm optimization , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

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

[18]  Nikhil Padhye.  Comparison of archiving methods in multi-objectiveparticle swarm optimization (MOPSO): empirical study , 2009, GECCO '09.

[19]  J. Fieldsend.  Multi-Objective Particle Swarm Optimisation Methods , 2004 .

[20]  Peter J. Bentley,et al.  Finding Acceptable Solutions in the Pareto-Optimal Range using Multiobjective Genetic Algorithms , 1998 .

[21]  Xiaodong Li,et al.  Using a distance metric to guide PSO algorithms for many-objective optimization , 2009, GECCO.

[22]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.