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 op-timisation (problems with four or more competing objec-tives) has been less well examined. Many-objective opti-misation 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 mea-sures 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 compet-ing objectives. We examine the use of eight quality mea-sures as selection operators for guides when truncated non-dominated archives of guides are maintained, and as main-tenance 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 ap-proach 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 Ra-tios measures are found to generally provide the best per-formance. Categories and Subject Descriptors I.2.8 [Problem solving, control methods and search]: Heuristic method