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

Abstract

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.