Improved selection strategy for multi-objective evolutionary algorithms with application to water distribution optimization problems

Abstract

Multi-objective evolutionary algorithms (MOEAs) have been applied to water distribution system (WDS) optimization problems for over two decades. The selection strategy is a key component of an MOEA that determines the composition of a population, and thereby the evolutionary search process, which imitates natural selection by granting fitter individuals an increasing opportunity to reproduce. This paper proposes the convex hull contribution (CHC) selection strategy for generational MOEAs (CHCGen) as a novel selection strategy that is based on the CHC of solutions to the Pareto front in the objective space. Numerical experiments using a general MOEA framework demonstrate that the CHCGen selection strategy is able to outperform existing popular selection strategies (e.g., crowding distance, hypervolume contribution, and hybrid replacement selection). Moreover, it is illustrated that the CHCGen selection strategy is able to improve the performance of existing MOEAs such as NSGA-II and GALAXY. The conclusions are based on the results of six bi-objective WDS problems.