PSA Based Multi Objective Evolutionary Algorithms

Abstract

It has generally been acknowledged that both proximity to the Pareto front and a certain diversity along the front, should be targeted when using evolutionary multiobjective optimization. Recently, a new partitioning mechanism, the Part and Select Algorithm (PSA), has been introduced. It was shown that this partitioning allows for the selection of a well-diversified set out of an arbitrary given set, while maintaining low computational cost. When embedded into an evolutionary search (NSGA-II), the PSA has significantly enhanced the exploitation of diversity. In this paper, the ability of the PSA to enhance evolutionary multiobjective algorithms (EMOAs) is further investigated. Two research directions are explored here. The first one deals with the integration of the PSA within an EMOA with a novel strategy. Contrary to most EMOAs, that give a higher priority to proximity over diversity, this new strategy promotes the balance between the two. The suggested algorithm allows some dominated solutions to survive, if they contribute to diversity. It is shown that such an approach substantially reduces the risk of the algorithm to fail in finding the Pareto front. The second research direction explores the use of the PSA as an archiving selection mechanism, to improve the averaged Hausdorff distance obtained by existing EMOAs. It is shown that the integration of the PSA into NSGA-II-I and ∆ p -EMOA as an archiving mechanism leads to algorithms that are superior to base EMOAS on problems with disconnected Pareto fronts.

Component-based design of multi-objective evolutionary algorithms using the Tigon optimization library

“…Two variants of NSGA-II that exist in the Tigon optimization library are NSGA-II-PSA [19] and NSGA-III [6]. In the former, NSGA-II Crowding and NSGA-II Elite Selection are replaced by two equivalent operators that use a clustering partition-based selection algorithm as opposed to the crowding distance.…”

Two tuned multi-objective meta-heuristic algorithms for solving a fuzzy multi-state redundancy allocation problem under discount strategies

“…Moreover, since the developed model of the problem is shown to be strongly NP-hard, a multi-objective metaheuristic algorithm of controlled elitism non-dominated ranked genetic algorithm (CE-NRGA) is developed to find Pareto solutions of the problem. The multi-objective meta-heuristics have received growing attention in recent years; the most utilized being NSGA II [30][31][32][33], MOPSO [34][35][36], MOGA [37,38]. The non-dominated ranked genetic algorithm (NRGA) proposed by Al Jadaan et al [39] has also been shown to be an efficient multi-objective algorithm, where it was employed to solve different optimization problems in project scheduling [40], facility layout [41], and flexible job shop [42].…”