A MOPSO Algorithm Based Exclusively on Pareto Dominance Concepts

Abstract

In extending the Particle Swarm Optimisation methodology to multi-objective problems it is unclear how global guides for particles should be selected. Previous work has relied on metric information in objective space, although this is at variance with the notion of dominance which is used to assess the quality of solutions. Here we propose methods based exclusively on dominance for selecting guides from a nondominated archive. The methods are evaluated on standard test problems and we find that probabilistic selection favouring archival particles that dominate few particles provides good convergence towards and coverage of the Pareto front. We demonstrate that the scheme is robust to changes in objective scaling. We propose and evaluate methods for confining particles to the feasible region, and find that allowing particles to explore regions close to the constraint boundaries is important to ensure convergence to the Pareto front.

An Analysis of Archive Update for Vector Evaluated Particle Swarm Optimization

“…On the other hand, Fig. 2(b) shows that the archive is updated during iterations [22][23][24][25][26][27]. Hence, this algorithm is denoted as "During Iterations" (DuI).…”

“…The DTLZ, which is abbreviated from Deb, Thiele, Laumanns, and Zitzler, consists of seven MOO test problems in order to extensively evaluate different features of MOO problems. A disadvantage of ZDT and DTLZ is that both test problems are separable and degenerate [23]. Hence, another test problem called WFG has been proposed by Huband et al [18].…”

Inverse Analysis in Civil Engineering: Applications to Identification of Parameters and Design of Structural Material Using Mono or Multi-Objective Particle Swarm Optimization

“…In this work we have applied the MOPSO method described in [Alvarez-Benitez et al, 2005]. In this method only the fully connected topology is used to calculate the position of each particle for each objective function and then the Pareto dominance test is applied to each particle regarding the particle's positions stored in the extended memory.…”

Particle Swarm Optimization with Velocity Adaptation

“…There exist a lot of strategies to handle boundary constraints in the literature, e.g., [3], [1], [14], [2], [7], among them:…”

“…One question is how to deal with particles that reach the infeasible region. Various methodologies were proposed in the literature, e.g., [3], [1]. Such methods often handle infeasible particles by guiding them towards the feasible region of the search space.…”