Strength Pareto Particle Swarm Optimization and Hybrid EA-PSO for Multi-Objective Optimization

Abstract

This paper proposes an efficient particle swarm optimization (PSO) technique that can handle multi-objective optimization problems. It is based on the strength Pareto approach originally used in evolutionary algorithms (EA). The proposed modified particle swarm algorithm is used to build three hybrid EA-PSO algorithms to solve different multi-objective optimization problems. This algorithm and its hybrid forms are tested using seven benchmarks from the literature and the results are compared to the strength Pareto evolutionary algorithm (SPEA2) and a competitive multi-objective PSO using several metrics. The proposed algorithm shows a slower convergence, compared to the other algorithms, but requires less CPU time. Combining PSO and evolutionary algorithms leads to superior hybrid algorithms that outperform SPEA2, the competitive multi-objective PSO (MO-PSO), and the proposed strength Pareto PSO based on different metrics.

A hybrid elitist pareto-based coordinate exchange algorithm for constructing multi-criteria optimal experimental designs

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A hybrid multi-objective PSO–EDA algorithm for reservoir flood control operation

“…Wang et al [32] developed a preference order to rank all the particles and thus to identified the global best particle. Elhossini et al [33] combined PSO with evolutionary algorithm and selected the global best particle from the external archive by a tournament selection. The personal best particle is selected as the one with lowest strength Pareto fitness.…”

An Experimental Investigation of Variation Operators in Reference-Point Based Many-Objective Optimization

“…Tan et al [27] presented an adaptive variation operator that exploits the chromosomal structure of binary representation and synergizes the function of crossover and mutation. Elhossini et al [12] suggested a hybridization of particle swarm optimization (PSO) and genetic operators to handle MOPs, where genetic operators are modified to preserve the information used by PSO. Some recent similar works can be referred in [14,23,19,25].…”