A Novel Multi-Objective Discrete Particle Swarm Optimization with Elitist Perturbation for Reconfiguration of Ship Power System

Abstract

A novel multi-objective discrete particle swarm optimization with elitist perturbation strategy (EPSMODPSO) is proposed and applied to solve the reconfiguration problem of shipboard power system(SPS). The new algorithm uses the velocity to decide each particle to move one step toward positive or negative direction to update the position. An elitist perturbation strategy is proposed to improve the local search ability of the algorithm. Reconfiguration model of SPS is established with multiple objectives, and an inherent homogeneity index is adopted as the auxiliary estimating index. Test results of examples show that the proposed EPSMODPSO performs excellent in terms of diversityand convergence of the obtained Pareto optimal front. It is competent to solve network reconfiguration of shipboard power system and other multi-objective discrete optimization problems.

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“…Many academics are devoted to developing improved technology and artificial intelligence because of the exceptional benefits that may be possible [5][6][7][8][9][10]. Fuzzy [5], Hybrid Fuzzy [6], Fuzzy Adaptive [7], Neural Network [8], Genetic Algorithm (GA) [9], and Particle Swarm Optimisation (PSO) [10] are a few of the recommended advanced control approaches that have demonstrated their efficacy and stability. In addition, there has been a lack of investigation into utilising existing control theory to improve the performance and efficacy of controllers, particularly complex systems or systems controlled through networks.…”