A novel particle swarm optimization with fuzzy adaptive inertia weight for reliability redundancy allocation problems

Abstract

Particle swarm optimization (PSO) is a stochastic population based algorithm inspired by the social learning of fishes or birds. It is a swarm intelligence technique for optimization process. The parameters of PSO take an important role in the performance of PSO algorithm. Inertia weight is an important parameter of this algorithm which affects the convergence and exploration-exploitation trade-off in PSO. Since the introduction of this parameter, various developments for determining the value of inertia weight was proposed. In this paper a novel fuzzy adaptive particle swarm optimization (FAPSO) algorithm has been proposed considering the inertia weight as a triangular fuzzy number (TFN) which changes in every iteration. The algorithm outperforms the standard PSO as well as the previous adaptive approaches. Four reliability redundancy benchmarks are considered to display the performability of the proposed PSO that develops the strengths of PSO to enable optimizing the RRAP which belongs to mixed integer non-linear programming problem. Finally a statistical analysis has been done which indicates that the proposed FAPSO performs better than the algorithms existing in literature.