Heterogeneous Redundancy Allocation for Series-Parallel Multi-State Systems Using Hybrid Particle Swarm Optimization and Local Search

Abstract

A hybrid algorithm of particle swarm optimization (PSO) and local search (LS) is proposed to solve the redundancy allocation problem for series-parallel multi-state systems. The proposed hybrid algorithm is able to design the system structure with a minimum cost to provide a desired level of availability. Unlike most of the previous studies that only consider homogeneous redundancy, the proposed algorithm allows for the heterogeneous redundancy technique. The universal generating function method is applied to evaluate system availability. The standard PSO is modified and novel LS strategies are integrated to adapt to the redundancy allocation problem. Case studies that facilitate comparisons between the proposed hybrid algorithm and other non-hybrid heuristics as well as meta-heuristics reported in the literature (such as genetic algorithms, tabu search, and ant colony optimization) are provided. The results illustrate the advantages of the proposed hybrid algorithm in terms of the solution quality or algorithm efficiency.