A hybrid ant colony optimization algorithm for solving facility layout problems formulated as quadratic assignment problems

Abstract

Purpose – This paper aims to describe a new hybrid ant colony optimization (ACO) algorithm developed to solve facility layout problems (FLPs) formulated as quadratic assignment problems (QAPs). Design/methodology/approach – A hybrid ACO algorithm which combines max‐min ant system (MMAS) (i.e. a variant of ACO) with genetic algorithm (GA) has been developed. The hybrid algorithm is further improved with the use of a novel minimum pheromone threshold strategy (MPTS). Findings – The hybrid algorithm shows satisfactory results in the experimental evaluation due to the synergy and collaboration between MMAS and GA. The results also show that the use of MPTS helps them to achieve such performance, by promoting search diversification. Research limitations/implications – The experimental evaluation presented emphasizes more on the search performance or pattern of the hybrid algorithm. Detailed computational work could reveal other strengths of the algorithm. Practical implications – The developmental work presented in this paper could be used by researchers and practitioners to solve QAPs. Its use may also be expanded to solve other combinatorial optimization and engineering problems. Originality/value – This paper provides useful insights into the development of a hybrid ACO algorithm that combines MMAS with GA for solving QAPs.