A Global-Best Guided Human Mental Search Algorithm with Random Clustering Strategy
Seyed Jalaleddin Mousavirad, Gerald Schaefer, Iakov Korovin · 2019
Human mental search (HMS) is a recent population-based metaheuristic inspired by the exploration manner in the bid space of online auctions. It has three main operators: (1) mental search which explores the vicinity of each candidate solution based on Levy flight, (2) grouping which is performed using a clustering algorithm to find a promising area, and (3) moving towards the promising area. HMS has shown competitive performance in solving various optimisation problems.In this paper, an improved HMS algorithm, Global-Best Human Mental Search with Random Clustering Strategy (GHMS-RCS) is proposed as a variant of HMS for global optimisation. GHMS-RCS benefits from the information of global best solutions to improve the exploitation of the HMS algorithm. Also, to reduce the time complexity and enhance exploration and exploitation, a new strategy named random clustering is introduced to improve the grouping operator in HMS. Experimental results show that GHMS-RCS outperforms standard HMS as well as other population-based algorithms including particle swarm optimisation (PSO), shuffled frog-leaping algorithm (SFLA), and biogeography-based optimisation (BBO).