On Improvements of the Human Mental Search Algorithm for Global Optimisation
Seyed Jalaleddin Mousavirad, Gerald Schaefer, Leila Esmaeili, Iakov Korovin · 2020
Population-based metaheuristic algorithms are problem-independent approaches to solve global optimisation problems. The human mental search (HMS) algorithm is a powerful population-based metaheuristic algorithm that has been shown to yield competitive performance for a variety of optimisation problems. HMS comprises three main operators, mental search, grouping, and movement. Mental search explores the neighbourhood of candidate solutions based on a Levy flight distribution to allow for simultaneous exploration and exploitation. Grouping is used to cluster the current population in order to find a promising area in search space, while during movement, candidate solutions move towards the identified promising area. In this paper, we propose an improved HMS algorithm-HMS-IS-OSK - that introduces an adaptive selection of the number of mental processes to improve the exploitation ability of HMS, and a one-step k-means algorithm for grouping to decrease the computational complexity. To evaluate the proposed algorithm, we perform a set of experiments on the CEC 2017 bench-mark functions with dimensionalities of 30, 50, and 100. The obtained results show that HMS-IS-OSK outperforms standard HMS as well as other population-based metaheuristic algorithms including covariance matrix adaptation evolution strategy (CMAES), particle swarm optimisation (PSO), artificial bee colony algorithm (ABC), whale optimisation algorithm (WOA), grey wolf optimiser (GWO), and moth-flame optimisation (MFO).