The novel combination lock algorithm for improving the performance of metaheuristic optimizers. (October 2022)

• Record Type: Journal Article
• Authors: Bahreininejad, Ardeshir; Taib, Hasnanizan
• Is Part Of: Advances in engineering software. Volume 172(2022)
• Publication Date: 2022-10
• Subjects: Population-based algorithms -- Optimization -- Combination lock algorithm -- Metaheuristic -- Constrained -- Unconstrained; Computer-aided engineering -- Periodicals; Engineering -- Computer programs -- Periodicals; Engineering -- Software -- Periodicals; Periodicals; 620.0028553
• DOI: 10.1016/j.advengsoft.2022.103177
• Languages: English
• ISSNs: 0965-9978

Abstract

Highlights: A novel method to improve the performance of population-based optimizers. The combination lock algorithm used as a pre-processing optimization method. The combination lock algorithm produces an elite individual. The elite individual is inserted into the initial population of optimizers. The new algorithm significantly improves the performance of optimizers. Abstract: Nature-inspired population-based metaheuristics are promising search methods for solving optimization problems. In this paper, a novel systematic pre-processing approach, called the combination lock algorithm, for obtaining a good starting point for population-based algorithms is proposed. The proposed algorithm is tested on 32 benchmark unconstrained multidimensional optimization problems of different characteristics that are either unimodal or multimodal, continuous or non-continuous, separable or non-separable, differentiable or non-differentiable. The tests also include four engineering constrained optimization benchmark problems. The experimental results of applying the proposed algorithm for the Particle Swarm Optimization, the Ant Colony Optimization for Continuous Domain, and the Grey Wolf Optimization were compared with the results obtained from the conventional approach of initializing the starting population of population-based metaheuristic methods. The simulation results show the potential of the proposed algorithm as an efficient and reliable approach to enhance the performance of population-based optimization algorithms such that, overall, 50% and up to 100% of the tested problems "across various population size" settings, had either improved or equalled the optimal values when the proposed algorithm was applied.