A multi-strategy enhanced sine cosine algorithm for global optimization and constrained practical engineering problems
Huiling Chen,
Mingjing Wang and
Xuehua Zhao
Applied Mathematics and Computation, 2020, vol. 369, issue C
Abstract:
The Sine Cosine Algorithm (SCA) has received much attention from engineering and scientific fields since it was proposed. Nevertheless, when solving multimodal or complex high dimensional optimization tasks, the conventional SCA still has a high probability of falling into the local optimal stagnation or failing to obtain the global optimum solution. Additionally, it performspoorly in convergence. Therefore, in this study, a multi-strategy enhanced SCA, a memetic algorithm termed MSCA, is proposed, which combines multiple control mechanisms including Cauchy mutation operator, chaotic local search mechanism, opposition-based learning strategy and two operators based on differential evolution to achieve a better balance between exploration and exploitation. To verify its performance, MSCA was compared with 11 state-of-the-art original optimizers and variant algorithms on 23 continuous benchmark tasks including 7 unimodal tasks, 6 multimodal tasks, 10 various fixed-dimension multimodal functions, and several typical CEC2014 benchmark problems. Furthermore, MSCA was utilized to solve three constrained practical engineering problems including tension/compression spring design, welded beam design, and pressure vessel design. The experimental results demonstrate that the proposed algorithm MSCA is superior to other competitors in terms of quality of solutions and convergence speed and can serve as an effective andefficient computer-aided tool for practical tasks with complex search space.
Keywords: Memetic sine cosine algorithm; Cauchy mutation operator; Chaotic local search; Opposition-based learning; Differential evolution; Constrained mathematical modeling (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (10)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0096300319308641
Full text for ScienceDirect subscribers only
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:369:y:2020:i:c:s0096300319308641
DOI: 10.1016/j.amc.2019.124872
Access Statistics for this article
Applied Mathematics and Computation is currently edited by Theodore Simos
More articles in Applied Mathematics and Computation from Elsevier
Bibliographic data for series maintained by Catherine Liu ().