Evolving chimp optimization algorithm by weighted opposition-based technique and greedy search for multimodal engineering problems

This paper presents an evolved chimp optimization algorithm (ChOA) that uses greedy search (GS) and opposition-based learning (OBL) to respectively increase the ChOA's capabilities for exploration and exploitation in addressing real practical engineering-constrained problems. In order to investigate the efficiency of the GSOBL-ChOA, its performance is evaluated by twenty-three standard benchmark functions, 10 benchmark functions from CEC06-2019, a randomly generated landscape, and 12 real practical Constrained Optimization Problems (COPs-2020) from a wide variety of engineering fields, including power system design, synthesis and process design, industrial chemical producer, power -electronic design, mechanical design, and animal feed ratio. The findings are compared to those obtained using benchmark optimizers such as CMA-ES and SHADE as state-of-the-art optimization techniques and CEC competition winners; standard ChOA; OBL-GWO, OBL-SSA, and OBL-CSA as the best benchmark OBL-based algorithms. In order to perform a comprehensive assessment, three non-parametric statistical tests, including the Wilcoxon rank-sum, Bonferroni-Dunn and Holm, and Friedman average rank tests, are utilized. The top two algorithms are GSOBL-ChOA and CMA-ES, with scores of forty and eleven, respectively, among 27 mathematical functions. jDE100 obtained the highest score of 100 in the 100-digit challenge, followed closely by DISHchain1e+12, which achieved the highest possible score of 97, and GSOBL-ChOA obtained the fourth-highest score of 93. Finally, GSOBL-ChOA and CMA-ES outperform other benchmarks in five and four real practical COPs, respectively. The source code of the paper can be downloaded using the following link: https://se.mathworks.com/ matlabcentral/fileexchange/119108-evolving-chimp-optimization-algorithm-by-weighted-opposition.(c) 2022 Elsevier B.V. All rights reserved.

Automated summary

This paper presents an evolved chimp optimization algorithm (ChOA) that uses greedy search and opposition-based learning to improve exploration and exploitation capabilities. The algorithm is evaluated on benchmark functions, real practical engineering-constrained problems, and other optimization techniques. The evaluation shows that the GSOBL-ChOA outperforms other benchmarks in several scenarios.