Ensembles strategies for backtracking search algorithm with application to engineering design optimization problems

This paper proposes two ensemble strategies for the backtracking search algorithm (BSA). The first one is an ensemble of two sets of evolutionary operators that balances exploration and exploitation abilities. The second one is an ensemble of values for each parameter associated with the evolutionary operators. The second strategy provides diverse search moves with various search step lengths that are essential for searching different search landscapes. In addition to the ensemble strategies, another strategy is used to reinitialize specific individuals of the population to escape from local optima. Sixteen variants of the BSA are built based on different combinations of these strategies or their modified versions. The best variant for solving 29 problems of CEC2017 test suite is statistically compared with nineteen state-of-the-art algorithms. The results confirm its superiority to all the considered algorithms. Remarkably, according to the Wilcoxon rank-sum test with a significance level of 0.05, it is better than others for solving at least 20 and 18 functions with 30 and 50 dimensions, respectively. Furthermore, it is applied to five engineering design optimization problems. Its solutions are at least as well as or better than those obtained by the best existing algorithms in the literature for three problems. (c) 2022 Elsevier B.V. All rights reserved.

Automated summary

This paper proposes two ensemble strategies for the backtracking search algorithm (BSA), one to balance exploration and exploitation abilities and the other to provide diverse search moves with various search step lengths. In addition, a strategy to reinitialize specific individuals of the population is used. The proposed algorithm outperforms nineteen state-of-the-art algorithms in solving 29 problems and achieves at least comparable or better results than the best existing algorithms for engineering design optimization problems.