Logistic-Gauss Circle optimizer: Theory and applications

Abstract

Chaotic maps can be used to make the distribution of the initial population more uniform, which improves the spatial exploration rate. Considering these advantages, this paper attempts to design search operations based on chaotic maps and develop a novel metaheuristic algorithm called the Logistic-Gauss Circle optimizer. The algorithm reasonably combines and reformulates the Logistic and Gauss maps into Logistic-Gauss search (exploration); reformulates the Circle map into Circle search (exploitation). Through these two operations, the proposed algorithm achieves global optimization. The performance of the proposed algorithm is validated by a comparative analysis with 5 high-quality metaheuristic algorithms on 10 benchmark functions. The results of statistical analyses, including the Wilcoxon signed-rank test and the Friedman test, indicate that the proposed algorithm outperforms its competitors. Furthermore, the strong competitiveness of the algorithm is verified through comparisons with 4 state-of-the-art algorithms. Finally, the proposed algorithm is applied to 5 real-world problems, thereby demonstrating its capability to address engineering optimization problems.