Advanced orthogonal learning-driven multi-swarm sine cosine optimization: Framework and case studies

Abstract

Sine cosine algorithm (SCA) is a widely used nature-inspired algorithm that is simple in structure and involves only a few parameters. For some complex tasks, especially high-dimensional problems and multimodal problems, the basic method may have problems in harmonic convergence or trapped into local optima. To efficiently alleviate this deficiency, an improved variant of basic SCA is proposed in this paper. The orthogonal learning, multi-swarm, and greedy selection mechanisms are utilized to improve the global exploration and local exploitation powers of SCA. In preference, the orthogonal learning procedure is introduced into the conventional method to expand its neighborhood searching capabilities. Next, the multi-swarm scheme with three sub-strategies is adopted to enhance the global exploration capabilities of the algorithm. Also, a greedy selection strategy is applied to the conventional approach to improve the qualities of the search agents. Based on these three strategies, we called the improved SCA as OMGSCA. The proposed OMGSCA is compared with a comprehensive set of meta-heuristic algorithms including six other improved SCA variants, basic version, and ten advanced meta-heuristic algorithms. We employed thirty IEEE CEC2014 benchmark functions, and eight advanced meta-heuristic algorithms on seventeen real-world benchmark problems from IEEE CEC2011. Also, non-parametric statistical Wilcoxon sign rank and the Friedman tests are performed to monitor the performance of the proposed method. The obtained experimental results demonstrate that the introduced strategies can significantly improve the exploratory and exploitative inclinations of the basic algorithm. The convergence speed of the original method has also been improved, substantially. The results suggest the proposed OMGSCA can be used as an effective and efficient auxiliary tool for solving complex optimization problems.