Spiral Gaussian mutation sine cosine algorithm: Framework and comprehensive performance optimization. (15th December 2022)

• Record Type: Journal Article
• Authors: Zhou, Wei; Wang, Pengjun; Heidari, Ali Asghar; Zhao, Xuehua; Chen, Huiling
• Is Part Of: Expert systems with applications. Volume 209(2022)
• Publication Date: 2022-12-15
• Subjects: Sine cosine algorithm -- Spiral motion -- Gaussian mutation -- Global optimization -- Engineering application; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2022.118372
• Languages: English
• ISSNs: 0957-4174

Abstract

Abstract: Sine Cosine Algorithm (SCA), as a recently viral population-based meta-heuristic, which is in the extensive application for a variety of optimization cases. Regardless of the concerns on its novelty, SCA updates the population based on a simple updating rule with a basic structure and few parameters. However, it is considered that SCA remains the weakness of low diversity, slow convergence speed, stagnation in local optimum, and low accuracy of solutions. In that case, an attempt is made to design a new SCA version to overcome these shortcomings, namely FGSCA, combined with two strategies, including spiral motion and Gaussian mutation. Spiral motion is inspired by Moth-flame Optimization (MFO), which is employed to strengthen the capacity of exploitation based on the original SCA. The Gaussian mutation is adopted to increase the population's diversity generated by SCA and strengthen local exploration capability. The principle is to update the population generated by SCA, respectively, and then choose the best from these two new populations by greedy selection. Combining the two strategies effectively strengthens the original SCA's performance and maintains a proper exploitation and exploration balance. To examine the performance of FGSCA, it is used for making comparisons with eight well-known meta-heuristic algorithms (MAs), six reported SCA variants, and ten improved MAs on 23 well-known benchmark test problems and 30 standard IEEE CEC2014 benchmark test problems. Results indicate that the overall performance of FGSCA is superior to twenty-four comparative MAs on 53 benchmark test problems. Besides, FGSCA is employed to resolve three practical engineering problems. Results exhibit that FGSCA harvests the best results among twenty-four comparative MAs. Thus, FGSCA is expected to be a mighty and efficient tool for dealing with various complex optimization problems.