An exploitation-boosted sine cosine algorithm for global optimization. (January 2023) - Li, Changlun; Liang, Ke; Chen, Yuan; Pan, Mingzhang
Abstract: The sine cosine algorithm (SCA) has drawn significant attention from researchers in different fields because of fewer control parameters, excellent global optimization ability, and simple principles. However, there are some drawbacks of the SCA which needed immediate attention. The position-updated equation of the SCA is good at exploration but poor at exploitation, which leads to slow converging speed and low converging accuracy in some complex cases. Therefore, an exploitation-boosted sine cosine algorithm (namely, EBSCA) is proposed in this study. In order to enhance the ability of exploitation, a new position-updated equation is designed by emphasizing the position information of the best individual, thereby guiding the updating of new candidate individuals. Meanwhile, the information weights of the best individual and the current individual in the new equation are dynamically adjusted by a balance factor b to avoid over-exploitation of the information from the best individual. Furthermore, a new integration way of combining the quantization orthogonal crossover strategy with the SCA is proposed to improve the utilizing efficiency of the searching space. The performance of the EBSCA is evaluated by being tested on 13 classical benchmark functions, IEEE CEC 2015 problems, and four well-known engineering applications. The comparisons demonstrate that the EBSCA algorithm obviously improves the performance of the SCA. Additionally, the experimental results show that, the proposed EBSCA algorithm exhibits higher competitiveness compared to other algorithms participated in this research.