The conventional sine cosine algorithm (SCA) does not appropriately balance exploration and exploitation, causing premature convergence, especially for complex optimization problems, such as the complex shifted or shifted rotated problems. To address this issue, this paper proposes an enhanced brain storm SCA (EBS-SCA), where an EBS strategy is employed to improve the population diversity, and by combining it with two different update equations, two new individual update strategies [individual update strategies (IUS): IUS-I and IUS-II] are developed to make effective balance between exploration and exploitation during the entire iterative search process. Double sets of benchmark suites involving 46 popular functions and two real-world problems are employed to compare the EBS-SCA with other metaheuristic algorithms. The experimental results validate that the proposed EBS-SCA achieves the overall best performance including the global search ability, convergence speed, and scalability.

brain storm optimization (BSO), global optimization, metaheuristic algorithm, Sine cosine algorithm (SCA)
IEEE Access
Department of Systems and Computer Engineering

Li, C. (Chunquan), Luo, Z. (Zu), Song, Z. (Zhenshou), Yang, F. (Feng), Fan, J. (Jinghui), & Liu, P. (2019). An enhanced brain storm sine cosine algorithm for global optimization problems. IEEE Access, 7, 28211–28229. doi:10.1109/ACCESS.2019.2900486