An improved sine cosine algorithm with heterogeneous subpopulations for global optimization and fractional order PID controller design.

As one of recently proposed optimization algorithm, sine cosine algorithm (SCA) suffers from the problems of skipping over true solutions, stagnating in local optima and premature convergence. Therefore, an improved sine cosine algorithm with heterogeneous subpopulations (HSISCA) is developed in this paper. The population of HSISCA is divided into two subpopulations which comply with different search mechanisms and information sharing rules. One subpopulation is specified to enhance exploration by incorporating Levy flight and random search guidance into SCA, while the other is specified to enhance exploitation by adaptive differential mutation operator. The exploration subpopulation is denied access to the information of exploitation subpopulation, but the exploitation subpopulation is allowed access to the information of exploration subpopulation. Moreover, the greedy selection is applied to each individual to preserve useful information. HSISCA is tested on the CEC2014 and CEC2017 benchmark functions, and is used for designing fractional order PID (FOPID) controller. The results confirm the better performance of HSISCA compared to other competitive algorithms, and demonstrate the effectiveness of HSISCA in designing FOPID controller for complex systems. [ABSTRACT FROM AUTHOR]