Chaotic oppositional sine-cosine method for solving global optimization problems

This study proposed an improved sine-cosine algorithm (SCA) for global optimization tasks. The SCA is a meta-heuristic method ground on sine and cosine functions. It has found its application in many fields. However, SCA still has some shortcomings such as weak global search ability and low solution quality. In this study, the chaotic local search strategy and the opposition-based learning strategy are utilized to strengthen the exploration and exploitation capability of the basic SCA, and the improved algorithm is called chaotic oppositional SCA (COSCA). The COSCA was validated on a comprehensive set of 22 benchmark functions from classical 23 functions and CEC2014. Simulation experiments suggest that COSCA's global optimization ability is significantly improved and superior to other algorithms. Moreover, COSCA is evaluated on three complex engineering problems with constraints. Experimental results show that COSCA can solve such problems more effectively than different algorithms.

Automated summary

This study proposes an improved sine-cosine algorithm (SCA) for global optimization tasks, called chaotic oppositional SCA (COSCA), which strengthens the exploration and exploitation capability of the basic SCA by utilizing chaotic local search strategy and opposition-based learning strategy. Simulation experiments show that COSCA significantly improves the global optimization ability and outperforms other algorithms, and it also effectively solves complex engineering problems with constraints.