A new global sine cosine algorithm for solving economic emission dispatch problem

Abstract

To explore a better method to solve the economic emission dispatch (EED) problem and enhance the optimization performance of the sine cosine algorithm (SCA), we propose a novel, efficient update of the sine cosine algorithm (ECDSCA) with three improvement strategies. First, in the phase of particle position updates, an enhanced elite leadership strategy is introduced to effectively adjust the global search and local exploitation capabilities of SCA. Second, a strategy combining crossover and optimal selections is designed to prevent the algorithm from falling into local extremes. Third, a dimension-by-dimension variation strategy is adopted to enrich the population diversity and improve SCA’s optimization accuracy. Theoretical analysis demonstrates that ECDSCA has the same time complexity as SCA. The probability measure method is utilized to prove that ECDSCA is a global convergence algorithm. To evaluate the optimization ability of ECDSCA, it is compared with six representative algorithms on the IEEE CEC2017 test suite. The test results reveal that the optimization ability, convergence rate, and robustness of ECDSCA are improved significantly. Finally, ECDSCA is used to solve the EED problem. The test is carried out on two cases and compared with several algorithms. The comparison results show that ECDSCA significantly outperforms the other comparison algorithms.