Dynamic balance sine cosine algorithm combining learning difference with Lévy flight
In order to improve the convergence performance of sine cosine algorithm,this paper proposes an improved dynamic balance sine cosine algorithm that integrates learning difference and Lévy flight,which is defined as SCALLD algorithm.By introducing the learning difference strategy,the search individu'l's depen-dence on its location information is reduced and the global exploration ability is enhanced.Adding Lévy flight mechanism to enrich population diversity and improve exploration ability;Adopting the dynamic balance strat-egy to balance exploration and exploitation capabilities,to improve convergence speed and stability.Experi-ments on the CEC2022 benchmark functions show that SCALLD demonstrates superior convergence perfor-mance and stability compared to the six comparison algorithms.Wilcoxon rank sum test further proves SCALLD's competitive advantage and provides a reference for solving complex optimization problems.
sine cosine algorithmintelligent optimization algorithmlearning difference strategyLévy flightdynamic balance