新型元启发式算法:碰撞算法及其改进算法OA

[Purposes]To enhance the performance of metaheuristic algorithms in solving complex engineering optimization problems,this study,based on the law of conservation of momentum,proposed a novel metaheuristic optimization algorithm:the collision algorithm(CA).To address its shortcomings in local search capabilities and population diversity degradation,the algorithm was improved with multiple strategies,and then a multi-strategy improved collision algorithm(AMICA)was developed.[Methods]First,the CA framework was established,and its performance was validated.Next,CA was refined through strategies such as an elite pool strategy,Circle chaotic mapping initialization,t-distribution mutation strategy,adaptive spiral search strategy,and a nonlinear convergence factor based on the cosine function,leading to the development of AMICA.Finally,the performance of AMICA was tested using the IEEE CEC2017 benchmark functions.[Results]A comprehensive evaluation based on the IEEE CEC2017 test function demonstrates that AMICA exhibits significant advantages in unimodal,multimodal,hybrid,and composite functions.Its convergence speed,accuracy,and robustness are notably superior to CA and other comparative algorithms.The Wilcoxon rank sum test and Friedman test further validate AMICA􀆳s global search capabilities and stability,showing significant superiority across all test functions and ranking it as the top performer in overall performance.[Conclusion]The unique exploratory approach of the novel metaheuristic CA provides new insights for the development of metaheuristic algorithms.Through multi-strategy collaborative improvements,the algorithm􀆳s performance is significantly improved.Compared to existing advanced algorithms,AMICA demonstrates superior overall performance in solving complex optimization problems.