Aiming at the problems of insufficient balance between global search and local exploitation,susceptibility to local optimal solutions, and relatively weak global search capability of optical microscope algorithm(OMA),...Aiming at the problems of insufficient balance between global search and local exploitation,susceptibility to local optimal solutions, and relatively weak global search capability of optical microscope algorithm(OMA), an improved optical microscope algorithm(IOMA) was proposed in this paper. IOMA incorporates T-distribution perturbation strategy,chaotic mapping initialization,density factor and diversity factor,and last place elimination mechanism,which is designed to enhance the algorithm's global exploration and local exploitation capabilities,expanding the search range,improving the global search effectiveness,and effectively reducing the risk of falling into local optimal solutions. The performance validation on 15 benchmark test functions and comparative analysis with other metaheuristic algorithms demonstrate the significant advantages of IOMA. Further,IOMA is applied to solve two real-world engineering design problems,and the experimental results confirm its efficiency and practicality in solving complex optimization problems.展开更多
基金supported by the National Key Research and Development Program of China (2022ZD0119000)
文摘Aiming at the problems of insufficient balance between global search and local exploitation,susceptibility to local optimal solutions, and relatively weak global search capability of optical microscope algorithm(OMA), an improved optical microscope algorithm(IOMA) was proposed in this paper. IOMA incorporates T-distribution perturbation strategy,chaotic mapping initialization,density factor and diversity factor,and last place elimination mechanism,which is designed to enhance the algorithm's global exploration and local exploitation capabilities,expanding the search range,improving the global search effectiveness,and effectively reducing the risk of falling into local optimal solutions. The performance validation on 15 benchmark test functions and comparative analysis with other metaheuristic algorithms demonstrate the significant advantages of IOMA. Further,IOMA is applied to solve two real-world engineering design problems,and the experimental results confirm its efficiency and practicality in solving complex optimization problems.
