Improved Artificial Bee Colony Coot Algorithm Based on Cosine Mutation and Its Application
Aiming at the problems of low solution accuracy,slow convergence and local optimality in COOT algorithm,we propose an im-proved artificial bee colony white-bone top chicken algorithm(ICOOT)based on cosine mutation.Firstly,the elite opposition-based learning strategy is used to initialize the individual position and increase the diversity of the initial individual search.Secondly,considering the powerful search ability of the artificial bee colony algorithm,an improved artificial bee colony search strategy guided by the global optimal value is proposed to update the positions of the white-boned top hen individuals to improve the search capability and convergence accuracy of the COOT.Finally,the sinus variation strategy is introduced to perturb the optimal individual,which on the one hand makes the algorithm jump out of the local optimal effectively,and on the other hand improves the quality of the optimal individual.Twelve benchmark test functions are used to test the optimization performance of the ICOOT.The ICOOT is applied to the problem of tension/pressure spring optimization engineering design,and is compared and analyzed with other meta-heuristic algorithms,which verifies the feasibility and superiority of the improved algorithm.
COOTelite opposition-based learningartificial bee colony algorithmcosine mutation strategyengineering design problem
万方数据
维普
