关键词: 多目标布谷鸟算法, 多资源均衡优化, 非均匀变异算子, 差分进化算子, 全局收敛性

Abstract: An improved multi-objective Cuckoo Search Algorithm (CSA) was proposed to overcome basic multi-objective CSA's default of low convergence speed in the later period and low solution quality when it was used to solve the multi-resource leveling problem. Firstly, a non-uniform mutation operator was embedded in the basic multi-objective cuckoo search to make a perfect balance between exploration and exploitation. Secondly, a differential evolution operator was employed for boosting cooperation and information exchange among the groups to enhance the convergence quality. The simulation test illustrates that the improved multi-objective CSA outperforms the basic multi-objective CSA and Vector Evaluated Particle Swarm Optimization Based on Pareto (VEPSO-BP) algorithm when global convergence is considered.

Key words: multi-objective Cuckoo Search Algorithm (CSA), multi-resouce leveling optimization, non-uniform mutation operator, differential evolution operator, global convergence