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

摘要： 针对标准多目标布谷鸟算法(CSA)后期收敛速度慢、收敛精度不高的缺陷,提出一种求解多资源均衡优化问题的改进多目标布谷鸟算法。首先,引入非均匀变异算子,以均衡算法的全局搜索能力和局部寻优能力;然后,引进差分进化算子,促进群体间的合作和信息交流,提高算法的收敛精度。通过算例测试表明,改进的多目标布谷鸟算法比标准多目标算法和VEPSO-BP算法具有更好的全局收敛性。

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