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。
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.
出处
《计算机应用》
CSCD
北大核心
2014年第1期189-193,共5页
journal of Computer Applications
基金
国家自然科学基金资助项目(71271138)
教育部人文社会科学规划基金资助项目(10YJA630187)
上海市教育委员会科研创新项目(12ZS133)
上海市一流学科建设项目(S1201YLXK)
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
multi-objective Cuckoo Search Algorithm （CSA）
muhi-resouce leveling optimization
non-uniform mutation operator
differential evolution operator
global convergence
