欢迎您！东篱公司
退出
-
申报数据库
-
立项数据库
-
成果数据库
-
项目获奖数据库
中文摘要：
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。
英文摘要：
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.
同期刊论文项目
同项目期刊论文
期刊信息
位置：