摘要
为提高已有多目标进化算法在求解高维复杂多目标优化问题上的收敛性和解集分布性,提出一种基于自适应差分进化算法的改进多目标进化算法。在以非支配排序遗传算法为代表的第二代精英多目标进化算法模型基础上,对模型中精英选择策略、拥挤密度估计方法进行改进,并根据多目标的特点提出了新的差分进化算法变异策略和参数自适应控制策略。将该算法与目前性能最好的6种多目标进化算法在标准测试函数集上进行对比实验,结果表明所提算法相对于其他算法具有明显的优势,能够在保证良好收敛性的同时,使获得的Pareto最优解集具有更均匀的分布性和更广的覆盖范围,尤其适合于高维复杂多目标优化问题的求解。
To improve the convergence and distribution of Multi-Objective Evolutionary Algorithms（MOEAs） in dealing with large-dimensional Multi-objective Optimization Problems（MOPs）,a Self-adaptive Differential Evolution Multi-objective Optimization（SDEMO） was proposed.Based on the model of Nondominated Sorting Genetic Algorithm II（NSGA-II）,the elitist selection strategy and the crowding distance calculation in the model of SDEMO were improved to achieve better convergence performance.In addition,new mutation strategy as well as new parameter control strategy of Differential Evolution（DE） algorithm were also presented according to the characteristics of MOPs.SDEMO was compared to 6 state-of-the-art MOEAs on benchmark test problems.Simulation results showed that SDEMO could ensure good convergence while had uniform distribution and wild coverage area for obtained Pareto optimum solution.It had obvious advantages than other algorithms,especially,applied to solving large-dimensional MOPs.
出处
《计算机集成制造系统》
EI
CSCD
北大核心
2011年第12期2660-2665,共6页
Computer Integrated Manufacturing Systems
基金
国家自然科学基金资助项目(61175126)~~
关键词
差分进化
多目标优化
差分变异策略
精英选择策略
拥挤密度估计
differential evolution
multi-objective optimization
differential mutation strategy
elitist selection strategy
crowding density estimation
