基于ε占优的正交多目标差分演化算法研究-中国学术期刊网络出版总库 式搜索算法,它利用群体中的个体在解空间中进行搜索,具有自适应、自学习、自组织和隐并行性等特点.目前,利用演化算法求解多目标优化问题(multi-objectiveopti mization problems,MOPs)已成为演化计算一个热门研究方向[1].与传统求解MOPs的数值算? xxx 【读者推荐文章
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自60年代以来，人们对求解多目标优化问题的兴趣日益增加。
Since the 60s, people's interest on solving the multi-objective optimization questions has increased day by day.
它的潜在并行性及自组织、自适应、自学习的智能特性对于求解多目标优化问题具有巨大的潜力。
Due to its intrinsic parallelism, self-organizing, adaptation and self-learning intelligent properties, evolutionary computation has large potential to solve multiple objectives optimal solutions.
计算机仿真表明，该算法可以明显改善求解多目标优化问题时的寻优过程，能适应实际应用环境下快速、有效的决策要求。
The simulation results demonstrate that the new algorithm can improve the process of MOP optimization, and can meet the requirements of high-speed and effectiveness in application.
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