| 引用本文: | 赵志伟,杨景明,呼子宇,车海军.基于角度邻域的多目标差分进化算法[J].控制理论与应用,2017,34(1):22~32.[点击复制] |
| ZHAO Zhi-wei,YANG Jing-ming,HU Zi-yu,CHE Haijun.Multiobjective differential evolution algorithm based on angle neighbourhood[J].Control Theory and Technology,2017,34(1):22~32.[点击复制] |
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| 基于角度邻域的多目标差分进化算法 |
| Multiobjective differential evolution algorithm based on angle neighbourhood |
| 摘要点击 3734 全文点击 2215 投稿时间：2016-01-25 修订日期：2016-12-04 |
| 查看全文 查看/发表评论 下载PDF阅读器 |
| DOI编号 10.7641/CTA.2017.60057 |
| 2017,34(1):22-32 |
| 中文关键词 差分进化 角度邻域 外部存档 多目标优化 |
| 英文关键词 differential evolution angle neighbourhood external archive multiobjective optimization |
| 基金项目 国家自然科学基金项目(U1260203), 河北省高等学校创新团队领军人才培育计划项目(LJRC013), 国家冷轧板带装备及工艺工程技术研究中心开 放课题(2012005)资助. |
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| 中文摘要 |
| 针对如何实现差分进化算法求解多目标优化问题, 提出了一种基于角度邻域的多目标差分进化算法, 通过
在选择操作中引入弱支配概念, 实现了对多目标优化问题的求解. 该算法通过计算目标空间中个体与权重向量的
夹角来确定每个个体的邻域, 并在此基础上引入了基于角度邻域的变异策略, 使个体的变异在邻域内进行, 保证进
化方向. 此外, 该算法创建了一个外部存档用来保存进化过程中的非支配解, 并定期对外部存档进行维护, 大大改善
了解集的分布性. 大量的数值仿真实验结果表明通过角度确定邻域的方法比通过欧氏距离确定邻域的方法更加有
效, 算法所得解集的收敛性和分布性也均明显优于基于分解的差分多目标进化算法(multiobjective evolutionary
algorithm based on decomposition and differential evolution, MOEA/D–DE)和非支配排序算法Ⅱ(nondominated sorting
genetic algorithm II, NSGA). |
| 英文摘要 |
| To solve the multiobjective optimization problem by differential evolution algorithm, a multiobjective differential
evolution algorithm based on angle neighborhood is proposed. The weak domination is introduced to obtain
the capacity of solving the multiobjective optimization problem. The neighbourhood of each individual is determined by
computing the angle between each individual and weight vector in the objective space. To ensure the evolutionary direction
of individual, the mutation strategy based on angle neighbourhood is introduced to execute the mutation operation
in angle neighborhood. Additionally, an external archive is established to save the non-dominated solutions obtained in
evolutionary process. The archive is maintained regularly, and the distributivity of the approximate set has been greatly
improved. A large amount of experimental results show that the neighbourhood determined by angle is more effective than
the neighbourhood determined by Euclidean distance, and the convergence and distribution of the approximate set obtained
by the proposed algorithm are obviously superior to multiobjective evolutionary algorithm based on decomposition and
differential evolution (MOEA/D–DE) and nondominated sorting genetic algorithm Ⅱ(NSGAⅡ). |
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