张利彪 周春光 马 铭 孙彩堂. 基于极大极小距离密度的多目标微分进化算法[J]. 计算机研究与发展, 2007, 44(1): 177-184.
 引用本文: 张利彪 周春光 马 铭 孙彩堂. 基于极大极小距离密度的多目标微分进化算法[J]. 计算机研究与发展, 2007, 44(1): 177-184.
Zhang Libiao, Zhou Chunguang, Ma Ming, and Sun Caitang. A Multi-Objective Differential Evolution Algorithm Based on Max-Min Distance Density[J]. Journal of Computer Research and Development, 2007, 44(1): 177-184.
 Citation: Zhang Libiao, Zhou Chunguang, Ma Ming, and Sun Caitang. A Multi-Objective Differential Evolution Algorithm Based on Max-Min Distance Density[J]. Journal of Computer Research and Development, 2007, 44(1): 177-184.

## A Multi-Objective Differential Evolution Algorithm Based on Max-Min Distance Density

• 摘要: 微分进化(differential evolution)是一种新的简单而有效的直接全局优化算法，并在许多领域得到了成功应用.提出了基于极大极小距离密度的多目标微分进化算法.新算法定义了极大极小距离密度，给出了基于极大极小距离密度的Pareto候选解集的维护方法，保证了非劣解集的多样性.并根据个体间的Pareto 支配关系和极大极小距离密度改进了微分进化的选择操作，保证了算法的收敛性，实现了利用微分进化算法求解多目标优化问题.通过对5个ZDT测试函数、两个高维测试函数的实验及与其他多目标进化算法的对比和分析，验证了新算法的可行性和有效性.

Abstract: Differential evolution is a simple and powerful globally optimization new algorithm. It is a population-based, direct search algorithm, and has been successfully applied in various fields. A multi-objective differential evolution algorithm based on max-min distance density is proposed. The algorithm proposed defines max-min distance density and gives a Pareto candidate solution set maintenance method, ensuring the diversity of the Pareto solution set. Using Pareto dominance relationship among individuals and max-min distance density, the algorithm improves the selection operation of differential evolution, ensures the convergence of the algorithm, and realizes the solution of multi-objective optimization problems by differential evolution. The proposed algorithm is applied to five ZDT test functions and two high dimension test functions, and it is also compared with other multi-objective evolutionary algorithms. Experimental result and analysis show that the algorithm is feasible and efficient.

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