Balancing Convergence and Diversity in Decomposition-Based Many-Objective Optimizers
Balancing Convergence and Diversity in Decomposition-Based Many-Objective Optimizers
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DOI：
10.1109/tevc.2015.2443001
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发表时间：
2016-04
影响因子：
14.3
通讯作者：
Yuan Yuan-Yuan;Hua Xu;Bo D. Wang;Bo Zhang;X. Yao
中科院分区：
文献类型：
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作者：
Yuan Yuan-Yuan;Hua Xu;Bo D. Wang;Bo Zhang;X. Yao
基于分解的多目标进化算法（MOEAs）通常利用聚合函数将多目标优化问题分解为多个单目标优化问题。然而，由于所采用的聚合函数的轮廓线的性质，他们通常无法保持在高维目标空间的多样性，即使使用不同的权重向量。为了解决这个问题，我们建议保持所需的多样性的解决方案在其进化过程中明确利用垂直距离的解决方案的权重向量在目标空间中，实现更好的平衡之间的收敛性和多样性在多目标优化。这个想法是为了增强两个性能良好的基于分解的算法，即，MOEA，基于分解和集成适应度排名。这两个增强的算法进行了比较，几个国家的最先进的算法和一系列的比较实验进行了一些测试问题，从两个著名的测试套件。实验结果表明，本文提出的两种算法在平衡收敛性和多样性方面比前人的算法更有效，在求解多目标优化问题时，与现有算法相比也具有很强的竞争力.
The decomposition-based multiobjective evolutionary algorithms (MOEAs) generally make use of aggregation functions to decompose a multiobjective optimization problem into multiple single-objective optimization problems. However, due to the nature of contour lines for the adopted aggregation functions, they usually fail to preserve the diversity in high-dimensional objective space even by using diverse weight vectors. To address this problem, we propose to maintain the desired diversity of solutions in their evolutionary process explicitly by exploiting the perpendicular distance from the solution to the weight vector in the objective space, which achieves better balance between convergence and diversity in many-objective optimization. The idea is implemented to enhance two well-performing decomposition-based algorithms, i.e., MOEA, based on decomposition and ensemble fitness ranking. The two enhanced algorithms are compared to several state-of-the-art algorithms and a series of comparative experiments are conducted on a number of test problems from two well-known test suites. The experimental results show that the two proposed algorithms are generally more effective than their predecessors in balancing convergence and diversity, and they are also very competitive against other existing algorithms for solving many-objective optimization problems.