Combining convergence and diversity in evolutionary multiobjective optimization
Combining convergence and diversity in evolutionary multiobjective optimization
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DOI：
10.1162/106365602760234108
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发表时间：
2002-09-01
影响因子：
6.8
通讯作者：
Zitzler, E
中科院分区：
文献类型：
--
作者：
Laumanns, M;Thiele, L;Zitzler, E
在过去的几年中，对进化算法的研究已经证明了它们在解决多目标优化问题方面的优势，其目标是在单次模拟运行中找到多个帕累托最优解。许多研究已经描述了进化算法朝着具有广泛分布解的帕累托最优解集前进的不同方式。然而，没有一种多目标进化算法（MOEAs）能够证明其收敛到具有解之间广泛多样性的真正帕累托最优解。在本文中，我们讨论了为什么许多早期的多目标进化算法不具备这些特性。基于c - 占优的概念，提出了新的存档策略，这些策略克服了这一基本问题，并可证明能使多目标进化算法同时具有所需的收敛性和分布特性。还对基准算法提出了一些修改建议。本文引入的c - 占优概念具有实用性，应该会使所提出的算法对研究人员和实践者都有用。
Over the past few years, the research on evolutionary algorithms has demonstrated their niche in solving multiobjective Optimization problems, where the goal is to find a number of Pareto-optimal solutions in a single simulation run. Many studies have depicted different ways evolutionary algorithms can progress towards the Pareto-optimal set with a widely spread distribution of solutions. However, none of the multiobjective evolutionary algorithms (MOEAs) has a proof of convergence to the true Pareto-optimal solutions with a wide diversity among the solutions. In this paper, we discuss why a number of earlier MOEAs do not have such properties. Based on the concept of c-dominance, new archiving strategies are proposed that overcome this fundamental problem and provably lead to MOEAs that have both the desired convergence and distribution properties. A number of modifications to the baseline algorithm are also suggested. The concept of c-dominance introduced in this paper is practical and should make the proposed algorithms useful to researchers and practitioners alike.