多目标优化的微分进化变体

2007 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making Pub Date : 2007-04-01 DOI:10.1109/MCDM.2007.369422

K. Zielinski, R. Laur

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引用次数: 13

摘要

在多目标优化中，不仅需要快速收敛，而且需要保持足够的多样性，以便找到整个pareto最优前沿。在这项工作中，研究了在选择方案和拥挤距离分配方面不同的差异进化的四种变体。假设这些变量在收敛速度和多样性数量上有所不同。性能显示为连续1000代，因此可以检测到随时间变化的不同行为

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Variants of Differential Evolution for Multi-Objective Optimization

In multi-objective optimization not only fast convergence is important, but it is also necessary to keep enough diversity so that the whole Pareto-optimal front can be found. In this work four variants of differential evolution are examined that differ in the selection scheme and in the assignment of crowding distance. The assumption is checked that the variants differ in convergence speed and amount of diversity. The performance is shown for 1000 consecutive generations, so that different behavior over time can be detected

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来源期刊

2007 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making

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