A hybrid evolutionary multiobjective optimization algorithm with adaptive multi-fitness assignment
A hybrid evolutionary multiobjective optimization algorithm with adaptive multi-fitness assignment
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DOI：
10.1007/s00500-014-1480-9
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发表时间：
2015-11-01
期刊：
影响因子：
4.1
通讯作者：
Tan, Kay Chen
中科院分区：
文献类型：
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作者：
Gu, Fangqing;Liu, Hai-Lin;Tan, Kay Chen
关于混合多算子重组方法的研究已经有很多，但是将不同的适应度分配结合到一个框架中的研究还很少。另一方面，适应度分配对进化多目标优化算法（EMOA）的性能有着显著的影响。本文提出了一种混合EMOA算法，该算法根据种群在目标空间中的分布情况将种群划分为若干个较小的子种群。每个子群体是由一个单独的EMOA，和一个混合的性能指标估计这些EMOA的性能。我们专注于适应度分配，并假设子种群中使用的所有EMOA采用相同的重组算子。为了评估该算法的性能，我们比较了它与MOEA/D-M2M，MOE-A/D，SMS-EMOA和NSGA-II的16个测试实例。实验结果表明，该算法的性能优于或类似于比较EMOA。
There are several studies on hybrid multi-operator recombination methods, while few works have been proposed in the area of combining different fitness assignment in a framework. On the other hand, it is known that fitness assignment has a marked impact on the performance of evolutionary multiobjective optimization algorithm (EMOA). In this paper, a hybrid EMOA is proposed, which divides the population into several smaller subpopulations according to their distribution in the objective space. Each subpopulation is evolved by an individual EMOA, and a hybrid performance measure estimates the performance of these EMOAs. We focus on the fitness assignment and assume that all EMOAs used in the subpopulations adopt the same recombination operator. To evaluate performance of the proposed algorithm, we compare it with MOEA/D-M2M, MOE-A/D, SMS-EMOA and NSGA-II on 16 test instances. Experimental results show that the proposed algorithm performs better than or similar to those compared EMOAs.