Mating Scheme for Controlling the Diversity-Convergence Balance for Multiobjective Optimization
Mating Scheme for Controlling the Diversity-Convergence Balance for Multiobjective Optimization
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DOI：
10.1007/978-3-540-24854-5_121
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发表时间：
2004-06
期刊：
影响因子：
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通讯作者：
H. Ishibuchi;Youhei Shibata
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文献类型：
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作者：
H. Ishibuchi;Youhei Shibata
本文的目的是清楚地表明潜在的能力，基于相似性的交配计划，动态控制的多样性的解决方案和收敛到帕累托前沿进化多目标优化之间的平衡。基于相似性的交配方案以如下方式选择两个父母。为了选择一个父节点（比如父节点A），首先通过迭代标准的基于适应度的二元锦标赛选择来选择预定数量的候选节点（比如α候选节点）。然后在目标空间中计算这些候选的平均解。选择与平均解最相似或最不相似的候选者作为父A。当我们想要增加解的多样性时，父A的选择概率通过选择最不相似的候选者而偏向于极端解。这种多样性保护努力的强度由参数α调整。当我们想要减少多样性时，我们也可以通过选择最相似的候选者来使选择概率偏向中心解。另一个亲本的选择概率（即，父A的配对）偏向于与父A相似的解，以增加向帕累托前沿的收敛速度。这是通过在预先指定数量的候选者（比如β候选者）中选择与父A最相似的一个来实现的。通过参数β来调整这种收敛加速努力的强度。当我们想要增加解的多样性时，选择与父A最不相似的候选者作为它的配偶。我们的思想是通过在进化多目标优化算法执行过程中改变两个控制参数α和β的值来动态地控制多样性和收敛性的平衡。我们通过多目标背包问题的计算实验来检验我们的想法的有效性。
The aim of this paper is to clearly demonstrate the potential ability of a similarity-based mating scheme to dynamically control the balance between the diversity of solutions and the convergence to the Pareto front in evolutionary multiobjective optimization. The similarity-based mating scheme chooses two parents in the following manner. For choosing one parent (say Parent A), first a pre-specified number of candidates (sayαcandidates) are selected by iterating the standard fitness-based binary tournament selection. Then the average solution of those candidates is calculated in the objective space. The most similar or dissimilar candidate to the average solution is chosen as Parent A. When we want to increase the diversity of solutions, the selection probability of Parent A is biased toward extreme solutions by choosing the most dissimilar candidate. The strength of this diversity-preserving effort is adjusted by the parameterα. We can also bias the selection probability toward center solutions by choosing the most similar candidate when we want to decrease the diversity. The selection probability of the other parent (i.e., the mate of Parent A) is biased toward similar solutions to Parent A for increasing the convergence speed to the Pareto front. This is implemented by choosing the most similar one to Parent A among a pre-specified number of candidates (sayβcandidates). The strength of this convergence speed-up effort is adjusted by the parameterβ. When we want to increase the diversity of solutions, the most dissimilar candidate to Parent A is chosen as its mate. Our idea is to dynamically control the diversity-convergence balance by changing the values of two control parametersαandβduring the execution of evolutionary multiobjective optimization algorithms. We examine the effectiveness of our idea through computational experiments on multiobjective knapsack problems.