A MULTIOBJECTIVE EVOLUTIONARY ALGORITHM USING DYNAMIC WEIGHT DESIGN METHOD

Fangqing Gu; Hai-lin Liu; Kay Chen Tan

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A MULTIOBJECTIVE EVOLUTIONARY ALGORITHM USING DYNAMIC WEIGHT DESIGN METHOD

机译：动态权重设计方法的多目标进化算法

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摘要

In most multiobjective evolutionary algorithms (MOEA) based on aggregating objectives, the weight vectors are user-supplied or generated randomly, and they are static in the algorithms. If the Pareto front (PF) shape is not complex, the algorithms can find a set of uniformly distributed Pareto optimal solutions along the PF; otherwise, they might fail. A dynamic weight design method based on the projection of the current nondominant solutions and equidistant interpolation is proposed in this paper. Even if the PF is complex, we can find evenly distributed Pareto optimal solutions by this method. Some test instances are constructed to compare the performance of the MOEA/D using dynamic weight design method with that of MOEA/D. The results indicate that the dynamic weight design method can dramatically improve the performance of the algorithms.

机译：在大多数基于聚集目标的多目标进化算法（MOEA）中，权重向量是用户提供或随机生成的，并且在算法中是静态的。如果Pareto前沿（PF）形状不复杂，则算法可以沿着PF找到一组均匀分布的Pareto最优解。否则，它们可能会失败。提出了一种基于当前非优势解的投影和等距插值的动态权重设计方法。即使PF复杂，我们也可以通过这种方法找到均匀分布的Pareto最优解。构建了一些测试实例，以使用动态权重设计方法将MOEA / D的性能与MOEA / D的性能进行比较。结果表明，动态权重设计方法可以显着提高算法的性能。

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来源
《International Journal of Innovative Computing Information and Control》 |2012年第5b期|p.3677-3688|共12页
作者
Fangqing Gu; Hai-lin Liu; Kay Chen Tan;
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作者单位

Faculty of Applied Mathematics Guangdong University of Technology No. 100, Waihuan Xi Road, Panyu District, Guangzhou, P. R. China;

Faculty of Applied Mathematics Guangdong University of Technology No. 100, Waihuan Xi Road, Panyu District, Guangzhou, P. R. China;

Faculty of Engineering National University of Singapore 21 Lower Kent Ridge Road, Singapore;

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正文语种 eng
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关键词
equidistant interpolation; multiobjective optimization; evolutionary algo-rithm; uniformly distribution;

机译：等距插值多目标优化;进化算法均匀分布;

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A MULTIOBJECTIVE EVOLUTIONARY ALGORITHM USING DYNAMIC WEIGHT DESIGN METHOD

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