The Pareto-Following Variation Operator as an alternative approximation model
The Pareto-Following Variation Operator as an alternative approximation model
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DOI：
10.1109/cec.2009.4982924
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发表时间：
2009-05
期刊：
影响因子：
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通讯作者：
A. K. A. Talukder-A.-K.-A.-Talukder-145512754;M. Kirley;R. Buyya
中科院分区：
文献类型：
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作者：
A. K. A. Talukder-A.-K.-A.-Talukder-145512754;M. Kirley;R. Buyya
本文对pareto - follow Variation Operator （PFVO）作为多目标进化算法（MOEA）的近似方法进行了批判性分析。在以前的工作中，我们已经描述了PFVO的开发和实现。仿真结果表明，将PFVO与NSGA-II集成后，算法的收敛速度显著提高。在本研究中，我们扩展了这项工作。我们声称，当PFVO与任何在选择之前使用非支配排序程序的MOEA相结合时，它将导致更快的收敛和高质量的解。给出了SPEA-II和RM-MEDA两种基本算法的数值计算结果。我们还描述了引入的对近似方法的增强，以便增强的算法能够在正确的方向上跟踪帕累托最优前沿。
This paper presents a critical analysis of the Pareto-Following Variation Operator (PFVO) when used as an approximation method for Multiobjective Evolutionary Algorithms (MOEA). In previous work, we have described the development and implementation of the PFVO. The simulation results reported indicated that when the PFVO was integrated with NSGA-II there was a significant increase in the convergence speed of the algorithm. In this study, we extend this work. We claim that when the PFVO is combined with any MOEA that uses a non-dominated sorting routine before selection, it will lead to faster convergence and high quality solutions. Numerical results are presented for two base algorithms: SPEA-II and RM-MEDA to support are claim. We also describe enhancements to the approximation method that were introduced so that the enhanced algorithm was able to track the Pareto-optimal front in the right direction.