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MOPSO algorithm and its application in multipurpose multireservoir operations

机译：MOPSO算法及其在多用途多油藏作业中的应用

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摘要

The main reason for applying evolutionary algorithms in multi-objective optimization problems is to obtain near-optimal nondominated solutions/Pareto fronts, from which decision-makers can choose a suitable solution. The efficiency of multi-objective optimization algorithms depends on the quality and quantity of Pareto fronts produced by them. To compare different Pareto fronts resulting from different algorithms, criteria are considered and applied in multi-objective problems. Each criterion denotes a characteristic of the Pareto front. Thus, ranking approaches are commonly used to evaluate different algorithms based on different criteria. This paper presents three multi-objective optimization methods based on the multi-objective particle swarm optimization (MOPSO) algorithm. To evaluate these methods, bi-objective mathematical benchmark problems are considered. Results show that all proposed methods are successful in finding near-optimal Pareto fronts. A ranking method is used to compare the capability of the proposed methods and the best method for further study is suggested. Moreover, the nominated method is applied as an optimization tool in real multi-objective optimization problems in multireservoir system operations. A new technique in multi-objective optimization, called warm-up, based on the PSO algorithm is then applied to improve the quality of the Pareto front by single-objective search. Results show that the proposed technique is successful in finding an optimal Pareto front.
机译：在多目标优化问题中应用进化算法的主要原因是获得接近最优的非支配解/ Pareto前沿，决策者可以从中选择合适的解决方案。多目标优化算法的效率取决于它们产生的Pareto前沿的质量和数量。为了比较不同算法产生的不同Pareto前沿，需要考虑标准并将其应用于多目标问题。每个标准表示帕累托前沿的特征。因此，排名方法通常用于根据不同的标准评估不同的算法。本文提出了基于多目标粒子群算法（MOPSO）的三种多目标优化方法。为了评估这些方法，考虑了双目标数学基准问题。结果表明，所有提出的方法均能成功找到近似最优的帕累托前沿。排序方法用于比较所提出的方法的能力，并提出了进一步研究的最佳方法。此外，在多储层系统运行中，在实际的多目标优化问题中，将所推荐的方法用作优化工具。然后，将基于PSO算法的多目标优化新技术称为预热，以通过单目标搜索提高帕累托锋的质量。结果表明，所提出的技术成功地找到了最优的帕累托前沿。

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  • 来源
    《Journal of Hydroinformatics》 |2011年第4期|p.794-811|共18页
  • 作者

    E. Fallah-Mehdipour; O. Bozorg Haddad; M. A. Marino;

  • 作者单位

    Department of Irrigation and Reclamation Engineering, Faculty of Agriculture Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Karaj, Tehran, Iran;

    Department of Irrigation and Reclamation Engineering, Faculty of Agriculture Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Karaj, Tehran, Iran;

    Department of Land, Air and Water Resources, Department of Civil and Environmental Engineering, and Department of Biological and Agricultural Engineering, University of California, 139 Veihmeyer Hall, University of California, Davis, CA 95616-8628, USA;

  • 收录信息 美国《科学引文索引》(SCI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    multi-objective particle swarm optimization (MOPSO); multipurpose; multireservoir systems; optimization problems;

    机译：多目标粒子群优化（MOPSO）;多用途多储层系统;优化问题;

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