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首页> 外文期刊>Natural Computing >A multi-objective optimization method based on discrete bacterial algorithm for environmental/economic power dispatch
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A multi-objective optimization method based on discrete bacterial algorithm for environmental/economic power dispatch

机译：基于离散细菌算法的环境/经济动力调度多目标优化方法

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摘要

Multi-objective optimization is an interesting and hot topic in the literature involving the conflicting objectives to be solved simultaneously. In this study, a new multiple optimization method based on a discrete bacterial algorithm is developed to address the multi-objective economic-environmental dispatch problem with non-linear, non-convex, and complexity constraints. In the proposed multi-objective bacterial based algorithm, the existence of bacteria complies with a fitness survival mechanism, in which a health sorting approach is operated to control the chances of reproduction as well as elimination. The performances of bacteria have been recorded and sorted for health evaluation, which can help to group the individuals according to their search capability and improve the overall quality of the population. To speed up the convergence rate and avoid local minima to some extent, a comprehensive learning strategy is embedded to enable the communication exchanges between the bacteria and external archive. The standard IEEE 30-bus, 6-generator test system is adopted to illustrate the efficiency of the proposed method by making the comparison with the other multiple bacterial-based algorithms as well as six other well developed evolutionary algorithms. The effectiveness of the propose method is well validated in experiments by providing similar or superior solutions to environmental/economic power dispatch issues considering the various constraints.
机译：多目标优化是涉及同时要解决的冲突目标的文献中一个有趣且热门的话题。在这项研究中，开发了一种基于离散细菌算法的新的多重优化方法，以解决具有非线性，非凸性和复杂性约束的多目标经济环境调度问题。在提出的基于多目标细菌的算法中，细菌的存在符合适应性生存机制，其中采用健康分类方法来控制繁殖和消除的机会。细菌的性能已被记录并分类以进行健康评估，这可以帮助根据搜索能力对个体进行分组并提高总体质量。为了加快收敛速度​​并在某种程度上避免局部最小值，嵌入了一种全面的学习策略，以使细菌与外部档案之间能够进行通信交换。通过与其他多种基于细菌的算法以及其他六种发达的进化算法进行比较，采用了标准的IEEE 30总线，六发电机测试系统来说明该方法的效率。考虑到各种限制因素，通过为环境/经济电力调度问题提供相似或更好的解决方案，该方法的有效性在实验中得到了充分验证。

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  • 来源
    《Natural Computing》 |2017年第4期|549-565|共17页
  • 作者

    Tan Lijing; Wang Hong; Yang Chen; Niu Ben;

  • 作者单位

    Shenzhen Inst Informat Technol, Dept Business Management, Shenzhen 518172, Peoples R China;

    Shenzhen Univ, Coll Management, Shenzhen 518060, Peoples R China|Hong Kong Polytech Univ, Fac Engn, Hong Kong, Hong Kong, Peoples R China;

    Shenzhen Univ, Coll Management, Shenzhen 518060, Peoples R China;

    Shenzhen Univ, Coll Management, Shenzhen 518060, Peoples R China|Arizona State Univ, Sch Comp Informat & Decis Syst Engn, Tempe, AZ 85281 USA;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Multi-objective optimization; Discrete bacterial-based algorithm; Environmental/economic power dispatch;

    机译：多目标优化;基于细菌的离散算法;环境/经济动力调度;

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