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Levy Flights in Metaheuristics Optimization Algorithms - A Review

机译:元启发式优化算法中的征税飞行-综述

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摘要

In recent years, Levy flight (LF) is increasingly being employed as search mechanism in metaheuristics optimization algorithms (MOA) to solve complex real world problems. LF-based algorithms are found to exhibit superior or equivalent results to their non-LF counterparts and are expected to be advantageous in situations where no prior information is accessible, the targets are challenging to ascertain, and the distribution of targets is scarce. It has emerged as an alternative to Gaussian distribution (GD) to achieve randomization in MOA and have applications in diverse biological, chemical, and physical phenomena. The purpose of this article is to make the readers acquainted with the applicability of LF in some of the latest optimization algorithm applied in the field of metaheuristics. Also the present study deals with examination of basic underlying principle in the working of LF along with their related properties.
机译:近年来,征税飞行(LF)越来越多地用作元启发式优化算法(MOA)的搜索机制,以解决现实中的复杂问题。发现基于LF的算法显示出比其非LF同类算法更好的结果或等效的结果,并且在无法获得先验信息,难以确定目标的目标以及目标分布稀缺的情况下有望发挥优势。它已成为高斯分布(GD)的替代方法,可以在MOA中实现随机化,并应用于各种生物,化学和物理现象。本文的目的是使读者熟悉LF在元启发式技术领域中应用的一些最新优化算法的适用性。此外,本研究还探讨了LF工作中的基本基本原理及其相关属性。

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  • 来源
    《Applied Artificial Intelligence》 |2018年第10期|802-821|共20页
  • 作者

    Chawla Mridul; Duhan Manoj;

  • 作者单位

    Deenbandhu Chhotu Ram Univ Sci & Technol, Dept Elect & Commun Engn, Murthal Sonipat, Haryana, India;

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  • 正文语种 eng
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