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Localized distance and time-based differential evolution for multimodal optimization problems

Published: 19 July 2022 Publication History

Abstract

Multimodal optimization problems (MMOPs) aim to find global multiple optimal solutions with high accuracy. Although many state-of-the-art algorithms are proposed to deal with MMOPs, there are still some challenges as how to avoid local optima and how to refine the solutions accuracy. Aim to these, this paper proposes a Localized Distance and Time-based Differential Evolution (LDTDE) for MMOPs, which includes three contributions. Firstly, a Random and Neighborhood-based Mutation (RNM) strategy is proposed to avoid local optima and refine the accuracy of found solutions. That is each individual not only performs random-based mutation operation but also performs neighborhood-based mutation operation by Euclidean distance. Secondly, a Locality-based Crowding Selection (LCS) strategy is introduced to accelerate the convergence and further approach the global optima. Thirdly, an Adaptive Parameter Control (APC) strategy is proposed to reduce the sensitivity of parameters, which can dynamically calculate the appropriate parameters for each individual based on the state of itself. The performance of LDTDE is tested on CEC'2013 and compared with state-of-the-art multimodal optimization algorithms.

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Cited By

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  • (2023)Strengthening evolution-based differential evolution with prediction strategy for multimodal optimization and its application in multi-robot task allocationApplied Soft Computing10.1016/j.asoc.2023.110218139:COnline publication date: 1-May-2023
  • (2023)An Evolutionary Multi-task Genetic Algorithm with Assisted-Task for Flexible Job Shop SchedulingComputer Supported Cooperative Work and Social Computing10.1007/978-981-99-2385-4_27(367-378)Online publication date: 13-May-2023

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  1. Localized distance and time-based differential evolution for multimodal optimization problems

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    cover image ACM Conferences
    GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference Companion
    July 2022
    2395 pages
    ISBN:9781450392686
    DOI:10.1145/3520304
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    Published: 19 July 2022

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    Author Tags

    1. adaptive parameter control
    2. differential evolution
    3. multimodal optimization problems
    4. temporal locality

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    • (2023)Strengthening evolution-based differential evolution with prediction strategy for multimodal optimization and its application in multi-robot task allocationApplied Soft Computing10.1016/j.asoc.2023.110218139:COnline publication date: 1-May-2023
    • (2023)An Evolutionary Multi-task Genetic Algorithm with Assisted-Task for Flexible Job Shop SchedulingComputer Supported Cooperative Work and Social Computing10.1007/978-981-99-2385-4_27(367-378)Online publication date: 13-May-2023

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