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Island-cellular model differential evolution for large-scale global optimization

Published: 15 July 2017 Publication History

Abstract

The Island-Cellular Model (ICM) is an important population distribution approach for Evolutionary Algorithms (EAs). This hybrid approach combines the Island Model (IM) and Cellular Model (CM) in a two-layer hierarchical model. Although the ICM has been shown to be an efficient way to implement EAs, there is still a lack of knowledge about its parameters and its performance for Large-Scale Global Optimization (LSGO) problems. However, the ICM approach is able to enrich the evolutionary search by keeping the population diversity in EAs. Thus, this paper proposes to implement the ICM approach for LSGO problems using the Differential Evolutionary (DE) algorithm. It also proposes an experimental study of the ICM parameters by investigating their impact on the EA. Experimental results on Large-Scale Global Optimization Benchmark Functions show that the ICM approach for the DE algorithm improves its performance. Furthermore, the results collected from different ICM approaches indicate that there is a trade-off between solution quality and convergence speed.

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Cited By

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  • (2024)Multi-Objective Majority–Minority Cellular Automata Algorithm for Global and Engineering Design OptimizationAlgorithms10.3390/a1710043317:10(433)Online publication date: 27-Sep-2024
  • (2023)A diversity-driven migration strategy for distributed evolutionary algorithmsSwarm and Evolutionary Computation10.1016/j.swevo.2023.10136182(101361)Online publication date: Oct-2023
  • (2020)A Spark-based differential evolution with grouping topology model for large-scale global optimizationCluster Computing10.1007/s10586-020-03124-zOnline publication date: 30-May-2020

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    cover image ACM Conferences
    GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference Companion
    July 2017
    1934 pages
    ISBN:9781450349390
    DOI:10.1145/3067695
    Permission to make digital or hard copies of all or part 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 components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Publication History

    Published: 15 July 2017

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    Author Tags

    1. cellular model
    2. differential evolution
    3. island model
    4. island-cellular model
    5. large-scale global optimization

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    • Universidade Federal de Ouro Preto

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    View all
    • (2024)Multi-Objective Majority–Minority Cellular Automata Algorithm for Global and Engineering Design OptimizationAlgorithms10.3390/a1710043317:10(433)Online publication date: 27-Sep-2024
    • (2023)A diversity-driven migration strategy for distributed evolutionary algorithmsSwarm and Evolutionary Computation10.1016/j.swevo.2023.10136182(101361)Online publication date: Oct-2023
    • (2020)A Spark-based differential evolution with grouping topology model for large-scale global optimizationCluster Computing10.1007/s10586-020-03124-zOnline publication date: 30-May-2020
    • (2019)Empirical Analysis of Island Model on Large Scale Global Optimization2019 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2019.8790331(342-349)Online publication date: Jun-2019

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