research-article

Asynchronous Differential Evolution with Strategy Adaptation for Global Numerical Optimization

Authors:

Tae Jong Choi,

Yeonju LeeAuthors Info & Claims

HPCCT '18: Proceedings of the 2018 2nd High Performance Computing and Cluster Technologies Conference

Pages 15 - 18

https://doi.org/10.1145/3234664.3234676

Published: 22 June 2018 Publication History

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Abstract

A new variant of Differential Evolution (DE) algorithm called A-SaDE algorithm is proposed. A-SaDE algorithm is based on two DE variants, Self-adaptive DE (SaDE) and Asynchronous DE (ADE) algorithms. SaDE algorithm is one of the well-known DE algorithms and shows powerful optimization performance by automatically tuning the mutation strategies as well as the control parameters. The asynchronous scheme is recently proposed, and it helps to find better solutions by increasing the greediness. We incorporated these two algorithms called A-SaDE algorithm and tested it with 13 scalable benchmark problems. The experimental results confirm that A-SaDE algorithm outperforms original SaDE algorithm, especially for unimodal functions.

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Cited By

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Kumar SPant M(2023)A Mathematical Model to Minimize the Total Cultivation Cost of SugarcaneSoft Computing for Problem Solving10.1007/978-981-19-6525-8_40(529-542)Online publication date: 2-Mar-2023
https://doi.org/10.1007/978-981-19-6525-8_40
Choi TAhn C(2021)An improved LSHADE-RSP algorithm with the Cauchy perturbation: iLSHADE-RSPKnowledge-Based Systems10.1016/j.knosys.2020.106628215(106628)Online publication date: Mar-2021
https://doi.org/10.1016/j.knosys.2020.106628
Choi TTogelius JCheong Y(2020)Advanced Cauchy Mutation for Differential Evolution in Numerical OptimizationIEEE Access10.1109/ACCESS.2020.29642228(8720-8734)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.2964222

Index Terms

Asynchronous Differential Evolution with Strategy Adaptation for Global Numerical Optimization
1. Mathematics of computing
  1. Mathematical analysis
    1. Mathematical optimization
      1. Discrete optimization
        Optimization with randomized search heuristics
        Evolutionary algorithms
2. Theory of computation
  1. Design and analysis of algorithms
    1. Mathematical optimization
      1. Continuous optimization
        Bio-inspired optimization
      2. Discrete optimization
        Optimization with randomized search heuristics
        Evolutionary algorithms

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Published In

HPCCT '18: Proceedings of the 2018 2nd High Performance Computing and Cluster Technologies Conference

June 2018

126 pages

ISBN:9781450364850

DOI:10.1145/3234664

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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Shanghai Jiao Tong University: Shanghai Jiao Tong University
Xi'an Jiaotong-Liverpool University: Xi'an Jiaotong-Liverpool University
Chinese Academy of Sciences

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 June 2018

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Funding Sources

This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. NRF-2017R1C1B2012752).

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HPCCT 2018

HPCCT 2018: 2018 2nd High Performance Computing and Cluster Technologies Conference

June 22 - 24, 2018

Beijing, China

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Cited By

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Kumar SPant M(2023)A Mathematical Model to Minimize the Total Cultivation Cost of SugarcaneSoft Computing for Problem Solving10.1007/978-981-19-6525-8_40(529-542)Online publication date: 2-Mar-2023
https://doi.org/10.1007/978-981-19-6525-8_40
Choi TAhn C(2021)An improved LSHADE-RSP algorithm with the Cauchy perturbation: iLSHADE-RSPKnowledge-Based Systems10.1016/j.knosys.2020.106628215(106628)Online publication date: Mar-2021
https://doi.org/10.1016/j.knosys.2020.106628
Choi TTogelius JCheong Y(2020)Advanced Cauchy Mutation for Differential Evolution in Numerical OptimizationIEEE Access10.1109/ACCESS.2020.29642228(8720-8734)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.2964222

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Two-stage update biogeography-based optimization using differential evolution algorithm (DBBO)

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