research-article

Improve Theoretical Upper Bound of Jumpk Function by Evolutionary Multitasking

Authors:

Zhengxin Huang,

Zefeng ChenAuthors Info & Claims

HPCCT '19: Proceedings of the 2019 3rd High Performance Computing and Cluster Technologies Conference

Pages 44 - 50

https://doi.org/10.1145/3341069.3342982

Published: 22 June 2019 Publication History

Abstract

Recently, the concept of evolutionary multitasking has emerged in the field of evolutionary computation as a promising approach to exploit the latent synergies among distinct optimization problems automatically. Many experimental studies have shown multifactorial evolutionary algorithm (MFEA), an implemented algorithm of evolutionary multitasking, can outperform the traditional optimization approaches of solving each task independently on handling synthetic and real-world multi-task optimization (MTO) problems in terms of solution quality and computation resource. However, as far as we know, there exists no study demonstrating the superiority of evolutionary multitasking from the aspect of theoretical analysis. In this paper, we propose a simple (4+2) MFEA to optimize the benchmarks Jumpk and LeadingOnes functions simultaneously. Our theoretical analysis shows that the upper bound of expected running time for the proposed algorithm on the Jumpk function can be improved to O(n2 + 2k) while the best upper bound for single-task optimization on this problem is O(nk-1). Moreover, the upper bound of expected running time to optimize LeadingOnes function is not increased. This result indicates that evolutionary multitasking is probably a promising approach to deal with some problems which traditional optimization methods can't well tackle. This paper provides an evidence of the effectiveness of the evolutionary multitasking from the aspect of theoretical analysis.

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Cited By

Zhao HNing XLiu XWang CLiu J(2023)What makes evolutionary multi-task optimization betterApplied Soft Computing10.1016/j.asoc.2023.110545145:COnline publication date: 1-Sep-2023
https://dl.acm.org/doi/10.1016/j.asoc.2023.110545
Wei TWang SZhong JLiu DZhang J(2022)A Review on Evolutionary Multitask Optimization: Trends and ChallengesIEEE Transactions on Evolutionary Computation10.1109/TEVC.2021.313943726:5(941-960)Online publication date: Oct-2022
https://doi.org/10.1109/TEVC.2021.3139437
Chen ZZhou YHe XZhang J(2022)Learning Task Relationships in Evolutionary Multitasking for Multiobjective Continuous OptimizationIEEE Transactions on Cybernetics10.1109/TCYB.2020.302917652:6(5278-5289)Online publication date: Jun-2022
https://doi.org/10.1109/TCYB.2020.3029176
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Index Terms

Improve Theoretical Upper Bound of Jumpk Function by Evolutionary Multitasking
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
      1. Discrete space search

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HPCCT '19: Proceedings of the 2019 3rd High Performance Computing and Cluster Technologies Conference

June 2019

293 pages

ISBN:9781450371858

DOI:10.1145/3341069

Copyright © 2019 ACM.

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Published: 22 June 2019

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HPCCT 2019: 2019 The 3rd High Performance Computing and Cluster Technologies Conference

June 22 - 24, 2019

Guangzhou, China

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Cited By

Zhao HNing XLiu XWang CLiu J(2023)What makes evolutionary multi-task optimization betterApplied Soft Computing10.1016/j.asoc.2023.110545145:COnline publication date: 1-Sep-2023
https://dl.acm.org/doi/10.1016/j.asoc.2023.110545
Wei TWang SZhong JLiu DZhang J(2022)A Review on Evolutionary Multitask Optimization: Trends and ChallengesIEEE Transactions on Evolutionary Computation10.1109/TEVC.2021.313943726:5(941-960)Online publication date: Oct-2022
https://doi.org/10.1109/TEVC.2021.3139437
Chen ZZhou YHe XZhang J(2022)Learning Task Relationships in Evolutionary Multitasking for Multiobjective Continuous OptimizationIEEE Transactions on Cybernetics10.1109/TCYB.2020.302917652:6(5278-5289)Online publication date: Jun-2022
https://doi.org/10.1109/TCYB.2020.3029176
Osaba EDel Ser JMartinez AHussain A(2022)Evolutionary Multitask Optimization: a Methodological Overview, Challenges, and Future Research DirectionsCognitive Computation10.1007/s12559-022-10012-814:3(927-954)Online publication date: 12-Apr-2022
https://doi.org/10.1007/s12559-022-10012-8
Xu QWang NWang LLi WSun Q(2021)Multi-Task Optimization and Multi-Task Evolutionary Computation in the Past Five Years: A Brief ReviewMathematics10.3390/math90808649:8(864)Online publication date: 14-Apr-2021
https://doi.org/10.3390/math9080864
Xu ZZhang KXu XHe J(2020)A Fireworks Algorithm Based on Transfer Spark for Evolutionary MultitaskingFrontiers in Neurorobotics10.3389/fnbot.2019.0010913Online publication date: 17-Jan-2020
https://doi.org/10.3389/fnbot.2019.00109
Wang LSun QXu QLi WJiang Q(2020)Analysis of Multitasking Evolutionary Algorithms under the Order of Solution VariablesComplexity10.1155/2020/46094892020Online publication date: 14-Oct-2020
https://dl.acm.org/doi/10.1155/2020/4609489

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