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Cooperative co-evolution with online optimizer selection for large-scale optimization

Published: 02 July 2018 Publication History

Abstract

Cooperative co-evolution (CC) is an effective framework that can be used to solve large-scale optimization problems. It typically divides a problem into components and uses one optimizer to solve the components in a round-robin fashion. However the relative contribution of each component to the overall fitness value may vary. Furthermore, using one optimizer may not be sufficient when solving a wide range of components with different characteristics. In this paper, we propose a novel CC framework which can select an appropriate optimizer to solve a component based on its contribution to the fitness improvement. In each evolutionary cycle, the candidate optimizer and component that make the greatest contribution to the fitness improvement are selected for evolving. We evaluated the efficacy of the proposed CC with Optimizer Selection (CCOS) algorithm using large-scale benchmark problems. The numerical experiments showed that CCOS outperformed the CC model without optimizer selection ability. When compared against several other state-of-the-art algorithms, CCOS generated competitive solution quality.

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      cover image ACM Conferences
      GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference
      July 2018
      1578 pages
      ISBN:9781450356183
      DOI:10.1145/3205455
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      Published: 02 July 2018

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      Author Tags

      1. algorithm hybridization
      2. algorithm selection
      3. cooperarive co-evolution
      4. large-scale optimization
      5. resources allocation

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      • (2023)An Efficient Adaptive Differential Grouping Algorithm for Large-Scale Black-Box OptimizationIEEE Transactions on Evolutionary Computation10.1109/TEVC.2022.317079327:3(475-489)Online publication date: 1-Jun-2023
      • (2023)Gray-box local search with groups of step sizesSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-023-09129-127:24(18709-18722)Online publication date: 13-Sep-2023
      • (2022)An Efficient Differential Grouping Algorithm for Large-Scale Global OptimizationIEEE Transactions on Evolutionary Computation10.1109/TEVC.2022.323007028:1(32-46)Online publication date: 21-Dec-2022
      • (2020)Contribution Based Co-Evolutionary Algorithm for Large-Scale Optimization ProblemsIEEE Access10.1109/ACCESS.2020.30364388(203369-203381)Online publication date: 2020
      • (2019)Cooperative Co-Evolution Algorithm with an MRF-Based Decomposition Strategy for Stochastic Flexible Job Shop SchedulingMathematics10.3390/math70403187:4(318)Online publication date: 28-Mar-2019
      • (2019)Decomposition for Large-scale Optimization Problems with Overlapping Components2019 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2019.8790204(326-333)Online publication date: 10-Jun-2019
      • (2019)Adaptive Multi-optimiser Cooperative Co-evolution for Large-Scale Optimisation2019 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2019.8790022(705-712)Online publication date: 10-Jun-2019
      • (2019)Distributed Contribution-Based Quantum-Behaved Particle Swarm Optimization With Controlled Diversity for Large-Scale Global Optimization ProblemsIEEE Access10.1109/ACCESS.2019.29441967(150093-150104)Online publication date: 2019

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