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首页> 外文期刊>Cybernetics, IEEE Transactions on >The Collaborative Local Search Based on Dynamic-Constrained Decomposition With Grids for Combinatorial Multiobjective Optimization
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The Collaborative Local Search Based on Dynamic-Constrained Decomposition With Grids for Combinatorial Multiobjective Optimization

机译：基于动态约束分解的协作本地搜索，具有组合多目标优化的网格

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摘要

The decomposition-based algorithms [e.g., multiobjective evolutionary algorithm based on decomposition (MOEA/D)] transform a multiobjective optimization problem (MOP) into a number of single-objective optimization subproblems and solve them in a collaborative manner. It is a natural framework for using single-objective local search (LS) to solve combinatorial MOPs. However, commonly used decomposition methods, such as weighted sum (WS), Tchebycheff (TCH), and penalty-based boundary intersection (PBI) may not be good at maintaining the population diversity while providing diverse initial solutions for different LS procedures in a collaborative way. Based on our previous work on the constrained decomposition with grids (CDG), this article proposes a dynamic CDG (DCDG) framework used to design a multiobjective memetic algorithm (DCDG-MOMA). DCDG uses grids for maintaining diversity, supporting the collaborative LS. In addition, DCDG dynamically increases the number of grids for obtaining more nondominated solutions as well as the better collaborative search among them. DCDG-MOMA has been compared with several classical and state-of-the-art algorithms on multiobjective traveling salesman problem (MOTSP), multiobjective quadratic assignment problem (MOQAP), and multiobjective capacitated arc routing problem (MOCARP).
机译：基于分解的算法[例如，基于分解的多目标进化算法（MOEA / D）]将多目标优化问题（MOP）转换为多个单目标优化子问题并以协同方式解决它们。它是使用单目标本地搜索（LS）来解决组合MOP的自然框架。然而，常用的分解方法，例如加权和（WS），TchebyCheff（TCH）和基于惩罚的边界交叉点（PBI）可能不擅长维持群体分集，同时为协作中提供不同LS程序提供不同的初始解决方案办法。基于我们以前的关于与网格（CDG）的受限分解的工作，本文提出了一种用于设计多目标膜算法（DCDG-MOMA）的动态CDG（DCDG）框架。 DCDG使用网格来维持多样性，支持协作LS。此外，DCDG动态增加了获得更多Nondominated解决方案的网格数以及它们之间更好的协作搜索。 DCDG-MOMA与多目标行驶推销员问题（MOTSP），多目标二次分配问题（MOQAP）和多目标电容电弧路由问题（MOCARP）进行了比较的若干经典和最先进的算法。

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  • 来源
    《Cybernetics, IEEE Transactions on》 |2021年第5期|2639-2650|共12页
  • 作者

    Cai Xinye; Xia Chao; Zhang Qingfu; Mei Zhiwei; Hu Han; Wang Lisong; Hu Jun;

  • 作者单位

    Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 210016 Peoples R China|Collaborat Innovat Ctr Novel Software Technol & I Nanjing 210023 Peoples R China|Civil Aviat Univ China Informat Technol Res Base Civil Aviat Adm China Tianjin 300300 Peoples R China;

    Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 210016 Peoples R China|Collaborat Innovat Ctr Novel Software Technol & I Nanjing 210023 Peoples R China|Civil Aviat Univ China Informat Technol Res Base Civil Aviat Adm China Tianjin 300300 Peoples R China;

    City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China;

    Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 210016 Peoples R China|Collaborat Innovat Ctr Novel Software Technol & I Nanjing 210023 Peoples R China|Civil Aviat Univ China Informat Technol Res Base Civil Aviat Adm China Tianjin 300300 Peoples R China;

    Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 210016 Peoples R China|Collaborat Innovat Ctr Novel Software Technol & I Nanjing 210023 Peoples R China|Civil Aviat Univ China Informat Technol Res Base Civil Aviat Adm China Tianjin 300300 Peoples R China;

    Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 210016 Peoples R China|Collaborat Innovat Ctr Novel Software Technol & I Nanjing 210023 Peoples R China|Civil Aviat Univ China Informat Technol Res Base Civil Aviat Adm China Tianjin 300300 Peoples R China;

    Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 210016 Peoples R China|Collaborat Innovat Ctr Novel Software Technol & I Nanjing 210023 Peoples R China|Civil Aviat Univ China Informat Technol Res Base Civil Aviat Adm China Tianjin 300300 Peoples R China;

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  • 正文语种 eng
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  • 关键词

    Collaboration; Optimization; Sociology; Statistics; Heuristic algorithms; Memetics; Cybernetics; Combinatorial multiobjective optimization; constrained decomposition with grids (CDG); decomposition; Pareto local search (LS);

    机译：协作;优化;社会学;统计;启发式算法;梅特科;控制论;组合多目标优化;用网格（CDG）约束分解;分解;帕累托本地搜索（LS）;

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