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The Collaborative Local Search Based on Dynamic-Constrained Decomposition With Grids for Combinatorial Multiobjective Optimization

delete2021-05-01
delete25
PRE
AI
蔡
蔡昕烨 (Xinye Cai) *
C
Chao Xia
Q
Qingfu Zhang
Z
Zhiwei Mei
H
Han Hu
L
Lisong Wang
J
Jun Hu
DOI：10.1109/TCYB.2019.2931434delete
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Abstract

Abstract

En 中文
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).
Keywords:
Collaboration
Optimization
Sociology
Statistics
Heuristic algorithms
Memetics
Cybernetics
Combinatorial multiobjective optimization
constrained decomposition with grids (CDG)
decomposition
Pareto local search (LS)
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Journal

IEEE Transactions on Cybernetics cover
IEEE Transactions on Cybernetics
IF:
10.5
Papers:
1.1W
Citations:
5.0W

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