Core-guided method for constraint-based multi-objective combinatorial optimization

Multi-Objective Combinatorial Optimization(MOCO), which consists of several conflicting objectives to be optimized, finds an ever-increasing number of uses in many real-world applications. In past years, the research of MOCO mainly focuses on evolutionary algorithms. Recently, constraint-based metho...

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Bibliographic Details
Published in:Applied intelligence (Dordrecht, Netherlands) Vol. 51; no. 6; pp. 3865 - 3879
Main Authors: Tian, Naiyu, Ouyang, Dantong, Wang, Yiyuan, Hou, Yimou, Zhang, Liming
Format: Journal Article
Language:English
Published: New York Springer US 01.06.2021
Springer Nature B.V
Subjects:
Algorithms
Artificial Intelligence
Combinatorial analysis
Computer Science
Constraints
Evolutionary algorithms
Machines
Manufacturing
Mechanical Engineering
Multiple objective analysis
Optimization
Processes
Pareto front
Constraint
Minimal correction set
SAT
Multi-objective combinatorial optimization
ISSN:0924-669X, 1573-7497
Online Access:Get full text
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Summary:Multi-Objective Combinatorial Optimization(MOCO), which consists of several conflicting objectives to be optimized, finds an ever-increasing number of uses in many real-world applications. In past years, the research of MOCO mainly focuses on evolutionary algorithms. Recently, constraint-based methods come into the view and have been proved to be effective on MOCO problems. This paper builds on the previous works of constraint-based algorithm MCSEnumPD(AAAI-18) using path diversification method. Due to the inadequacy that the original method fails to prune the redundant search space effectively, this paper proposes the definition of infeasible path and develops a novel algorithm that exploits the properties of unsatisfiable cores, referred as CgPDMCS. The approach extends MCSEnumPD algorithm with a core-guided path diversification method, which avoids solving infeasible paths representing the supersets of the unsatisfiable cores. Experimental results show that the novel approach provides further performance gains over the previous constraint-based algorithms, especially for the instances tightly constrained.
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ISSN:0924-669X
1573-7497
DOI:10.1007/s10489-020-01998-5