PPLS/D: Parallel Pareto Local Search Based on Decomposition

Pareto local search (PLS) is a basic building block in many metaheuristics for a multiobjective combinatorial optimization problem. In this paper, an enhanced PLS variant called parallel PLS based on decomposition (PPLS/D) is proposed. PPLS/D improves the efficiency of PLS using the techniques of pa...

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Veröffentlicht in:IEEE transactions on cybernetics Jg. 50; H. 3; S. 1060 - 1071
Hauptverfasser: Shi, Jialong, Zhang, Qingfu, Sun, Jianyong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.03.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Combinatorial analysis
Combinatorial mathematics
Decomposition
Evolutionary computation
Heuristic methods
Linear programming
Multiobjective combinatorial optimization
Multiple objective analysis
Optimization
parallel metaheuristic
Parallel processing
Pareto local search
Quadratic programming
Search problems
Searching
Traveling salesman problem
Traveling salesman problems
unconstrained binary quadratic programming
ISSN:2168-2267, 2168-2275, 2168-2275
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Zusammenfassung:Pareto local search (PLS) is a basic building block in many metaheuristics for a multiobjective combinatorial optimization problem. In this paper, an enhanced PLS variant called parallel PLS based on decomposition (PPLS/D) is proposed. PPLS/D improves the efficiency of PLS using the techniques of parallel computation and problem decomposition. It decomposes the original search space into L subregions and executes L parallel processes searching in these subregions simultaneously. Inside each subregion, the PPLS/D process is guided by a unique scalar objective function. PPLS/D differs from the well-known two phase PLS in that it uses the scalar objective function to guide every move of the PLS procedure in a fine-grained manner. In the experimental studies, PPLS/D is compared against the basic PLS and a recently proposed PLS variant on the multiobjective unconstrained binary quadratic programming problems and the multiobjective traveling salesman problems with, at most, four objectives. The experimental results show that regardless of whether the initial solutions are randomly generated or generated by heuristic methods, PPLS/D always performs significantly better than the other two PLS variants.
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ISSN:2168-2267
2168-2275
2168-2275
DOI:10.1109/TCYB.2018.2880256