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PPLS/D: Parallel Pareto Local Search Based on Decomposition | IEEE Journals & Magazine | IEEE Xplore

PPLS/D: Parallel Pareto Local Search Based on Decomposition


Abstract:

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...Show More

Abstract:

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.
Published in: IEEE Transactions on Cybernetics ( Volume: 50, Issue: 3, March 2020)
Page(s): 1060 - 1071
Date of Publication: 29 November 2018

ISSN Information:

PubMed ID: 30507544

Funding Agency:


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