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Pareto Local Search (PLS) is a basic building block in many metaheuristicsfor Multiobjective Combinatorial Optimization Problem (MCOP). In this paper, anenhanced PLS variant called Parallel Pareto Local Search based on Decomposition(PPLS/D) is proposed. PPLS/D improves the efficiency of PLS using thetechniques of parallel computation and problem decomposition. It decomposes theoriginal search space into L subregions and executes L parallel processessearching in these subregions simultaneously. Inside each subregion, the PPLS/Dprocess is guided by a unique scalar objective function. PPLS/D differs fromthe well-known Two Phase Pareto Local Search (2PPLS) in that it uses the scalarobjective function to guide every move of the PLS procedure in a fine-grainedmanner. In the experimental studies, PPLS/D is compared against the basic PLSand a recently proposed PLS variant on the multiobjective Unconstrained BinaryQuadratic Programming problems (mUBQPs) and the multiobjective TravelingSalesman Problems (mTSPs) with at most four objectives. The experimentalresults show that, no matter whether the initial solutions are randomlygenerated or generated by heuristic methods, PPLS/D always performssignificantly better than the other two PLS variants.

PPLS/D: Parallel Pareto Local Search based on Decomposition