  Pareto Local Search (PLS) is a basic building block in many metaheuristics
for Multiobjective Combinatorial Optimization Problem (MCOP). In this paper, an
enhanced PLS variant called Parallel Pareto Local Search 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 Pareto Local Search (2PPLS) 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 (mUBQPs) and the multiobjective Traveling
Salesman Problems (mTSPs) with at most four objectives. The experimental
results show that, no matter whether the initial solutions are randomly
generated or generated by heuristic methods, PPLS/D always performs
significantly better than the other two PLS variants.
