PPLS/D: Parallel Pareto Local Search based on Decomposition

TL;DR

This paper introduces PPLS/D, an enhanced parallel and decomposed version of Pareto Local Search that significantly improves efficiency and solution quality for multiobjective combinatorial optimization problems.

Contribution

PPLS/D combines parallel processing and problem decomposition with scalar objective guidance, offering a novel and more effective approach to multiobjective local search.

Findings

PPLS/D outperforms basic PLS and 2PPLS in experiments.

PPLS/D is effective with both random and heuristic initial solutions.

Experimental results show significant performance improvements.

Abstract

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.