MOEA/D with guided local search: Some preliminary experimental results

Abstract

Multiobjective Evolutionary Algorithm based on Decomposition (MOEA/D) decomposes a multiobjective optimisation into a number of single-objective problem and optimises them in a collaborative manner. This paper investigates how to use the Guided Local Search (GLS), a well-studied single objective heuristic to enhance MOEA/D performance. In our proposed approach, the GLS applies to these subproblems to escape local Pareto optimal solutions. The experimental studies have shown that MOEA/D with GLS outperforms the classical MOEA/D on a bi-objective travelling salesman problem.