MOEA/D with Tabu Search for Multiobjective Permutation Flow Shop Scheduling Problems

Abstract

Multiobjective Evolutionary Algorithm based on Decomposition (MOEA/D) decomposes a multiobjective 
optimisation problem into a number of single-objective problems and optimises them in a collaborative 
manner. This paper investigates how to use Tabu Search (TS), a well-studied single objective heuristic 
to enhance MOEA/D performance. In our proposed approach, the TS is applied to these subproblems 
with the aim to escape from local optimal solutions. The experimental studies have shown that MOEA/D 
with TS outperforms the classical MOEA/D on multiobjective permutation flow shop scheduling problems. 
It also have demonstrated that use of problem specific knowledge can significantly improve the algorithm 
performance.