MOEA/D with Tabu Search for multiobjective permutation flow shop scheduling problems
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.
2014 IEEE Congress on Evolutionary Computation (CEC)