Swipe to navigate through the articles of this issue
Communicated by V. Loia.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This paper proposes an idea of using well studied and documented single-objective optimization methods in multiobjective evolutionary algorithms. It develops a hybrid algorithm which combines the multiobjective evolutionary algorithm based on decomposition (MOEA/D) with guided local search (GLS), called MOEA/D-GLS. It needs to optimize multiple single-objective subproblems in a collaborative way by defining neighborhood relationship among them. The neighborhood information and problem-specific knowledge are explicitly utilized during the search. The proposed GLS alternates among subproblems to help escape local Pareto optimal solutions. The experimental results have demonstrated that MOEA/D-GLS outperforms MOEA/D on multiobjective traveling salesman problems.
Please log in to get access to this content
To get access to this content you need the following product:
Alhindi A, Zhang Q (2014) MOEA/D with Tabu search for multiobjective permutation flow shop scheduling problems. In: Proceedings of the IEEE world congress on evolutionary computation (CEC). IEEE, Beijing, pp 1155–1164
Alhindi A, Zhang Q, Tsang E (2014) Hybridisation of decomposition and GRASP for combinatorial multiobjective optimisation. The 14th UK workshop on computational intelligence (UKCI). IEEE, Bradford, pp 1155–1164
Alsheddy A (2011) Empowerment scheduling : a multi-objective optimization approach using guided local search. Ph.D thesis, University of Essex, Essex
Alsheddy A, Tsang E (2010) Guided pareto local search based frameworks for biobjective optimization. In: IEEE congress on evolutionary computation (CEC). IEEE, Shanghai, pp 1–8
Hansen MP, Jaszkiewicz A (1998) Evaluating the quality of approximations to the non-dominated set. Technical University of Denmark, Department of Mathematical Modelling, Lyngby
Ke L, Zhang Q, Battiti R (2013) MOEA/D-ACO: a multiobjective evolutionary algorithm using decomposition and ant colony. IEEE Trans Cybern 43(6):1845–1959 CrossRef
Li H, Landa-Silva D (2011) An adaptive evolutionary multi-objective approach based on simulated annealing. Evolut Comput 19(4):561–595 CrossRef
Oliver I, Smith D, Holland J (1987) A study of permutation crossover operators on the TSP, genetic algorithms and their applications. In: Proceedings of the second international conference on genetic algorithms, Hillsdale, pp 224–230
Tairan N, Zhang Q (2010) Population-based guided local search: some preliminary experimental results. In: IEEE congress on evolutionary computation (CEC). IEEE, Shanghai, pp 1–5
Tairan N, Zhang Q (2011) P-GLS-II: an enhanced version of the population-based guided local search. In: Proceedings of the 13th annual conference on genetic and evolutionary computation. ACM, Dublin, pp 537–544
Voudouris C (1997) Guided local search for combinatorial optimisation problems. Ph.D thesis, University of Essex, Essex
Voudouris C, Tsang E, Alsheddy A (2010) Guided local search. In: Handbook of metaheuristics, Springer, pp 321–361
Zhang Q, Li H (2007) MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans Evolut Comput 11(6):712–731 CrossRef
- MOEA/D-GLS: a multiobjective memetic algorithm using decomposition and guided local search
- Publication date
- Springer Berlin Heidelberg
Neuer Inhalt/© ITandMEDIA