EB-GLS: an improved guided local search based on the big valley structure
Local search is a basic building block in memetic algorithms. Guided local search (GLS) can improve the efficiency of local search. By changing the guide function, GLS guides a local search to escape from locally optimal solutions and find better solutions. The key component of GLS is its penalizing mechanism which determines which feature is selected to penalize when the search is trapped in a locally optimal solution. The original GLS penalizing mechanism only makes use of the cost and the current penalty value of each feature. It is well known that many combinatorial optimization problems have a big valley structure, i.e., the better a solution is, the more the chance it is closer to a globally optimal solution. This paper proposes to use big valley structure assumption to improve the GLS penalizing mechanism. An improved GLS algorithm called elite biased GLS (EB-GLS) is proposed. EB-GLS records and maintains an elite solution as an estimate of the globally optimal solutions, and reduces the chance of penalizing the features in this solution. We have systematically tested the proposed algorithm on the symmetric traveling salesman problem. Experimental results show that EB-GLS is significantly better than GLS.
KeywordsCombinatorial optimization Metaheuristics Traveling salesman problem Guided local search Elitism
The work described in this paper was supported by a grant from ANR/RCC Joint Research Scheme sponsored by the Research Grants Council of the Hong Kong Special Administrative Region, China and France National Research Agency (Project No. A-CityU101/16).
- 1.Alhindi A, Zhang Q (2013) MOEA/D with guided local search: some preliminary experimental results. In: Computer science and electronic engineering conference (CEEC), 2013 5th, IEEE, pp 109–114Google Scholar
- 2.Alsheddy A, Tsang E (2011) Empowerment scheduling for a field workforce. J Sched 14(6):639–654Google Scholar
- 3.Basharu M, Arana I, Ahriz H (2005) Distributed guided local search for solving binary DisCSPs. In: FLAIRS conference, pp 660–665Google Scholar
- 5.Boese KD (1995) Cost versus distance in the traveling salesman problem. UCLA Computer Science Department, Los AngelesGoogle Scholar
- 12.Jones T (1995) Evolutionary algorithms, fitness landscapes and search. Ph.D. Thesis, CiteseerGoogle Scholar
- 13.Jones T, Forrest S (1995) Fitness distance correlation as a measure of problem difficulty for genetic algorithms. In: Eshelman LJ (ed) Proceedings of the 6th international conference on genetic algorithms. Morgan Kaufmann, San Francisco, CA, pp 184–192Google Scholar
- 14.Kauffman SA (1993) The origins of order: self-organization and selection in evolution. Oxford University Press, OxfordGoogle Scholar
- 16.Lourenço HR, Martin OC, Stützle T (2010) Iterated local search: framework and applications. In: Handbook of metaheuristics, Springer, pp 363–397Google Scholar
- 23.Ochoa G, Veerapen N (2016) Deconstructing the big valley search space hypothesis. In: Evolutionary computation in combinatorial optimization, Springer, pp 58–73Google Scholar
- 24.Oliveira SM, Hussin MS, Stützle T, Roli A, Dorigo M (2011) A detailed analysis of the population-based ant colony optimization algorithm for the TSP and the QAP. In: Proceedings of the 13th annual conference companion on genetic and evolutionary computation, ACM, pp 13–14Google Scholar
- 27.Tao X, Haubrich HJ (2005) A hybrid metaheuristic method for the planning of medium-voltage power distribution systemsGoogle Scholar
- 31.Voudouris C, Tsang E, Alsheddy A (2010) Guided local search. In: Handbook of metaheuristics, Springer, pp 321–361Google Scholar