Abstract
In this paper we present an evolutionary strategy for the multidimensional 0–1 knapsack problem. Our algorithm incorporates a flipping local search process in order to locally improve the obtained individuals and also, a heuristic operator which computes problem-specific knowledge, based on the surrogate multipliers approach introduced in [12]. Experimental results show that our evolutionary algorithm is capable of obtaining high quality solutions for large size problems and that the local search procedure significatively improves the final obtained result.
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Alonso, C.L., Caro, F., Montaña, J.L.: An Evolutionary Strategy for the Multidimensional 0–1 Knapsack Problem based on Genetic Computation of Surrogate Multipliers. In: Mira, J., Álvarez, J.R. (eds.) IWINAC 2005. LNCS, vol. 3562, pp. 63–73. Springer, Heidelberg (2005)
Balas, E., Martin, C.H.: Pivot and Complement–A Heuristic for 0–1 Programming. Management Science 26(1), 86–96 (1980)
Balas, E., Zemel, E.: An Algorithm for Large zero–one Knapsack Problems. Operations Research 28, 1130–1145 (1980)
Beasley, J.E.: Obtaining Test Problems via Internet. Journal of Global Optimization 8, 429–433 (1996)
Chu, P.C., Beasley, J.E.: A Genetic Algorithm for the Multidimensional Knapsack Problem. Journal of Heuristics 4, 63–86 (1998)
Freville, A., Plateau, G.: Heuristics and Reduction Methods for Multiple Constraints 0–1 Linear Programming Problems. Europena Journal of Operationa Research 24, 206–215 (1986)
Gavish, B., Pirkul, H.: Allocation of Databases and Processors in a Distributed Computing System. In: Akoka, J. (ed.) Management od Distributed Data Processing, North-Holland, pp. 215–231. North-Holland, Amsterdam (1982)
Gavish, B., Pirkul, H.: Efficient Algorithms for Solving Multiconstraint Zero–One Knapsack Problems to Optimality. Mathematical Programming 31, 78–105 (1985)
Glover, F., Kochenberger, G.A.: Critical event tabu search for multidimensional knapsack problems. In: Metaheuristics: The Theory and Applications, pp. 407–427. Kluwer Academic Publishers, Dordrecht (1996)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading (1989)
Khuri, S., Bäck, T., Heitkötter, J.: The Zero/One Multiple Knapsack Problem and Genetic Algorithms. In: Proceedings of the 1994 ACM Symposium on Applied Computing (SAC 1994), pp. 188–193. ACM Press, New York (1994)
Pirkul, H.: A Heuristic Solution Procedure for the Multiconstraint Zero–One Knapsack Problem. Naval Research Logistics 34, 161–172 (1987)
Raidl, G.R.: An Improved Genetic Algorithm for the Multiconstraint Knapsack Problem. In: Proceedings of the 5th IEEE International Conference on Evolutionary Computation, pp. 207–211 (1998)
Rinnooy Kan, A.H.G., Stougie, L., Vercellis, C.: A Class of Generalized Greedy Algorithms for the Multi-knapsack Problem. Discrete Applied Mathematics 42, 279–290 (1993)
Thiel, J., Voss, S.: Some Experiences on Solving Multiconstraint Zero–One Knapsack Problems with Genetic Algorithms. INFOR 32, 226–242 (1994)
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Alonso, C.L., Caro, F., Montaña, J.L. (2006). A Flipping Local Search Genetic Algorithm for the Multidimensional 0-1 Knapsack Problem. In: Marín, R., Onaindía, E., Bugarín, A., Santos, J. (eds) Current Topics in Artificial Intelligence. CAEPIA 2005. Lecture Notes in Computer Science(), vol 4177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881216_3
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DOI: https://doi.org/10.1007/11881216_3
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