Abstract
In this paper, we present an artificial bee colony (ABC) algorithm for the 0-1 Multidimensional Knapsack Problem (MKP_01). The objective of MKP_01 is to find a subset of a given set of n objects in such a way that the total profit of the objects included in the subset is maximized, while a set of knapsack constraints remains satisfied. The ABC algorithm is a new metaheuristic technique based on the intelligent foraging behavior of honey bee swarms. Heuristic-based repair operators and local search are incorporated into our ABC algorithm. Computational results demonstrate that our ABC algorithm not only produces better results but converges very rapidly in comparison with other swarm-based approaches.
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References
Chu, P.C., Beasley, J.E.: A Genetic Algorithm for the Multidimentional Knapsack Problem. Journal of Heuristic 4, 63–86 (1998)
Kong, M., Tian, P., Kao, Y.: A New Ant Colony Optimization Algorithm for the Multidimensional Knapsack Problem. Computers and Operations Research 35, 2672–2683 (2008)
Alaya, I., Solnon, C., Ghéira, K.: Ant Algorithm for the Multi-Dimensional Knapsack Problem. In: International Conference on Bio-Inspired Optimization Methods and Their Applications (BIOMA 2004), pp. 63–72 (2004)
Fidanova, S.: Evolutionary Algorithm for Multidimensional Knapsack Problem. In: PPSN VII Workshop (2002)
Leguizamon, G., Michalewicz, Z.: A New Version of Ant System for Subset Problem. In: Congress on Evolutionary Computation, pp. 1459–1464 (1999)
Karaboga, D.: An Idea Based on Honey Bee Swarm for Numerical Optimization. Technical Report TR06, Computer Engineering Department, Erciyes University, Turkey (2005)
Basturk, B., Karaboga, D.: An Artificial Bee Colony (ABC) Algorithm for Numeric Function Optimization. In: Proceedings of the IEEE Swarm Intelligence Symposium, Indianapolis, USA, May 12-14 (2006)
Basturk, B., Karaboga, D.: A Powerful and Efficient Algorithm for Numeric Function Optimization: Artificial Bee Colony (ABC) algorithm. Journal of Global Optimization 39, 459–471 (2007)
Basturk, B., Karaboga, D.: Artificial Bee Colony (ABC) Optimization Algorithm for Solving Constrained Optimization Problems. In: Melin, P., Castillo, O., Aguilar, L.T., Kacprzyk, J., Pedrycz, W. (eds.) IFSA 2007. LNCS (LNAI), vol. 4529, pp. 789–798. Springer, Heidelberg (2007)
Karaboga, D., Basturk, B.: On the Performance of Artificial Bee Colony (ABC) Algorithm. Applied Soft Computing 8, 687–697 (2008)
Singh, A.: An Artificial Bee Colony (ABC) Algorithm for the Leaf-Constrained Minimum Spanning Tree Problem. Applied Soft Computing 9, 625–631 (2009)
Pirkul, H.: A Heuristic Solution Procedure for the Multiconstraint Zero–One Knapsack Problem. Naval Research Logistics 34, 61–72 (1987)
Martello, S., Toth, P.: Knapsack Problems: Algorithms and Computer Implementations. Wiley, Chichester (1990)
Singh, A., Gupta, A.K.: Two Heuristics for the One-Dimensional Bin-Packing Problem. OR Spectrum 29, 765–781 (2007)
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Sundar, S., Singh, A., Rossi, A. (2010). An Artificial Bee Colony Algorithm for the 0–1 Multidimensional Knapsack Problem. In: Ranka, S., et al. Contemporary Computing. IC3 2010. Communications in Computer and Information Science, vol 94. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14834-7_14
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DOI: https://doi.org/10.1007/978-3-642-14834-7_14
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