International Journal of Information Engineering and Electronic Business(IJIEEB)

ISSN: 2074-9023 (Print), ISSN: 2074-9031 (Online)

Published By: MECS Press

IJIEEB Vol.5, No.4, Oct. 2013

A Multi-objective Binary Cuckoo Search for Bi-criteria Knapsack Problem

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Abdesslem Layeb,Nesrine Lahouesna,Bouchra Kireche

Index Terms

Combinatorial optimization;Evolutionary computation;Cuckoo Search;Binary Cuckoo Search;knapsack problem;multi-objective optimization


Cuckoo Search (CS) is one of the most recent population-based metaheuristics. CS algorithm is based on the cuckoo’s behavior and the mechanism of Lévy flights. The Binary Cuckoo Search algorithm (BCS) is new discrete version used to solve binary optimization problem based on sigmoid function. In this paper, we propose a new cuckoo search for binary multiobjective optimization. Pareto dominance is used to find optimal pareto solutions. Computational results on some bi-criteria knapsack instances show the effectiveness of the proposed algorithm and its ability to achieve good quality solutions.

Cite This Paper

Abdesslem Layeb, Nesrine Lahouesna, Bouchra Kireche ,"A Multi-objective Binary Cuckoo Search for Bi-criteria Knapsack Problem", IJIEEB, vol.5, no.4, pp.8-15, 2013. DOI: 10.5815/ijieeb.2013.04.02


[1]A. Warburton, Approximation of Pareto optima in multiple-objective, shortest-path problems, Operations Research, Vol.35, No.1, pp.70–79, 1987. 

[2]K. Deb et al., A Fast Elitist Non-Dominated Sorting Genetic Algorithm for Multi-Objective Optimization: NSGA-II, Proceedings of the Parallel Problem Solving from Nature. Springer Lecture Notes in Computer Science No. 1917, pp. 849-858 Paris, France, 2000.

[3]M.R. Sierra and C.A.C. Coello, Multi-objective particle swarm optimizers: A survey of the state-of-the-art, International Journal of Computational Intelligence Research , Vol. 2, No. 3, pp.287–308, 2006 

[4]R. C. Eberhart, Y.Shi, and J. Kennedy, Swarm Intelligence. The Morgan Kaufmann Series in Artificial Intelligence. Morgan Kaufmann, San Francisco, CA, USA, 2001.

[5]X.-S.Yang, and S. Deb, Engineering Optimisation by Cuckoo Search, Int. J. Mathematical Modelling and Numerical Optimisation, Vol. 1, No. 4, pp. 330–343, 2010.

[6]P. Barthelemy, J. Bertolotti, D. S. Wiersma, A Lévy flight for light. Nature, Vol. 453, pp. 495-498, 2008.

[7]R. B. Payne, M. D. Sorenson, and K. Klitz, The Cuckoos, Oxford University Press, 2005.

[8]I. Pavlyukevich, Lévy flights, non-local search and simulated annealing, J. Computational Physics, Vol. 226, pp. 1830-1844, 2007.

[9]A., Gherboudj, A. Layeb, and S. Chikhi, Solving 0-1 knapsack problems by a discrete binary version of cuckoo search algorithm, in the International Journal of Bio-Inspired Computation, Vol. 4, No. 4, pp. 229-236, 2012.

[10]D. Pisinger, Where are the hard knapsack problems, Computers and Operations Research, Vol.32, N°. 9, pp. 2271-2284, 2005.

[11]C. Bazgan, H. Hugot, and D.Vanderpooten, “Solving efficiently the 0 – 1 multiobjective knapsack problem,” Computers and Operations Research,Vol. 36, No.1, pp. 260–279, 2009. 

[12]A. Layeb, A novel quantum inspired cuckoo search for knapsack problems, Int. J. Bio-Inspired Computation, Vol. 3, No. 5, pp 297-305, 2011.

[13]M. Dhivya, Energy Efficient Computation of Data Fusion in Wireless Sensor Networks Using Cuckoo Based Particle Approach, Int. J. of Communications, Network and System Sciences, Vol. 4, No. 4, pp. 249-255, 2011.