Table of Contents Author Guidelines Submit a Manuscript
Computational Intelligence and Neuroscience
Volume 2014 (2014), Article ID 970456, 9 pages
http://dx.doi.org/10.1155/2014/970456
Research Article

An Improved Hybrid Encoding Cuckoo Search Algorithm for 0-1 Knapsack Problems

1School of Information Engineering, Shijiazhuang University of Economics, Shijiazhuang 050031, China
2School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China

Received 31 August 2013; Revised 4 December 2013; Accepted 16 December 2013; Published 12 January 2014

Academic Editor: Christian W. Dawson

Copyright © 2014 Yanhong Feng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Cuckoo search (CS) is a new robust swarm intelligence method that is based on the brood parasitism of some cuckoo species. In this paper, an improved hybrid encoding cuckoo search algorithm (ICS) with greedy strategy is put forward for solving 0-1 knapsack problems. First of all, for solving binary optimization problem with ICS, based on the idea of individual hybrid encoding, the cuckoo search over a continuous space is transformed into the synchronous evolution search over discrete space. Subsequently, the concept of confidence interval (CI) is introduced; hence, the new position updating is designed and genetic mutation with a small probability is introduced. The former enables the population to move towards the global best solution rapidly in every generation, and the latter can effectively prevent the ICS from trapping into the local optimum. Furthermore, the greedy transform method is used to repair the infeasible solution and optimize the feasible solution. Experiments with a large number of KP instances show the effectiveness of the proposed algorithm and its ability to achieve good quality solutions.