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Applied Intelligence

, Volume 46, Issue 3, pp 684–702 | Cite as

A complex-valued encoding wind driven optimization for the 0-1 knapsack problem

Article

Abstract

This paper presents a complex-valued encoding wind driven optimization (CWDO) with a greedy strategy for the 0-1 knapsack problem. We introduce a complex-value encoding method, which can be viewed as an effective global optimization strategy, and a greedy strategy, which can be viewed as an enhanced local search strategy into wind driven optimization. These strategies increase the diversity of the population and avoid premature convergence. This paper presents three types of test cases for evaluation: standard, small-scale, and large-scale test cases. The experimental results show that the proposed algorithm is suitable for these three cases. Compared to the complex valued cuckoo search algorithm, greedy genetic algorithm, wind driven optimization, binary cuckoo search algorithm, bat algorithm and particle swarm optimization, the performance, stability, and robustness of the CWDO algorithm is better. The simulation results show that the CWDO algorithm is an effective and feasible method for solving the 0-1 knapsack problem.

Keywords

Complex-valued encoding Wind driven optimization (WDO) Greedy strategy Knapsack problem 

Notes

Acknowledgments

The authors would like to thank the anonymous reviewers for their careful review and constructive comments. This work was supported by National Science Foundation of China (Grant No. 61463007, 61563008).

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Yongquan Zhou
    • 1
    • 2
  • Zongfan Bao
    • 1
  • Qifang Luo
    • 1
    • 2
  • Sen Zhang
    • 1
  1. 1.College of Information Science and EngineeringGuangxi University for NationalitiesNanningChina
  2. 2.Schools Key Laboratory of Guangxi Highs Complex System and Computational IntelligenceNanningChina

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