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Soft Computing

, Volume 20, Issue 4, pp 1389–1413 | Cite as

A particle swarm inspired cuckoo search algorithm for real parameter optimization

  • Xiangtao Li
  • Minghao Yin
Methodologies and Application

Abstract

The cuckoo search algorithm (CS) is a simple and effective global optimization algorithm. It has been successfully applied to solve a wide range of real-world optimization problems. In this paper, inspired by the particle swarm optimization (PSO), the proposed algorithm uses the best individuals among the entire population to enhance the convergence rate of the standard cuckoo search algorithm. While the PSO directly uses the global best solution of the population to determine new positions for the particles at the each iteration, agents of the CS do not directly use this information but the global best solution in the CS is stored at the each iteration. The global best solutions are used to add into the Information flow between the nest helps increase global and local search abilities of the new approach. Therefore, in the first component, the neighborhood information is added into the new population to enhance the diversity of the algorithm. In the second component, two new search strategies are used to balance the exploitation and exploration of the algorithm through a random probability rule. In other aspect, our algorithm has a very simple structure and thus is easy to implement. To verify the performance of PSCS, 30 benchmark functions chosen from literature are employed. The results show that the proposed PSCS algorithm clearly outperforms the basic CS and PSO algorithm. Compared with some evolution algorithms (CLPSO, CMA-ES, GL-25, DE, OXDE, ABC, GOABC, FA, FPA, CoDE, BA, BSA, BDS and SDS) from literature, experimental results indicate that the proposed algorithm performs better than, or at least comparable to state-of-the-art approaches from literature when considering the quality of the solution obtained. In the last part, experiments have been conducted on two real-world optimization problems including the spread spectrum radar poly-phase code design problem and the chaotic system. Simulation results demonstrate that the proposed algorithm is very effective.

Keywords

Cuckoo search algorithm Global numerical optimization  Particle swarm optimization Exploration Exploitation  Chaotic system 

Notes

Acknowledgments

This research is fully supported by Opening Fund of Top Key Discipline of Computer Software and Theory in Zhejiang Provincial Colleges at Zhejiang Normal University under Grant No. ZSDZZZZXK37 and the Fundamental Research Funds for the Central Universities Nos. 11CXPY010. Guangxi Natural Science Foundation (No. 2013GXNSFBA019263), Science and Technology Research Projects of Guangxi Higher Education (No.2013YB029), Scientific Research Foundation of Guangxi Normal University for Doctors.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.School of Computer Science and Information TechnologyNortheast Normal UniversityChangchunChina

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