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Integrated the Simplified Interpolation and Clonal Selection into the Particle Swarm Optimization for Optimization Problems

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Simulated Evolution and Learning (SEAL 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4247))

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Abstract

Particle Swarm Optimization (PSO) is gaining momentum as a simple and effective optimization technique. However, its performance on complex problem with multiple minima falls short of that of the Ant Clony Optimization (ACO) algorithm. The new algorithm, which we call Hybrid Particle Swarm Optimization, combines the ideas of particle swarm optimizati-on with clonal selection strategy and simplified quadratic interpolation (SQI), which is used to improve its local search ability, and to escape from the local optima. Simulation results on 14 benchmark test functions show that the hybrid algorithm is able to avoid the premature convergence and find much better solutions with high speed.

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Author information

Authors and Affiliations

  1. Institute of Intelligent Information Processing and National Key Laboratory for Radar Signal Processing, Xidian University, Xi’an, Shaanxi, 710071, China

    Jing Wang, Xiaohua Zhang & Licheng Jiao

Authors
  1. Jing Wang
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  2. Xiaohua Zhang
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  3. Licheng Jiao
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Editor information

Editors and Affiliations

  1. Department of Industrial Engineering and Management, Cheng Shiu University, Kaohsiung County Taiwan, ROC

    Tzai-Der Wang

  2. School of Computer Science and information Technology, RMIT University, VIC 3001, Melbourne, Australia

    Xiaodong Li

  3. AI-ECON Research Center, Department of Economics, National Chengchi University, 11623, Taipei, Taiwan

    Shu-Heng Chen

  4. Department of Computer Science and Technology, University of Sci. & Tech. of China, 230026, Hefei, Anhui, P.R. China

    Xufa Wang

  5. School of Information Technology and Electrical Engineering, University of New South Wales, Australian Defence Force Academy, Canberra, Australia

    Hussein Abbass

  6. The Univertity of Tokyo, Japan

    Hitoshi Iba

  7. Department of Computer, University of Science and Technology of China, 230027, Hefei, China

    Guo-Liang Chen

  8. University of Birmingham, Birmingham, UK

    Xin Yao

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© 2006 Springer-Verlag Berlin Heidelberg

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Wang, J., Zhang, X., Jiao, L. (2006). Integrated the Simplified Interpolation and Clonal Selection into the Particle Swarm Optimization for Optimization Problems. In: Wang, TD., et al. Simulated Evolution and Learning. SEAL 2006. Lecture Notes in Computer Science, vol 4247. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11903697_55

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  • DOI: https://doi.org/10.1007/11903697_55

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-47331-2

  • Online ISBN: 978-3-540-47332-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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