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An Improved Harmony Search Algorithms Based on Particle Swarm Optimizer

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Intelligent Computing Theories and Technology (ICIC 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7996))

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Abstract

An improved harmony search algorithms based on particle swarm optimizer (HSPSO) is presented. The new heuristic optimization algorithm hybridizes HS and PSO, and it is based on the principles of those two methods with some differences. Comparisons with improved HS (IHS) , PSO algorithm (PSO), and it variants on a set of benchmark functions indicate that the HSPSO is capable of alleviating the problems of premature convergence.

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References

  1. Niu, B., Wang, H., Chai, Y.J.: Bacterial Colony Optimization. Discrete Dynamics in Nature and Society, 1–28 (2012)

    Google Scholar 

  2. Niu, B., Wang, H., Wang, J.W., Tan, L.J.: Multi-objective Bacterial Foraging Optimization. Neurocomputing (October 2012)

    Google Scholar 

  3. Geem, Z.W., Kim, J.H., Loganathan, G.V.: A New Heuristic Optimization Algorithm: Harmony Search. Simulation 76, 60–68 (2001)

    Article  Google Scholar 

  4. Lee, K.S., Geem, Z.W.: A New Meta-heuristic Algorithm for Continuous Engineering Optimization: Harmony Search Theory and Practice. Computer Methods in Applied Mechanics and Engineering 194, 3902–3933 (2005)

    Article  MATH  Google Scholar 

  5. Eberhart, R., Kennedy, J.: New Optimizer Using Particle Swarm Theory. In: Proceedings of the International Symposium on Micromechatronics and Human Science, pp. 39–43. IEEE, Piscataway (1995)

    Google Scholar 

  6. Coello, C.: Theoretical and Numerical Constraint-handling Techniques Used with Evolutionary Algorithms: A Survey of the State of The Art. Computer Methods in Applied Mechanics and Engineering 191, 1245–1287 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  7. Mahdavi, M., Fesanghary, M., Damangir, E.: An Improved Harmony Search Algorithm for Solving Optimization Problems. Applied Mathematics and Computation 188, 1567–1579 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  8. Shi, Y., Eberhart, R.: Empirical Study of Particle Swarm Optimization. In: Proceedings of the 1999 Congress on Evolutionary Computation, pp. 1945–1950. IEEE, Piscataway (1999)

    Google Scholar 

  9. Eberhart, R., Shi, Y.: Comparison between Genetic Algorithms and Particle Swarm Optimization. In: Porto, V.W., Waagen, D. (eds.) EP 1998. LNCS, vol. 1447, pp. 611–616. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

  10. Zhao, Z.Q., Glotin, H.: Diversifying Image Retrieval by Affinity Propagation Clustering on Visual Manifolds. IEEE Mutimedia 16, 34–43 (2009)

    Article  Google Scholar 

  11. Zhao, Z.Q.: A Novel Modular Neural Network for Imbalanced Classification Problems. Pattern Recognition Letters 30, 783–788 (2009)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. College of Management, Shenzhen University, Shenzhen, 518060, China

    Guangwei Song, Hongfei Yu, Ben Niu & Li Li

  2. Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, 230031, China

    Ben Niu

  3. Institute for Cultural Industries, Shenzhen University, Shenzhen, 518060, China

    Ben Niu

Authors
  1. Guangwei Song
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  2. Hongfei Yu
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  3. Ben Niu
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  4. Li Li
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Editor information

Editors and Affiliations

  1. Machine Learning and Systems Biology Laboratory, Tongji University, 4800 Caoan Road, 201804, Shanghai, China

    De-Shuang Huang

  2. School of Electrical Engineering, University of Ulsan, 680-749 #7-413, San 29, Muger Dong, Ulsan, South Korea

    Kang-Hyun Jo

  3. Guangxi University for Nationalities, 530006, Nanning, Guangxi, China

    Yong-Quan Zhou

  4. School of Computer Science and Engineering, Inha University, 402-751, Incheon, South Korea

    Kyungsook Han

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© 2013 Springer-Verlag Berlin Heidelberg

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Song, G., Yu, H., Niu, B., Li, L. (2013). An Improved Harmony Search Algorithms Based on Particle Swarm Optimizer. In: Huang, DS., Jo, KH., Zhou, YQ., Han, K. (eds) Intelligent Computing Theories and Technology. ICIC 2013. Lecture Notes in Computer Science(), vol 7996. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39482-9_70

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  • DOI: https://doi.org/10.1007/978-3-642-39482-9_70

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-39481-2

  • Online ISBN: 978-3-642-39482-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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