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Particle Swarm Optimization and Differential Evolution Algorithms: Technical Analysis, Applications and Hybridization Perspectives

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Advances of Computational Intelligence in Industrial Systems

Part of the book series: Studies in Computational Intelligence ((SCI,volume 116))

Summary

Since the beginning of the nineteenth century, a significant evolution in optimization theory has been noticed. Classical linear programming and traditional non-linear optimization techniques such as Lagrange’s Multiplier, Bellman’s principle and Pontyagrin’s principle were prevalent until this century. Unfortunately, these derivative based optimization techniques can no longer be used to determine the optima on rough non-linear surfaces. One solution to this problem has already been put forward by the evolutionary algorithms research community. Genetic algorithm (GA), enunciated by Holland, is one such popular algorithm. This chapter provides two recent algorithms for evolutionary optimization – well known as particle swarm optimization (PSO) and differential evolution (DE). The algorithms are inspired by biological and sociological motivations and can take care of optimality on rough, discontinuous and multimodal surfaces. The chapter explores several schemes for controlling the convergence behaviors of PSO and DE by a judicious selection of their parameters. Special emphasis is given on the hybridizations of PSO and DE algorithms with other soft computing tools. The article finally discusses the mutual synergy of PSO with DE leading to a more powerful global search algorithm and its practical applications.

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Authors and Affiliations

  1. Department of Electronics and Telecommunication Engineering, Jadavpur University, Kolkata, 700032, India

    Swagatam Das & Amit Konar

  2. Center of Excellence for Quantifiable Quality of Service, Norwegian University of Science and Technology, Norway

    Ajith Abraham

Authors
  1. Swagatam Das
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  2. Ajith Abraham
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  3. Amit Konar
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Editor information

Editors and Affiliations

  1. Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China

    Ying Liu

  2. School of Computer Engineering, Nanyang Technological University, Nanyang Avenue, Singapore, 639798

    Aixin Sun  & Ee-Peng Lim  & 

  3. Department of Mechanical Engineering, National University of Singapore, 9 Engineering Drive 1, Singapore, 117576

    Han Tong Loh  & Wen Feng Lu  & 

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Das, S., Abraham, A., Konar, A. (2008). Particle Swarm Optimization and Differential Evolution Algorithms: Technical Analysis, Applications and Hybridization Perspectives. In: Liu, Y., Sun, A., Loh, H.T., Lu, W.F., Lim, EP. (eds) Advances of Computational Intelligence in Industrial Systems. Studies in Computational Intelligence, vol 116. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78297-1_1

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  • DOI: https://doi.org/10.1007/978-3-540-78297-1_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-78296-4

  • Online ISBN: 978-3-540-78297-1

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