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An integrated method of particle swarm optimization and differential evolution

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Abstract

Particle swarm optimization (PSO) and differential evolution (DE) have their similarities and compatibility in the design update process, such that a new design vector is determined by using neighborhood designs under algorithm control parameters. The paper deals with an integrated method of a hybrid PSO (HPSO) algorithm combined with DE in order to refine the optimization performance. PSO and DE also possess common characteristics compared with genetic algorithm (GA). The crossover- and mutation-like operators are suggested in the HPSO. A bounce back method is also implemented to prevent the design from locating out of design spaces during the optimization process. For the purpose of further enhancing the search capabilities, such HPSO is combined with the Q-learning that is one of efficient reinforcement learning methods. Using a number of nonlinear multimodal functions and engineering optimization problems, the proposed algorithms of HPSO and HPSO with Q-learning are compared with PSO DE and GA.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Yonsei University, Seoul, 120-749, Korea

    Pyungmo Kim

  2. School of Mechanical Engineering, Yonsei University, Seoul, 120-749, Korea

    Jongsoo Lee

Authors
  1. Pyungmo Kim
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  2. Jongsoo Lee
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Correspondence to Jongsoo Lee.

Additional information

This paper was recommended for publication in revised form by Associate Editor Tae Hee Lee

Jongsoo Lee received a B.S. degree in Mechanical Engineering from Yonsei University in 1988. He then went on to receive his M.S. degree from University of Minnesota in 1992 and Ph.D. degree from Rensselaer Polytechnic Institute in 1996. Dr. Lee is currently a Professor at the School of Mechanical Engineering at Yonsei University in Seoul, Korea. He is currently serving as a committee member of the division of CAE and Applied Mechanics in the Korean Society of Mechanical Engineers. Dr. Lee’s research interests are in the area of engineering design optimization, fluidstructure interactions, and reliability based robust product design.

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Kim, P., Lee, J. An integrated method of particle swarm optimization and differential evolution. J Mech Sci Technol 23, 426–434 (2009). https://doi.org/10.1007/s12206-008-0917-4

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  • DOI: https://doi.org/10.1007/s12206-008-0917-4

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