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Complex System Modelling and Control Through Intelligent Soft Computations pp 45–86Cite as

A Hybrid Global Optimization Algorithm: Particle Swarm Optimization in Association with a Genetic Algorithm

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Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 319))

Abstract

The genetic algorithm (GA) is an evolutionary optimization algorithm operating based upon reproduction, crossover and mutation. On the other hand, particle swarm optimization (PSO) is a swarm intelligence algorithm functioning by means of inertia weight, learning factors and the mutation probability based upon fuzzy rules. In this paper, particle swarm optimization in association with genetic algorithm optimization is utilized to gain the unique benefits of each optimization algorithm. Therefore, the proposed hybrid algorithm makes use of the functions and operations of both algorithms such as mutation, traditional or classical crossover, multiple-crossover and the PSO formula. Selection of these operators is based on a fuzzy probability. The performance of the hybrid algorithm in the case of solving both single-objective and multi-objective optimization problems is evaluated by utilizing challenging prominent benchmark problems including FON, ZDT1, ZDT2, ZDT3, Sphere, Schwefel 2.22, Schwefel 1.2, Rosenbrock, Noise, Step, Rastrigin, Griewank, Ackley and especially the design of the parameters of linear feedback control for a parallel-double-inverted pendulum system which is a complicated, nonlinear and unstable system. Obtained numerical results in comparison to the outcomes of other optimization algorithms in the literature demonstrate the efficiency of the hybrid of particle swarm optimization and genetic algorithm optimization with regard to addressing both single-objective and multi-objective optimization problems.

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Acknowledgments

The authors would like to thank the anonymous reviewers for their valuable suggestions that enhance the technical and scientific quality of this paper.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Faculty of Engineering, The University of Guilan, Rasht, Iran

    M. Andalib Sahnehsaraei

  2. Department of Mechanical Engineering, Sirjan University of Technology, Sirjan, Iran

    M. J. Mahmoodabadi & S. M. Mortazavi Yazdi

  3. Department of Mechanical Engineering, The University of Texas at San Antonio, San Antonio, TX, 78249, USA

    M. Taherkhorsandi & K. K. Castillo-Villar

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  1. M. Andalib Sahnehsaraei
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  2. M. J. Mahmoodabadi
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Correspondence to M. Taherkhorsandi .

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  1. Department of Engineering Design and Mathematics, University of the West of England, Bristol, United Kingdom

    Quanmin Zhu

  2. Faculty of Computers and Information, Benha University, Benha, Egypt

    Ahmad Taher Azar

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Andalib Sahnehsaraei, M., Mahmoodabadi, M.J., Taherkhorsandi, M., Castillo-Villar, K.K., Mortazavi Yazdi, S.M. (2015). A Hybrid Global Optimization Algorithm: Particle Swarm Optimization in Association with a Genetic Algorithm. In: Zhu, Q., Azar, A. (eds) Complex System Modelling and Control Through Intelligent Soft Computations. Studies in Fuzziness and Soft Computing, vol 319. Springer, Cham. https://doi.org/10.1007/978-3-319-12883-2_2

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