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Probabilistic opposition-based particle swarm optimization with velocity clamping

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Abstract

A probabilistic opposition-based Particle Swarm Optimization algorithm with Velocity Clamping and inertia weights (OvcPSO) is designed for function optimization—to accelerate the convergence speed and to optimize solution’s accuracy on standard benchmark functions. In this work, probabilistic opposition-based learning for particles is incorporated with PSO to enhance the convergence rate—it uses velocity clamping and inertia weights to control the position, speed and direction of particles to avoid premature convergence. A comprehensive set of 58 complex benchmark functions including a wide range of dimensions have been used for experimental verification. It is evident from the results that OvcPSO can deal with complex optimization problems effectively and efficiently. A series of experiments have been performed to investigate the influence of population size and dimensions upon the performance of different PSO variants. It also outperforms FDR-PSO, CLPSO, FIPS, CPSO-H and GOPSO on various benchmark functions. Last but not the least, OvcPSO has also been compared with opposition-based differential evolution (ODE); it outperforms ODE on lower swarm population and higher-dimensional functions.

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Notes

  1. The feasibility of using position-based Learning, velocity clamping and decreasing inertia weights with PSO is investigated by the authors in a preliminary pilot study given in [27].

  2. The terms opposite number probability and Jumping Rate have been used interchangeably in this paper.

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Authors and Affiliations

  1. Next Generation Intelligent Networks Research Center, FAST-National University of Computer and Emerging Sciences, Islamabad, 44000, Pakistan

    Farrukh Shahzad

  2. Center for Advanced Research in Engineering (CARE), Islamabad, 44000, Pakistan

    Sohail Masood

  3. FAST-National University of Computer and Emerging Scinces, Islamabad, 44000, Pakistan

    Naveed Kazim Khan

Authors
  1. Farrukh Shahzad
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  2. Sohail Masood
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  3. Naveed Kazim Khan
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Correspondence to Farrukh Shahzad.

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Shahzad, F., Masood, S. & Khan, N.K. Probabilistic opposition-based particle swarm optimization with velocity clamping. Knowl Inf Syst 39, 703–737 (2014). https://doi.org/10.1007/s10115-013-0624-z

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  • DOI: https://doi.org/10.1007/s10115-013-0624-z

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