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Hidden Markov Model Classifier for the Adaptive Particle Swarm Optimization

  • Oussama Aoun
  • Malek SarhaniEmail author
  • Abdellatif El Afia
Chapter
Part of the Operations Research/Computer Science Interfaces Series book series (ORCS, volume 62)

Abstract

Particle swarm optimization (PSO) is a stochastic algorithm based population that integrates social interactions of animals in nature. Adaptive Particle swarm optimization (APSO) as an amelioration of the original one, improve the performance of global search and gives better efficiency. The APSO defines four evolutionary states: exploration, exploitation, convergence, and jumping out. According to the state, the inertia weight and acceleration coefficients are controlled. In this paper, we integrate Hidden Markov Model Particle swarm optimization (HMM) in APSO to have a stochastic state classification at each iteration. Furthermore, to tackle the problem of the dynamic environment during iterations, an additional online learning for HMM parameters is integrated into the algorithm using online Expectation-Maximization algorithm. We performed evaluations on ten benchmark functions to test the HMM integration inside APSO. Experimental results show that our proposed scheme outperforms other PSO variants in major cases regarding solution accuracy and specially convergence speed.

Keywords

Particle swarm optimization Swarm intelligence Hidden Markov model Machine learning Parameters adaptation Metaheuristics control 

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Oussama Aoun
    • 1
  • Malek Sarhani
    • 1
    Email author
  • Abdellatif El Afia
    • 1
  1. 1.Department of Informatics and Decision SupportMohammed V UniversityRabatMorocco

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