A Quantum Particle Swarm Optimization Algorithm Based on Self-Updating Mechanism

A Quantum Particle Swarm Optimization Algorithm Based on Self-Updating Mechanism

Shuyue Wu
Copyright: © 2021 |Pages: 21
DOI: 10.4018/978-1-7998-8593-1.ch001
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Abstract

The living mechanism has limited life in nature; it will age and die with time. This article describes that during the progressive process, the aging mechanism is very important to keep a swarm diverse. In the quantum behavior particle swarm (QPSO) algorithm, the particles are aged and the algorithm is prematurely convergent, the self-renewal mechanism of life is introduced into QPSO algorithm, and a leading particle and challengers are introduced. When the population particles are aged and the leading power of leading particle is exhausted, a challenger particle becomes the new leader particle through the competition update mechanism, group evolution is completed and the group diversity is maintained, and the global convergence of the algorithm is proven. Next in the article, twelve Clement2009 benchmark functions are used in the experimental test, both the comparison and analysis of results of the proposed method and classical improved QPSO algorithms are given, and the simulation results show strong global finding ability of the proposed algorithm. Especially in the seven multi-model test functions, the comprehensive performance is optimal.
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2. Quantum Particle Swarm Optimization (Qpso)

In the PSO algorithm, the global best position (Pg), the individual best position (Pi), the velocity information Vi of the particle, and the position information Xi are used. PSO evolution equation:

978-1-7998-8593-1.ch001.m01
(1)
978-1-7998-8593-1.ch001.m02
(2)

  • Quantum Particle Swarm Optimization (QPSO): The running track of PSO algorithm particles were analyzed by Clerc (Clerc et al., 2002), he pointed out that in the PSO algorithm, if each particle can converge to its local attraction Pi = (Pi1, Pi2, ..., PiD), then PSO algorithm may converge. Among them:

    978-1-7998-8593-1.ch001.m03
    (3)

and set

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