Open Access

ARTICLE

A Novel Quantum-Behaved Particle Swarm Optimization Algorithm

Tao Wu¹, Lei Xie¹, Xi Chen², Amir Homayoon Ashrafzadeh³, Shu Zhang^{4, *}

1 School of Computer Science, Chengdu University of Information Technology, Chengdu, 610225, China.
2 School of Computer Science and Technology, Southwest Minzu University, Chengdu, 610041, China.
3 CSIT Department, School of Science, RMIT University, Melbourne, 3058, Australia.
4 Department of Emergency Medicine, West China Hospital, Sichuan University, Chengdu, 610041, China.

* Corresponding Author: Shu Zhang. Email: .

Computers, Materials & Continua 2020, 63(2), 873-890. https://doi.org/10.32604/cmc.2020.07478

Received 24 May 2019; Accepted 31 August 2019; Issue published 01 May 2020

Abstract

The efficient management of ambulance routing for emergency requests is vital to save lives when a disaster occurs. Quantum-behaved Particle Swarm Optimization (QPSO) algorithm is a kind of metaheuristic algorithms applied to deal with the problem of scheduling. This paper analyzed the motion pattern of particles in a square potential well, given the position equation of the particles by solving the Schrödinger equation and proposed the Binary Correlation QPSO Algorithm Based on Square Potential Well (BCQSPSO). In this novel algorithm, the intrinsic cognitive link between particles’ experience information and group sharing information was created by using normal Copula function. After that, the control parameters chosen strategy gives through experiments. Finally, the simulation results of the test functions show that the improved algorithms outperform the original QPSO algorithm and due to the error gradient information will not be over utilized in square potential well, the particles are easy to jump out of the local optimum, the BCQSPSO is more suitable to solve the functions with correlative variables.

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Keywords

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