An improved sparrow search algorithm based on quantum computations and multi-strategy enhancement

•A new method to solve optimization problems is proposed (QMESSA).•Designing 3 new strategies to improve convergence rate and optimization accuracy.•Giving the convergence proof of the proposed algorithm.•The superiority of QMESSA is verified. Aiming at the defects of the sparrow search algorithm (S...

Full description

Saved in:
Bibliographic Details
Published in:Expert systems with applications Vol. 215; p. 119421
Main Authors: Wu, Rui, Huang, Haisong, Wei, Jianan, Ma, Chi, Zhu, Yunwei, Chen, Yilin, Fan, Qingsong
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.04.2023
Subjects:
Metaheuristic
Multi-strategy
Optimization problems
Sparrow Search Algorithm
Metaheuristic
Optimization problems
Sparrow Search Algorithm
Multi-strategy
ISSN:0957-4174, 1873-6793
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:•A new method to solve optimization problems is proposed (QMESSA).•Designing 3 new strategies to improve convergence rate and optimization accuracy.•Giving the convergence proof of the proposed algorithm.•The superiority of QMESSA is verified. Aiming at the defects of the sparrow search algorithm (SSA), such as a deficient optimization accuracy and low search efficiency, the sparrow search algorithm based on quantum computations and multi-strategy enhancement (QMESSA) is proposed. Firstly, based on a diversified initial population strategy, an improved circle chaotic mapping theory was proposed, and an initial population with more randomness and diversity was obtained by combining quantum computations with a quantum gate mutation mechanism. Secondly, using an enhanced search strategy, an adaptive T-distribution and a new position update formula were constructed to accelerate the convergence and enhance its variability. Finally, a dynamic evolution formula was designed for the precision elimination mechanism. Based on this, individuals with poor fitness are replaced by new individuals generated using this formula. In addition, a new boundary control strategy was proposed. The convergence of QMESSA was systematically proven, and the proposed algorithm was tested on 24 benchmark functions and CEC 2017 functions. The experiment results as well as the results of a Wilcoxon rank-sum test show that QMESSA achieves a better comprehensive performance than SSA and other advanced optimization algorithms. Finally, the superiority of QMESSA was verified for several classical practical application problems. The source code of QMESSA is available at https://ww2.mathworks.cn/matlabcentral/fileexchange/120013-project1-0.
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2022.119421