Bioinspired Bare Bones Mayfly Algorithm for Large-Scale Spherical Minimum Spanning Tree

Tian Zhang; Yongquan Zhou; Guo Zhou; Wu Deng; Qifang Luo

doi:10.3389/fbioe.2022.830037

Bioinspired Bare Bones Mayfly Algorithm for Large-Scale Spherical Minimum Spanning Tree

Front Bioeng Biotechnol. 2022 Mar 1:10:830037. doi: 10.3389/fbioe.2022.830037. eCollection 2022.

Authors

Tian Zhang^{1

2}, Yongquan Zhou^{1

2}, Guo Zhou³, Wu Deng⁴, Qifang Luo^{1

2}

Affiliations

¹ College of Artificial Intelligence, Guangxi University for Nationalities, Nanning, China.
² Guangxi Key Laboratories of Hybrid Computation and IC Design Analysis, Nanning, China.
³ Department of Science and Technology Teaching, China University of Political Science and Law, Beijing, China.
⁴ College of Electronic Information and Automation, Civil Aviation University of China, Tianjin, China.

Abstract

Mayfly algorithm (MA) is a bioinspired algorithm based on population proposed in recent years and has been applied to many engineering problems successfully. However, it has too many parameters, which makes it difficult to set and adjust a set of appropriate parameters for different problems. In order to avoid adjusting parameters, a bioinspired bare bones mayfly algorithm (BBMA) is proposed. The BBMA adopts Gaussian distribution and Lévy flight, which improves the convergence speed and accuracy of the algorithm and makes better exploration and exploitation of the search region. The minimum spanning tree (MST) problem is a classic combinatorial optimization problem. This study provides a mathematical model for solving a variant of the MST problem, in which all points and solutions are on a sphere. Finally, the BBMA is used to solve the large-scale spherical MST problems. By comparing and analyzing the results of BBMA and other swarm intelligence algorithms in sixteen scales, the experimental results illustrate that the proposed algorithm is superior to other algorithms for the MST problems on a sphere.

Keywords: Prüfer code; bare bones mayfly algorithm; bioinspired algorithm; large-scale spherical MST; mayfly algorithm.