An improved multi-objective particle swarm optimization algorithm for the design of foundation pit of rail transit upper cover project

In this study, a multi-objective particle swarm optimization (MOIPSO) algorithm is proposed to address complex optimization problems, including real-world engineering challenges. The algorithm retains the basic convergence mechanism of particle swarm optimization (PSO) as its core, while innovativel...

Full description

Saved in:
Bibliographic Details
Published in:Scientific reports Vol. 15; no. 1; pp. 10403 - 25
Main Authors: Shao, Jinyan, Lu, Yuan, Sun, Yi, Zhao, Lei
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 26.03.2025
Nature Publishing Group
Nature Portfolio
Subjects:
639/166
639/705
Algorithms
Humanities and Social Sciences
Multi-objective optimization problems
Multi-objective particle swarm optimization (MOIPSO)
multidisciplinary
Objective function
Optimization
Pareto optimum
Particle swarm optimization
Rail transit upper cover foundation pit
Science
Science (multidisciplinary)
Multi-objective optimization problems
Multi-objective particle swarm optimization (MOIPSO)
Particle swarm optimization
Rail transit upper cover foundation pit
ISSN:2045-2322, 2045-2322
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In this study, a multi-objective particle swarm optimization (MOIPSO) algorithm is proposed to address complex optimization problems, including real-world engineering challenges. The algorithm retains the basic convergence mechanism of particle swarm optimization (PSO) as its core, while innovatively combining the fast non-dominated sorting technique to effectively evaluate and approximate the Pareto optimal solution set. To enhance the diversity and generalization of the solution set, the crowding distance mechanism is introduced, ensuring a good balance between multiple optimization objectives and a wider coverage of the solution space. Additionally, an acceleration factor based on trigonometric functions and an adaptive Gaussian mutation strategy are incorporated, improving the exploration ability of the particles in the search space and facilitating their movement towards the global optimal solution more effectively. The performance of the algorithm is verified using the multi-modal multi-objective benchmark function set provided by CEC2020, and comparisons are made with five advanced multi-objective metaheuristics. The MOIPSO algorithm is also applied to solve the design problem of rail transit upper cover foundation pit, further demonstrating the practical effectiveness of the proposed algorithm. The results show that MOIPSO not only performs well in multi-objective function testing but also proves highly competitive in solving real-world engineering problems. Note that the source codes of MOGWO are publicly available at https://au.mathworks.com/matlabcentral/fileexchange/177404-moipso-optimization-engineering-problem .
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-025-87350-8