Multi-guide particle swarm optimization for multi-objective optimization: empirical and stability analysis

This article presents a new particle swarm optimization (PSO)-based multi-objective optimization algorithm, named multi-guide particle swarm optimization (MGPSO). The MGPSO is a multi-swarm approach, where each subswarm optimizes one of the objectives. An archive guide is added to the velocity updat...

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Published in:	Swarm intelligence Vol. 13; no. 3-4; pp. 245 - 276
Main Authors:	Scheepers, Christiaan, Engelbrecht, Andries P., Cleghorn, Christopher W.
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.12.2019 Springer Nature B.V
Subjects:	Algorithms Artificial Intelligence Communications Engineering Computer Communication Networks Computer Science Computer Systems Organization and Communication Networks Exploration Exploratory behavior Mathematical and Computational Engineering Networks Optimization Velocity Attainment surface Order-2 stability Multi-objective optimization Stability analysis Multi-guide particle swarm optimization Particle swarm optimization Order-1 stability
ISSN:	1935-3812, 1935-3820
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Abstract	This article presents a new particle swarm optimization (PSO)-based multi-objective optimization algorithm, named multi-guide particle swarm optimization (MGPSO). The MGPSO is a multi-swarm approach, where each subswarm optimizes one of the objectives. An archive guide is added to the velocity update equation to facilitate convergence to a Pareto front of non-dominated solutions. An extensive empirical and stability analysis of the MGPSO is conducted. The empirical analysis focuses on the exploration behavior of the MGPSO and compares the performance of the MGPSO with that of state-of-the-art multi-objective PSO and evolutionary algorithms. The results show that the MGPSO is highly competitive on a number of benchmark functions. The paper provides a theoretical stability analysis which focuses on the sufficient and necessary conditions for order-1 and order-2 stability of the MGPSO. The paper extends existing work on MGPSO stability analysis by deriving new stability criteria for differing values of the acceleration coefficients used in the velocity update equation.
AbstractList	This article presents a new particle swarm optimization (PSO)-based multi-objective optimization algorithm, named multi-guide particle swarm optimization (MGPSO). The MGPSO is a multi-swarm approach, where each subswarm optimizes one of the objectives. An archive guide is added to the velocity update equation to facilitate convergence to a Pareto front of non-dominated solutions. An extensive empirical and stability analysis of the MGPSO is conducted. The empirical analysis focuses on the exploration behavior of the MGPSO and compares the performance of the MGPSO with that of state-of-the-art multi-objective PSO and evolutionary algorithms. The results show that the MGPSO is highly competitive on a number of benchmark functions. The paper provides a theoretical stability analysis which focuses on the sufficient and necessary conditions for order-1 and order-2 stability of the MGPSO. The paper extends existing work on MGPSO stability analysis by deriving new stability criteria for differing values of the acceleration coefficients used in the velocity update equation.
Author	Engelbrecht, Andries P. Cleghorn, Christopher W. Scheepers, Christiaan
Author_xml	– sequence: 1 givenname: Christiaan surname: Scheepers fullname: Scheepers, Christiaan – sequence: 2 givenname: Andries P. orcidid: 0000-0002-0242-3539 surname: Engelbrecht fullname: Engelbrecht, Andries P. email: engel@sun.ac.za organization: Department of Industrial Engineering, and Computer Science Division, University of Stellenbosch – sequence: 3 givenname: Christopher W. surname: Cleghorn fullname: Cleghorn, Christopher W. organization: Department of Computer Science, School for Information Technology, University of Pretoria
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SubjectTerms	Algorithms Artificial Intelligence Communications Engineering Computer Communication Networks Computer Science Computer Systems Organization and Communication Networks Exploration Exploratory behavior Mathematical and Computational Engineering Networks Optimization Velocity
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Title	Multi-guide particle swarm optimization for multi-objective optimization: empirical and stability analysis
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