Many Objective Particle Swarm Optimization

Many-objective problems refer to the optimization problems containing more than three conflicting objectives. To obtain a representative set of well-distributed non-dominated solutions close to Pareto front in the objective space remains a challenging problem. Many papers have proposed different Mul...

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Bibliographic Details
Published in:Information sciences Vol. 374; pp. 115 - 134
Main Authors: Figueiredo, E.M.N., Ludermir, T.B., Bastos-Filho, C.J.A.
Format: Journal Article
Language:English
Published: Elsevier Inc 20.12.2016
Subjects:
Algorithms
Convergence
Evolutionary algorithms
Many-objective problems
Multi-Objective Evolutionary Algorithms
Multiple objective analysis
Optimization
Pareto optimality
Particle Swarm Optimization
Search process
Swarm intelligence
Swarm intelligence
Multi-Objective Evolutionary Algorithms
Particle Swarm Optimization
Many-objective problems
ISSN:0020-0255, 1872-6291
Online Access:Get full text
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Summary:Many-objective problems refer to the optimization problems containing more than three conflicting objectives. To obtain a representative set of well-distributed non-dominated solutions close to Pareto front in the objective space remains a challenging problem. Many papers have proposed different Multi-Objective Evolutionary Algorithms to solve the lack of the convergence and diversity in many-objective problems. One of the more promising approaches uses a set of reference points to discriminate the solutions and guide the search process. However, this approach was incorporated mainly in Multi-Objective Evolutionary Algorithms, and there are just some few promising adaptations of Particle Swarm Optimization approaches for effectively tackling many-objective problems regarding convergence and diversity. Thus, this paper proposes a practical and efficient Many-Objective Particle Swarm Optimization algorithm for solving many-objective problems. Our proposal uses a set of reference points dynamically determined according to the search process, allowing the algorithm to converge to the Pareto front, but maintaining the diversity of the Pareto front. Our experimental results demonstrate superior or similar performance when compared to other state-of-art algorithms.
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ISSN:0020-0255
1872-6291
DOI:10.1016/j.ins.2016.09.026