A study on auto-configuration of Multi-Objective Particle Swarm Optimization Algorithm

Created by W.Langdon from gp-bibliography.bib Revision:1.8129

@InProceedings{delima:2017:CEC,

• author = "Ricardo Henrique Remes {de Lima} and Aurora Trinidad Ramirez Pozo",
• booktitle = "2017 IEEE Congress on Evolutionary Computation (CEC)",
• title = "A study on auto-configuration of Multi-Objective Particle Swarm Optimization Algorithm",
• year = "2017",
• editor = "Jose A. Lozano",
• pages = "718--725",
• address = "Donostia, San Sebastian, Spain",
• publisher = "IEEE",
• month = "5-8 " # jun,
• keywords = "genetic algorithms, genetic programming, context-free grammars, evolutionary computation, particle swarm optimisation, statistical analysis, GE, IRACE, MOPSO algorithm, PSO performance, SMPSO, autoconfiguration study, context-free grammar, grammatical evolution, iterated racing, monoobjective particle swarm optimization algorithm, multiobjective evolutionary algorithms automatic design, multiobjective particle swarm optimization algorithm, speed-constrained MOPSO, statistical tests, velocity equation, Algorithm design and analysis, Grammar, Particle swarm optimization, Production, Space exploration",
• isbn13 = "978-1-5090-4601-0",
• DOI = "doi:10.1109/CEC.2017.7969381",
• abstract = "Researches point out to the importance of automatic design of multi-objective evolutionary algorithms. Because in general, algorithms automatically designed outperform traditional multi-objective evolutionary algorithms from the literature. Nevertheless, until fairly recently, most of the researches have been focused on a small group of algorithms, often based on evolutionary algorithms. On the other hand, mono-objective Particle Swarm Optimization algorithm (PSO) have been widely used due to its flexibility and competitive results in different applications. Besides, as PSO performance depends on different aspects of design like the velocity equation, its automatic design has been targeted by many researches with encouraging results. Motivated by these issues, this work studies the automatic design of Multi-Objective Particle Swarm Optimization (MOPSO). A framework that uses a context-free grammar to guide the design of the algorithms is implemented. The framework includes a set of parameters and components of different MOPSOs, and two design algorithms: Grammatical Evolution (GE) and Iterated Racing (IRACE). Evaluation results are presented, comparing MOPSOs generated by both design algorithms. Furthermore, the generated MOPSOs are compared to the Speed-constrained MOPSO (SMPSO), a well-known algorithm using a set of Multi-Objective problems, quality indicators and statistical tests.",
• notes = "IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969381}",

}

Genetic Programming entries for Ricardo Henrique Remes de Lima Aurora Trinidad Ramirez Pozo

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