A study on auto-configuration of Multi-Objective Particle Swarm Optimization Algorithm
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- @InProceedings{delima:2017:CEC,
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author = "Ricardo Henrique Remes {de Lima} and
Aurora Trinidad Ramirez Pozo",
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booktitle = "2017 IEEE Congress on Evolutionary Computation (CEC)",
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title = "A study on auto-configuration of Multi-Objective
Particle Swarm Optimization Algorithm",
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year = "2017",
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editor = "Jose A. Lozano",
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pages = "718--725",
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address = "Donostia, San Sebastian, Spain",
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publisher = "IEEE",
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month = "5-8 " # jun,
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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",
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isbn13 = "978-1-5090-4601-0",
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DOI = "doi:10.1109/CEC.2017.7969381",
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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.",
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notes = "IEEE Catalog Number: CFP17ICE-ART Also known as
\cite{7969381}",
- }
Genetic Programming entries for
Ricardo Henrique Remes de Lima
Aurora Trinidad Ramirez Pozo
Citations