Many-objective optimization with corner-based search

The performance of multi-objective evolutionary algorithms can severely deteriorate when applied to problems with 4 or more objectives, called many-objective problems. For Pareto dominance based techniques, available information about some optimal solutions can be used to improve their performance....

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Vydáno v:Memetic computing Ročník 7; číslo 2; s. 105 - 118
Hlavní autoři: Freire, Hélio, de Moura Oliveira, P. B., Solteiro Pires, E. J., Bessa, Maximino
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2015
Témata:
Applications of Mathematics
Artificial Intelligence
Bioinformatics
Complex Systems
Control
Engineering
Mathematical and Computational Engineering
Mechatronics
Regular Research Paper
Robotics
Many-objective algorithm
Particle swarm optimization
Corner solutions
Evolutionary computation
ISSN:1865-9284, 1865-9292
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Shrnutí:The performance of multi-objective evolutionary algorithms can severely deteriorate when applied to problems with 4 or more objectives, called many-objective problems. For Pareto dominance based techniques, available information about some optimal solutions can be used to improve their performance. This is the case of corner solutions. This work considers the behaviour of three multi-objective algorithms [Non-dominated sorting genetic algorithm (NSGA-II), Speed-constrained multi-objective particle swarm optimization (SMPSO) and generalized differential evolution (GDE3)] when corner solutions are inserted into the population at different evolutionary stages. The problem of finding corner solutions is addressed by proposing a new algorithm based in multi-objective particle swarm optimization (MOPSO). Results concerning the behaviour of the aforementioned algorithms in five benchmark problems (DTLZ1-5) and respective analysis are presented.
ISSN:1865-9284
1865-9292
DOI:10.1007/s12293-015-0151-4