Enhanced decomposition-based hybrid evolutionary and gradient-based algorithm for many-objective optimization

This paper presents a novel decomposition-based hybrid many-objective optimization method using particle swarm optimization (PSO) and sequential quadratic programming (SQP) algorithms. In the proposed method, the objective function space decomposed into optimization sub-problems. In this respect, th...

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Vydané v:	Applied intelligence (Dordrecht, Netherlands) Ročník 53; číslo 24; s. 30497 - 30522
Hlavní autori:	Mohammad Zadeh, Parviz, Mohagheghi, Mostafa
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.12.2023 Springer Nature B.V
Predmet:	Artificial Intelligence Computational efficiency Computer Science Coordinates Decomposition Evolutionary algorithms Function space Machines Manufacturing Mechanical Engineering Multiple objective analysis Operators (mathematics) Optimization algorithms Particle swarm optimization Processes Quadratic programming Sequential quadratic programming (SQP) Multi-objective particle swarm optimization (MOPSO) Decomposition Many-objective optimization Reference point Multi-objective evolutionary algorithms (MOEAs)
ISSN:	0924-669X, 1573-7497
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