Multi-Objective Volleyball Premier League algorithm

This paper proposes a novel optimization algorithm called the Multi-Objective Volleyball Premier League (MOVPL) algorithm for solving global optimization problems with multiple objective functions. The algorithm is inspired by the teams competing in a volleyball premier league. The strong point of t...

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Vydané v:Knowledge-based systems Ročník 196; s. 105781
Hlavní autori: Moghdani, Reza, Salimifard, Khodakaram, Demir, Emrah, Benyettou, Abdelkader
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Amsterdam Elsevier B.V 21.05.2020
Elsevier Science Ltd
Predmet:
Algorithms
Benchmarks
Design engineering
Design optimization
Engineering design optimization problems
Evolutionary algorithms
Experiments
Function words
Global optimization
Multi-objective evolutionary algorithm
Multiple objective analysis
Objectives
Optimization
Pareto solution
Particle swarm optimization
Volleyball
Global optimization
Engineering design optimization problems
Multi-objective evolutionary algorithm
Pareto solution
ISSN:0950-7051, 1872-7409
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Shrnutí:This paper proposes a novel optimization algorithm called the Multi-Objective Volleyball Premier League (MOVPL) algorithm for solving global optimization problems with multiple objective functions. The algorithm is inspired by the teams competing in a volleyball premier league. The strong point of this study lies in extending the multi-objective version of the Volleyball Premier League algorithm (VPL), which is recently used in such scientific researches, with incorporating the well-known approaches including archive set and leader selection strategy to obtain optimal solutions for a given problem with multiple contradicted objectives. To analyze the performance of the algorithm, ten multi-objective benchmark problems with complex objectives are solved and compared with two well-known multi-objective algorithms, namely Multi-Objective Particle Swarm Optimization (MOPSO) and Multi-Objective Evolutionary Algorithm Based on Decomposition (MOEA/D). Computational experiments highlight that the MOVPL outperforms the two state-of-the-art algorithms on multi-objective benchmark problems. In addition, the MOVPL algorithm has provided promising results on well-known engineering design optimization problems.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:0950-7051
1872-7409
DOI:10.1016/j.knosys.2020.105781