A Hybrid Multiobjective Optimization Based on Nondominated Sorting and Crowding Distance, with Applications to Wave Energy Converters

Multiobjective evolutionary algorithm based on decomposition (MOEA/D) has attracted a lot of attention since it can handle multiobjective problems (MOP) with a complicated Pareto front. The procedure involves decomposing a MOP into single subproblems, which are eventually optimized simultaneously ba...

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Vydané v:International transactions on electrical energy systems Ročník 2022; s. 1 - 18
Hlavní autori: Saveca, John, Sun, Yanxia, Wang, Zenghui
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Hoboken Hindawi 26.03.2022
John Wiley & Sons, Inc
Predmet:
Algorithms
Complexity
Crowding
Decomposition
Electricity distribution
Energy
Evolutionary algorithms
Evolutionary computation
Genetic algorithms
Maintenance costs
Multiple objective analysis
Ocean waves
Operators (mathematics)
Optimization
Pareto optimization
Pareto optimum
Particle swarm optimization
Performance evaluation
Sorting algorithms
Wave energy
Wave power
ISSN:2050-7038, 2050-7038
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Shrnutí:Multiobjective evolutionary algorithm based on decomposition (MOEA/D) has attracted a lot of attention since it can handle multiobjective problems (MOP) with a complicated Pareto front. The procedure involves decomposing a MOP into single subproblems, which are eventually optimized simultaneously based on the MOP neighborhood information. However, the MOEA/D strategy tends to produce a distributed optimization that is not of good quality in some problems with complex Pareto optimal front, such as problems with a long tail and sharp peak, common in real-world situations. This paper proposes an improved MOEA/D to enhance the distributed optimization quality and minimize its complexity while accelerating the optimization to get a better solution. The improved method is achieved by incorporating a Hybrid Differential Evolution/Particle Swarm Optimization algorithm and a hybrid operator based on nondominated sorting and crowding distance algorithm. This incorporation takes place in the mutation generator and initial population part of the original MOEA/D algorithm. Simulations and comparisons are carried out based on some MOP benchmark functions to verify the proposed method’s performance. The experimental results show that the proposed method achieves better performance compared to other algorithms. Furthermore, the proposed method is also applied to optimize the multiobjective wave energy converter model to maximize power per year and minimize cost per unit power.
Bibliografia:ObjectType-Article-1
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ISSN:2050-7038
2050-7038
DOI:10.1155/2022/8309697