skip to main content
Idioma:

Many-objective Evolutionary Algorithm with Knee point-based Reference Vector Adaptive Adjustment Strategy

Zhu, Zhuanghua

KSII transactions on Internet and information systems, 2022-09, Vol.16 (9), p.2976-2990 [Periódico revisado por pares]

한국인터넷정보학회

Texto completo disponível

Citações Citado por
  • Título:
    Many-objective Evolutionary Algorithm with Knee point-based Reference Vector Adaptive Adjustment Strategy
  • Autor: Zhu, Zhuanghua
  • Assuntos: Adaptive adjustment ; Algorithms ; Artificial intelligence ; Evolutionary algorithm ; Knee point ; Mathematical optimization ; Reference vector
  • É parte de: KSII transactions on Internet and information systems, 2022-09, Vol.16 (9), p.2976-2990
  • Notas: Korean Society for Internet Information
    KISTI1.1003/JNL.JAKO202229454812183
  • Descrição: The adaptive adjustment of reference or weight vectors in decomposition-based methods has been a hot research topic in the evolutionary community over the past few years. Although various methods have been proposed regarding this issue, most of them aim to diversify solutions in the objective space to cover the true Pareto fronts as much as possible. Different from them, this paper proposes a knee point-based reference vector adaptive adjustment strategy to concurrently balance the convergence and diversity. To be specific, the knee point-based reference vector adaptive adjustment strategy firstly utilizes knee points to construct the adaptive reference vectors. After that, a new fitness function is defined mathematically. Then, this paper further designs a many-objective evolutionary algorithm with knee point-based reference vector adaptive adjustment strategy, where the mating operation and environmental selection are designed accordingly. The proposed method is extensively tested on the WFG test suite with 8, 10 and 12 objectives and MPDMP with state-of-the-art optimizers. Extensive experimental results demonstrate the superiority of the proposed method over state-of-the-art optimizers and the practicability of the proposed method in tackling practical many-objective optimization problems.
  • Editor: 한국인터넷정보학회
  • Idioma: Coreano;Inglês
Voltar para lista de resultados

  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

Buscando em bases de dados remotas. Favor aguardar.