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Seven-spot ladybird optimization: a novel and efficient metaheuristic algorithm for numerical optimization.

This paper presents a novel biologically inspired metaheuristic algorithm called seven-spot ladybird optimization (SLO). The SLO is inspired by recent discoveries on the foraging behavior of a seven-spot ladybird. In this paper, the performance of the SLO is compared with that of the genetic algorit...

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Publicado en:Scientific World Journal pp. 378515 - 378516
Autores principales: Wang, Peng, Zhu, Zhouquan, Huang, Shuai
Formato: research Journal Article
Publicado: Wiley-Blackwell 2013
Acceso en línea:Ver este registro en EBSCOhost
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      jtl: Scientific World Journal
      issn: 1537744X
      maglogo: N
    pubinfo:
      dt: 2013
      pid: 480
      pub: Wiley-Blackwell
      place: Malden, Massachusetts
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        2012424038
        10.1155/2013/378515
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        104136229
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      tig:
        atl: Seven-spot ladybird optimization: a novel and efficient metaheuristic algorithm for numerical optimization.
      aug:
        au:
          Wang, Peng
          Zhu, Zhouquan
          Huang, Shuai
        affil: School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an 710072, China.
      sug:
        subj:
          Algorithms
          Insects Physiology
          Decision Support Techniques
          Eating Behavior
          Models, Biological
          Computing Methodologies
          Behavior
          Computer Simulation
      ab: This paper presents a novel biologically inspired metaheuristic algorithm called seven-spot ladybird optimization (SLO). The SLO is inspired by recent discoveries on the foraging behavior of a seven-spot ladybird. In this paper, the performance of the SLO is compared with that of the genetic algorithm, particle swarm optimization, and artificial bee colony algorithms by using five numerical benchmark functions with multimodality. The results show that SLO has the ability to find the best solution with a comparatively small population size and is suitable for solving optimization problems with lower dimensions.
      pubtype: Academic Journal
      doctype:
        research
        Journal Article
      ougenre: Article
    language: English
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