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Local cooperation delivers global optimization

  • Zhou Wu
  • , Lu Xu
  • , Tommy W. S. Chow
  • , Mingbo Zhao

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

Abstract

The cooperation behaviors existing in the animal and human being societies, have been modeled for the numerical optimization, but the local cooperation has not been modeled separately in optimization problems. In this paper the local cooperation is newly modeled as Neighborhood Field Model (NFM). Based on NFM, a new optimization technique called Neighborhood Field Optimization algorithm (NFO) is firstly proposed to deliver global optimization. In NFO, each individual is attracted by its superior neighbor and repulsed by its inferior neighbor to search a better solution. In this paper, NFO is compared with certain algorithms under twelve different benchmark functions. The results show that NFO can outperform them on multimodal functions in the respect of accuracy, effectiveness and robustness. It also can be noted that the cooperation behavior can play a dominant role in the optimization algorithm separately. © 2012 IEEE.
Original languageEnglish
Title of host publication2012 IEEE Congress on Evolutionary Computation, CEC 2012
DOIs
Publication statusPublished - 2012
Event2012 IEEE Congress on Evolutionary Computation, CEC 2012 - Brisbane, QLD, Australia
Duration: 10 Jun 201215 Jun 2012

Conference

Conference2012 IEEE Congress on Evolutionary Computation, CEC 2012
PlaceAustralia
CityBrisbane, QLD
Period10/06/1215/06/12

Research Keywords

  • Differential Evolution
  • Evolutionary Algorithms
  • Local Cooperation
  • Neighborhood Field Model
  • Particle Swarm Optimization
  • Potential Field Model

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