Global optimization through randomized group search in contracting regions

Chao Yu, Dipti Srinivasan, Qing Guo Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper proposes a new method for global optimization through randomized group search in contracting regions. For each iteration, a population is randomly produced within the search region, where the population size is chosen to ensure that the empirical optimum is an estimate of the true optimum within a predefined accuracy with a certain confidence. Fitness values are evaluated at the samples in the population. A very small subset of them with top-ranking fitness values are selected as good points. Neighborhoods of these good points are used to form a new and smaller search region, in which a new population is generated. It is easy to implement the algorithm. Extensive simulation on benchmark problems shows that the proposed method is fast and reasonably accurate.

Original languageEnglish
Title of host publication2016 IEEE Congress on Evolutionary Computation, CEC 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2813-2820
Number of pages8
ISBN (Electronic)9781509006229
DOIs
Publication statusPublished - 14 Nov 2016
Event2016 IEEE Congress on Evolutionary Computation, CEC 2016 - Vancouver, Canada
Duration: 24 Jul 201629 Jul 2016

Publication series

Name2016 IEEE Congress on Evolutionary Computation, CEC 2016

Conference

Conference2016 IEEE Congress on Evolutionary Computation, CEC 2016
Country/TerritoryCanada
CityVancouver
Period24/07/1629/07/16

ASJC Scopus subject areas

  • Artificial Intelligence
  • Modeling and Simulation
  • Computer Science Applications
  • Control and Optimization

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