Type-II opposition-based differential evolution

Hojjat Salehinejad, Shahryar Rahnamayan, Hamid R. Tizhoosh

Research output: Chapter in Book/Report/Conference proceedingConference contribution


The concept of opposition-based learning (OBL) can be categorized into Type-I and Type-II OBL methodologies. The Type-I OBL is based on the opposite points in the variable space while the Type-II OBL considers the opposite of function value on the landscape. In the past few years, many research works have been conducted on development of Type-I OBL-based approaches with application in science and engineering, such as opposition-based differential evolution (ODE). However, compared to Type-I OBL, which cannot address a real sense of opposition in term of objective value, the Type-II OBL is capable to discover more meaningful knowledge about problem's landscape. Due to natural difficulty of proposing a Type-II-based approach, very limited research has been reported in that direction. In this paper, for the first time, the concept of Type-II OBL has been investigated in detail in optimization; also it is applied on the DE algorithm as a case study. The proposed algorithm is called opposition-based differential evolution Type-II (ODE-II) algorithm; it is validated on the testbed proposed for the IEEE Congress on Evolutionary Computation 2013 (IEEE CEC-2013) contest with 28 benchmark functions. Simulation results on the benchmark functions demonstrate the effectiveness of the proposed method as the first step for further developments in Type-II OBL-based schemes.

Original languageEnglish (US)
Title of host publicationProceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages8
ISBN (Electronic)9781479914883
StatePublished - Sep 16 2014
Event2014 IEEE Congress on Evolutionary Computation, CEC 2014 - Beijing, China
Duration: Jul 6 2014Jul 11 2014

Publication series

NameProceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014


Conference2014 IEEE Congress on Evolutionary Computation, CEC 2014

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Theoretical Computer Science


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