1 Ocak 2021 Dergi makalesi Açık Erişim

Aydilek, Ibrahim Berkan; Karacizmeli, Izzettin Hakan; Tenekeci, Mehmet Emin; Kaya, Serkan; Gumuscu, Abdulkadir

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  <identifier identifierType="URL">https://aperta.ulakbim.gov.tr/record/231538</identifier>
  <creators>
    <creator>
      <creatorName>Aydilek, Ibrahim Berkan</creatorName>
      <givenName>Ibrahim Berkan</givenName>
      <familyName>Aydilek</familyName>
      <affiliation>Harran Univ, Engn Fac, Comp Engn Dept, Sanliurfa, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Karacizmeli, Izzettin Hakan</creatorName>
      <givenName>Izzettin Hakan</givenName>
      <familyName>Karacizmeli</familyName>
      <affiliation>Harran Univ, Engn Fac, Ind Engn Dept, Sanliurfa, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Tenekeci, Mehmet Emin</creatorName>
      <givenName>Mehmet Emin</givenName>
      <familyName>Tenekeci</familyName>
      <affiliation>Harran Univ, Engn Fac, Comp Engn Dept, Sanliurfa, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Kaya, Serkan</creatorName>
      <givenName>Serkan</givenName>
      <familyName>Kaya</familyName>
      <affiliation>Harran Univ, Engn Fac, Ind Engn Dept, Sanliurfa, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Gumuscu, Abdulkadir</creatorName>
      <givenName>Abdulkadir</givenName>
      <familyName>Gumuscu</familyName>
      <affiliation>Harran Univ, Engn Fac, Elect Elect Engn Dept, Sanliurfa, Turkey</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Using Chaos Enhanced Hybrid Firefly Particle Swarm Optimization Algorithm For Solving Continuous Optimization Problems</title>
  </titles>
  <publisher>Aperta</publisher>
  <publicationYear>2021</publicationYear>
  <dates>
    <date dateType="Issued">2021-01-01</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Journal article</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://aperta.ulakbim.gov.tr/record/231538</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1007/s12046-021-01572-w</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="http://www.opendefinition.org/licenses/cc-by">Creative Commons Attribution</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">Optimization becomes more important and the use of optimization methods is becoming widespread with the developments in computer sciences. Researchers from different scientific fields are looking for better solutions to solve complex problems with optimization methods. In some complex problems, optimal results can be obtained utilizing metaheuristic algorithms. Researchers carry out different studies to improve the performance of present metaheuristic algorithms. Although the success of metaheuristic algorithms has been seen in previous studies, there are some weaknesses in these algorithms. Therefore, successful results cannot be obtained for each problem sometimes. In order to overcome this problem, more successful algorithms can be obtained by hybridizing the strong points of the different methods together. In addition, one of the important factors affecting the success of optimization algorithms is scanning ability of the solution space in order to find the optima. Exploring search space is carried out using random variables by some metaheuristic algorithms. The chaotic values that are generated by chaotic maps can be used instead of random values. Thus, search ability of algorithms performs more dynamically. In this study, hybrid firefly and particle swarm optimization algorithms are transformed to a chaotic-based algorithm by use of 10 different chaotic maps. Random valued parameters are generated by chaotic maps. In order to indicate the performances between different dimensions, CEC 2015 benchmark and constraint problems are used in experimental studies. Chaos enhanced methods are compared against canonical and hybrid optimization algorithms. It has been seen that obtained results of the proposed method were sufficiently successful and reliable.</description>
  </descriptions>
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