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      A HYBRID METHOD BASED ON CUCKOO SEARCH ALGORITHM FOR GLOBAL OPTIMIZATION PROBLEMS

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          Abstract

          Cuckoo search algorithm is considered one of the promising metaheuristic algorithms applied to solve numerous problems in different fields. However, it undergoes the premature convergence problem for high dimensional problems because the algorithm converges rapidly. Therefore, we proposed a robust approach to solve this issue by hybridizing optimization algorithm, which is a combination of Cuckoo search algorithmand Hill climbing called CSAHC discovers many local optimum traps by using local and global searches, although the local search method is trapped at the local minimum point. In other words, CSAHC has the ability to balance between the global exploration of the CSA and the deep exploitation of the HC method. The validation of the performance is determined by applying 13 benchmarks. The results of experimental simulations prove the improvement in the efficiency and the effect of the cooperation strategy and the promising of CSAHC.  

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          Author and article information

          Contributors
          Malaysia
          Malaysia
          Algeria
          Journal
          Journal of Information and Communication Technology
          UUM Press
          June 12 2018
          : 17
          : 469-491
          Affiliations
          [1 ]School of Computer Sciences, Universiti Sains Malaysia, Malaysia
          Article
          8261
          10.32890/jict2018.17.3.8261

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