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Development and performance validation of new parallel hybrid cuckoo search–genetic algorithm

  • Lamyae MelloukEmail author
  • Abdessadek Aaroud
  • Mohamed Boulmalf
  • Khalid Zine-Dine
  • Driss Benhaddou
Original Paper
  • 3 Downloads

Abstract

In this work, a new hybrid cuckoo search and genetic algorithm optimization method using a novel adaptive penalty function was proposed to solve the economic dispatch (ED) problem in smart grid.   Please check and confirm the edit made in article title. This method was also paralyzed in order to solve the problem within specific time suitable to solve Energy management Problems. Three improvements are achieved through this combination. First, parallelism allows further reduction of the execution time. Second, the hybridization of both cuckoo search and genetic algorithm methods allows better diversification and exploration of search space which increases the solution quality. Third, the new adaptive penalty function was developed to discard infeasible solutions and to choose near-optimal ones within a short time. The efficiency of the developed algorithm is proven theoretically and experimentally. Three scenarios are considered to prove experimentally the out-performance of the developed method: (1) the proposed method is compared with Cuckoo Search and Genetic Algorithm methods using a set of benchmark functions. (2) A comparative study is carried out by applying the method to the ED continuous problem optimization case study. (3) The method is compared with Cuckoo search to solve discrete demand side management problem, considering each consumer as an independent parameter. The performance evaluation was conducted using Matlab data parallelism library.

Notes

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Université International de Rabat, Computer Science FacultyTIClabMorocco
  2. 2.LAROSERI Lab.Chouaib Doukkali University, Faculty of SciencesEl JadidaMorocco
  3. 3.University of HoustonHoustonUSA

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