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


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.



  1. 1.
    Moretti, M., Djomo, S.N., Azadi, H., May, K., De Vos, K., Van Passel, S., Witters, N.: A systematic review of environmental and economic impacts of smart grids. Renew. Sustain Energy Rev. 68, 888–898 (2017)Google Scholar
  2. 2.
    Mohan, V., Suresh, R., Singh, J.G., Ongsakul, W., Madhu, N.: Microgrid energy management combining sensitivities, interval and probabilistic uncertainties of renewable generation and loads. IEEE J. Emerg. Select. Top. Circ. Syst. 7(2), 262–270 (2017)Google Scholar
  3. 3.
    Fadaee, M., Radzi, M.A.M.: Multi-objective optimization of a stand-alone hybrid renewable energy system by using evolutionary algorithms: a review. Renew. Sustain. Energy Rev. 16(5), 3364–3369 (2012)Google Scholar
  4. 4.
    Bidram, A., Davoudi, A.: Hierarchical structure of microgrids control system. IEEE Trans. Smart Grid 3(4), 1963–1976 (2012)Google Scholar
  5. 5.
    Mohamed, F.A., Koivo, H.N.: Online management genetic algorithms of microgrid for residential application. Energy Convers. Manag. 64, 562–568 (2012)Google Scholar
  6. 6.
    Mohammadi-Ivatloo, B., Rabiee, A., Soroudi, A., Ehsan, M.: Iteration pso with time varying acceleration coefficients for solving non-convex economic dispatch problems. Int. J. Electr. Power Energy Syst. 42(1), 508–516 (2012)Google Scholar
  7. 7.
    Zheng, W., Wenchuan, W., Zhang, B., Li, Z., Liu, Y.: Fully distributed multi-area economic dispatch method for active distribution networks. IET Gen. Trans. Distrib. 9(12), 1341–1351 (2015)Google Scholar
  8. 8.
    Vo, D.N., Schegner, P., Ongsakul, W.: Cuckoo search algorithm for non-convex economic dispatch. IET Gen. Trans. Distrib. 7(6), 645–654 (2013)Google Scholar
  9. 9.
    Boroojeni, K.G., Amini, M.H., Iyengar, S.S., Rahmani, M., Pardalos, P.M.: An economic dispatch algorithm for congestion management of smart power networks. Energy Syst. 8(3), 643–667 (2017)Google Scholar
  10. 10.
    Lavaei, J., Low, S.H.: Zero duality gap in optimal power flow problem. IEEE Trans. Power Syst. 27(1), 92 (2012)Google Scholar
  11. 11.
    Chehouri, A., Younes, R., Perron, J., Ilinca, A.: A constraint-handling technique for genetic algorithms using a violation factor. arXiv:1610.00976 (2016)
  12. 12.
    Sun, Y., Wang, Z.: Improved particle swarm optimization based dynamic economic dispatch of power system. Proc. Manuf. 7, 297–302 (2017)Google Scholar
  13. 13.
    Mohamed, F.A., Koivo, H.N.: System modelling and online optimal management of microgrid using mesh adaptive direct search. Int. J. Electr. Power Energy Syst. 32(5), 398–407 (2010)Google Scholar
  14. 14.
    Hadji, B., Mahdad, B., Srairi, K., Mancer, N.: Multi-objective economic emission dispatch solution using dance bee colony with dynamic step size. Energy Proc. 74, 65–76 (2015)Google Scholar
  15. 15.
    Fang, X., Yang, D., Xue, G.: Online strategizing distributed renewable energy resource access in islanded microgrids. In: GLOBECOM, pp. 1–6 (2011)Google Scholar
  16. 16.
    Alyazidi, N.M., Mahmoud, M.S., Abouheaf, M.I.: Adaptive critics based cooperative control scheme for islanded microgrids. Neurocomputing 272, 532–541 (2018)Google Scholar
  17. 17.
    Gupta, R.A., Kumar, R., Bansal, A.K.: Bbo-based small autonomous hybrid power system optimization incorporating wind speed and solar radiation forecasting. Renew. Sustain. Energy Rev. 41, 1366–1375 (2015)Google Scholar
  18. 18.
    Krishnamurthy, S., Tzoneva, R., Apostolov, A.: Method for a parallel solution of a combined economic emission dispatch problem. Electr. Power Compon. Syst. 45(4), 393–409 (2017)Google Scholar
  19. 19.
    Goldberg, D.E., Holland, J.H.: Genetic algorithms and machine learning. Mach. Learn. 3(2), 95–99 (1988)Google Scholar
  20. 20.
    Gandomi, A.H., Yang, X.-S., Alavi, A.H.: Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng. Comput. 29(1), 17–35 (2013)Google Scholar
  21. 21.
    Shehab, M., Khader, A.T., Al-Betar, M.A.: A survey on applications and variants of the cuckoo search algorithm. Appl. Soft Comput. 61, 1041–1059 (2017)Google Scholar
  22. 22.
    Kiziloz, H.E., Dokeroglu, T.: A robust and cooperative parallel tabu search algorithm for the maximum vertex weight clique problem. Comput. Ind. Eng. 118, 54–66 (2018)Google Scholar
  23. 23.
    Hemavathi, S., Devarajan, N.: Efficient dynamic economic load dispatch using parallel process of enhanced optimization approach. Circ. Syst. 7(10), 3260 (2016)Google Scholar
  24. 24.
    Yang, X.-S., Deb, S.: Engineering optimisation by cuckoo search. Int. J. Math. Modell. Numer. Optim. 1(4), 330–343 (2010)zbMATHGoogle Scholar
  25. 25.
    Katsigiannis, Y.A., Kanellos, F.D., Papaefthimiou, S.: A software tool for capacity optimization of hybrid power systems including renewable energy technologies based on a hybrid genetic algorithm–tabu search optimization methodology. Energy Syst. 7(1), 33–48 (2016)Google Scholar
  26. 26.
    Tuffaha, M., Gravdahl, J.T.: Discrete state-space model to solve the unit commitment and economic dispatch problems. Energy Syst. 8(3), 525–547 (2017)Google Scholar
  27. 27.
    Ghazi, Z., Doustmohammadi, A.: Fault detection and power distribution optimization of smart grids based on hybrid petri net. Energy Syst. 8(3), 465–493 (2017)Google Scholar
  28. 28.
    Gandomkar, M., Vakilian, M., Ehsan, M.: A genetic-based tabu search algorithm for optimal dg allocation in distribution networks. Electr. Power Compon. Syst. 33(12), 1351–1362 (2005)Google Scholar
  29. 29.
    Xiao, L., Shao, W., Yu, M., Ma, J., Jin, C.: Research and application of a hybrid wavelet neural network model with the improved cuckoo search algorithm for electrical power system forecasting. Appl. Energy 198, 203–222 (2017)Google Scholar
  30. 30.
    Chen, G., Li, C., Dong, Z.: Parallel and distributed computation for dynamical economic dispatch. IEEE Trans. Smart Grid 8(2), 1026–1027 (2017)Google Scholar
  31. 31.
    Ongsakul, W., Tippayachai, J.: Parallel micro genetic algorithm based on merit order loading solutions for constrained dynamic economic dispatch. Electr. Power Syst. Res. 61(2), 77–88 (2002)Google Scholar
  32. 32.
    Fukuyama, Y., Ueki, Y.: An application of neural network to dynamic dispatch using multi processors. IEEE Trans. Power Syst. 9(4), 1759–1765 (1994)Google Scholar
  33. 33.
    Yang, X.-S., Press, L.: Nature-inspired metaheuristic algorithms, 2nd edn. (2010)Google Scholar
  34. 34.
    Yeniay, Ö.: Penalty function methods for constrained optimization with genetic algorithms. Math. Comput. Appl. 10(1), 45–56 (2005)MathSciNetGoogle Scholar
  35. 35.
    Joines, J.A., Houck, C.R.: On the use of non-stationary penalty functions to solve nonlinear constrained optimization problems with GA’s. In: Evolutionary Computation, 1994. IEEE World Congress on Computational Intelligence., Proceedings of the First IEEE Conference on, pp. 579–584. IEEE (1994)Google Scholar
  36. 36.
    Kazarlis, S., Petridis, V.: Varying fitness functions in genetic algorithms: studying the rate of increase of the dynamic penalty terms. In: International conference on parallel problem solving from nature, Springer, New York, pp. 211–220 (1998)Google Scholar
  37. 37.
    Hasançebi, O., Erbatur, F.: Constraint handling in genetic algorithm integrated structural optimization. Acta Mech. 139(1–4), 15–31 (2000)zbMATHGoogle Scholar
  38. 38.
    Barbosa, H.J.C., Lemonge, A.C.C.: An adaptive penalty method for genetic algorithms in constrained optimization problems. In: Frontiers in Evolutionary Robotics. InTech (2008)Google Scholar
  39. 39.
    Lemonge, A.C.C., Barbosa, H.J.C.: An adaptive penalty scheme for genetic algorithms in structural optimization. Int. J. Numer. Methods Eng. 59(5), 703–736 (2004)zbMATHGoogle Scholar
  40. 40.
    Kuri-Morales, A.F., Gutiérrez-García, J.: Penalty function methods for constrained optimization with genetic algorithms: a statistical analysis. In: Mexican International Conference on Artificial Intelligence, Springer, New York, pp. 108–117 (2002)Google Scholar
  41. 41.
    Morales, A.K., Quezada, C.V.: A universal eclectic genetic algorithm for constrained optimization. In: Proceedings of the 6th European congress on intelligent techniques and soft computing, vol. 1, pp. 518–522 (1998)Google Scholar
  42. 42.
    Elsied, M., Oukaour, A., Gualous, H., Hassan, R.: Energy management and optimization in microgrid system based on green energy. Energy 84, 139–151 (2015)Google Scholar
  43. 43.
    Saad, W., Han, Z., Poor, H.V.: Coalitional game theory for cooperative micro-grid distribution networks. In: Communications Workshops (ICC), 2011 IEEE International Conference on, IEEE, pp. 1–5 (2011)Google Scholar
  44. 44.
    Mohamed, F.A., Koivo, H.N.: Modelling and environmental/economic power dispatch of microgrid using multiobjective genetic algorithm optimization. In: Fundamental and Advanced Topics in Wind Power. InTech (2011)Google Scholar

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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