A New Hybrid Intelligent Search Method to Find Global Optimal Solution for Engineering Problems

Document Type : Power Article

Authors

1 Computer Department of Islamic Azad University

2 Department of Energy Management and Optimization, Institute of Science and High Technology and Environmental Sciences, Graduate University of Advanced Technology, Kerman, Iran

Abstract

The complexity of engineering problems and the existence of various constraints on these issues, encourage the researchers to use of innovative methods based on a heuristic algorithm to find the optimal solution for practical problems at a cost-effective time and non-consistency tolerance. A distinction has been made between various issues and, therefore, extensive research has been done to improve heuristic algorithms in order to enhance their ability to solve engineering and practical problems. In this paper, due to the ability to global search some of the metaheuristic search patterns (such as the EMA algorithm) and the ability to local search for some meta-heuristic search patterns (such as the FPA algorithm), a novel combination method is proposed to use the ability of both types of algorithms. Then, using the proposed method, a hybrid search pattern with new abilities is presented, whose abilities are proven on standard benchmark testing functions as well as solving engineering problems.

Keywords

Main Subjects


[1]        حسین شریف زاده و نیما امجدی، "مروری بر انواع الگوریتم‌های فراکاوشی در بهینه‌سازی"، نشریه مدل سازی در مهندسی، دوره 12، شماره 38، پائیز 1393، صفحه 27-43.
[2]        R. L. Haupt, and S. E. Haupt, Practical genetic algorithms, 2th ed., Wiley, NJ, USA, 1998.
[3]        Z.-L. Gaing, "Particle swarm optimization to solving the economic dispatch considering the generator constraints", IEEE transactions on power systems, vol. 18, no. 3, August 2003, pp. 1187-1195.
[4]        Y. Shi, “Particle swarm optimization: developments, applications and resources”, in IEEE Proceedings of the 2001 Congress on Evolutionary Computation, Seoul, South Korea, vol. 1, May 2001, pp. 81-86.
[5]        A. R. Mehrabian, and C. Lucas, "A novel numerical optimization algorithm inspired from weed colonization", Ecological informatics, vol. 1, no. 4, December 2006, pp. 355-366.
[6]        Y. Kumar, and P. K. Singh, "Improved cat swarm optimization algorithm for solving global optimization problems and its application to clustering", Applied Intelligence, vol. 48, no. 9, September 2018, pp. 2697-2681.
[7]        J. Zhang, Y. Zhou, and Q. Luo, "Nature-inspired approach: a wind-driven water wave optimization algorithm", Applied Intelligence, vol. 49, no. 1, January 2019, pp. 233–252.
[8]        E. Cuevas, A. Reyna-Orta, and M.-A. Díaz-Cortes, "A Multimodal Optimization Algorithm Inspired by the States of Matter",  Neural Processing Letters, vol. 48, no. 1, August 2018, pp. 517-556.
[9]        A. Askarzadeh, "A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm", Computers & Structures, vol. 169, June 2016, pp. 1-12.
[10]      S. Łukasik, and P. A. Kowalski, “Study of flower pollination algorithm for continuous optimization”,  in Intelligent Systems' 2014, Warsaw, Poland, vol. 1, September 2014, pp. 451-459.
[11]      X.-S. Yang, “Flower pollination algorithm for global optimization”, in International conference on unconventional computing and natural computation, Orléan, France, vol. 7445, September 2012, pp. 240-249.
[12]      S. Mirjalili, S. M. Mirjalili, and A. Lewis, "Grey wolf optimizer", Advances in engineering software, vol. 69, March 2014, pp. 46-61.
[13]      N. Ghorbani, and E. Babaei, "Exchange market algorithm", Applied Soft Computing, vol. 19, June 2014, pp. 177-187.
[14]      صابر ارمغانی و نیما امجدی، "توزیع بار اقتصادی با در نظر گرفتن آلودگی در سیستم‌های قدرت چندناحیه‌ای با استفاده از الگوریتم بهینه‌سازی فاخته"، نشریه مدل سازی در مهندسی, دوره 12، شماره 37، تابستان 1393، صفحه 89-104.
[15]      مسعود احمدی گرجی و نیما امجدی، "برنامه ریزی توسعه پویای شبکه های توزیع در حضور منابع تولید پراکنده با استفاده از یک الگوریتم بهینه سازی جدید دو سطحی"، نشریه مدل سازی در مهندسی, دوره 13، شماره 43، زمستان 1394، صفحه 143-157.
[16]      N. Ghorbani, and E. Babaei, "Exchange market algorithm for economic load dispatch", International Journal of Electrical Power & Energy Systems, vol. 75, February 2016, pp. 19-27.
[17]      V. K. Kamboj, S. Bath, and J. Dhillon, "Solution of non-convex economic load dispatch problem using Grey Wolf Optimizer", Neural Computing and Applications, vol. 27, no. 5, July 2016, pp. 1301-1316.
[18]      V. K. Kamboj, A. Bhadoria, and S. Bath, "Solution of non-convex economic load dispatch problem for small-scale power systems using ant lion optimizer", Neural Computing and Applications, vol. 28, no. 8, August 2017, pp. 2181-2192
[19]      A. J. Wood, and B. Wollenberg, Power generation operation and control, 2th ed., Wiley, New York, USA, 1996.
[20]      M. Modiri-Delshad, and N. A. Rahim, "Solving non-convex economic dispatch problem via backtracking search algorithm", Energy, vol. 77, December 2014, pp. 372-381.
[21]      L. dos Santos Coelho, and V. C. Mariani, "An improved harmony search algorithm for power economic load dispatch",  Energy Conversion and Management, vol. 50, no. 10, October 2009, pp. 2522-2526.