Performance estimation of differential evolution, particle swarm optimization and cuckoo search algorithms
Автор: Pankaj P. Prajapati, Mihir V. Shah
Статья в выпуске: 6 vol.10, 2018 года.
Most design optimization problems in engineering are in general extremely nonlinear and deal with various design variables under complex restrictions. Traditional mathematical optimization procedure may fail to find the optimum solution to real-world problems. Evolutionary Algorithms (EAs) can serve as an efficient approach for these types of optimization problems. In this paper, Particle Swarm Optimization (PSO), Differential Evolution (DE) and Cuckoo Search (CS) algorithms are used to find the optimal solution for some typical unimodal and multimodal benchmark functions. The source codes of all these algorithms are developed using C language and tested on a core i5, 2.4 GHz processor with 8 GB internal RAM. PSO algorithm has a simplicity of implementation and good convergence speed. In contrast, CS algorithm has good ability to find a global optimum solution. To use the advantages of CS and PSO algorithms, a hybrid algorithm of CS and PSO (CSPSO) is implemented and tested with the same benchmark functions. The experimental simulation results obtained by all these algorithms show that hybrid CSPSO outperforms with PSO, DE and CS algorithms.
Optimization, Benchmark Function, Unimodal, Multimodal, Differential Evolution Algorithm, Particle Swarm Optimization Algorithm, Cuckoo Search Algorithm, Hybrid Algorithm
Короткий адрес: https://readera.ru/15016500
IDR: 15016500 | DOI: 10.5815/ijisa.2018.06.07
Список литературы Performance estimation of differential evolution, particle swarm optimization and cuckoo search algorithms
- M. Dorigo and G. Di. Caro, New ideas in optimization, McGraw-Hill Ltd., UK, ISBN: 0-07-709506-5, 1999.
- K. Deb, A. Pratap, S. Agarwal and T. Meyarivan, A fast and elitist multi-objective Genetic Algorithm: NSGA-II, IEEE Transaction on Evolutionary Computation, Vol. 6, No. 2, pp. 182-197, 2002. DOI:10.1109/4235.996017
- M. Lovbjerg, Improving PSO by hybridization of stochastic search heuristics and self-organized criticality, Master Thesis, Aarhus Universitet, Denmark, 2002.
- D. H. Wolpert and W. G. Macread, No free lunch theorems for optimization, IEEE Trans. On Evol. Comput. Vol. 1, No. 1, pp. 67–82, 1997. DOI :10.1109/4235.585893
- J. Holland, Adaption in natural and artificial systems, Ann Arbor, University of Michigan Press, 1975.
- J. Naomi Rosenfield Boeira, The effects of "Preferentialism" on a Genetic Algorithm population over elitism and regular development in a Binary F6 fitness function, International Journal of Intelligent Systems and Applications (IJISA), Vol.8, No.9, pp.38-46, 2016. DOI: 10.5815/ijisa.2016.09.05
- J. Kennedy and R. C. Eberhart, Particle Swarm Optimization, Proc. of IEEE Int. Conf. on Neural Networks, Piscataway, NJ, pp. 1942-1948, 1995.
- M. Dorigo, V. Maniezzo and A. Golomi, Ant system: optimization by a colony of cooperating agents, IEEE Trans. Syst. Man Cybernet, Vol. 26, No. 1, pp. 29–41, 1996. DOI: 10.1109/3477.484436
- R. Stron and K. Price, Differential Evolution - a simple and efﬁcient heuristic for global optimization over continuous spaces, Journal of Global Optimization, Vol. 11, No. 4, pp. 341–359, 1997.
- D. Karaboga, An idea based on Honey Bee swarm for numerical optimization., Technical Report-TR-06, Erciycs University, Engineering Faculty, Computer Engineering Dept., 2005.
- X. S. Yang and S. Deb, Cuckoo search via L´evy flights, in Proc. of World Congress on Nature & Biologically Inspired Computing (NaBIC 2009), India. IEEE Publications, USA, pp. 210-214, 2009.
- X. S. Yang, Firefly algorithm, stochastic test functions and design optimization, International Journal of Bio-Inspired Computation, Vol. 2, No. 2, pp.78–84, 2010.
- Yongbo Sui, Lingzhi Yi and Wenxin Yu, A novel and improved Firefly Algorithm based on two order oscillation, International Journal of Intelligent Systems and Applications (IJISA), Vol.9, No.5, pp.19-26, 2017. DOI: 10.5815/ijisa.2017.05.03
- X. S. Yang, A new metaheuristic Bat-inspired Algorithm, in J. R. Gonzalez et al. (Eds.): Nature Inspired Cooperative Strategies for Optimisation (NICSO 2010), Vol. 284, pp.65–74, Springer, SCI, 2010.
- Menga, X. Z. Gaob, Y. Liuc and H. Zhanga, A novel Bat Algorithm with habitat selection and Doppler effect in echoes for optimization, Expert Systems with Applications, Vol. 42, No. 17–18, 6350–6364, 2015.
- H. Salimi, Stochastic Fractal Search: A Powerful Metaheuristic Algorithm, Knowledge-Based Systems, Vol. 75, pp. 1-18, 2015.
- Z. Bayraktar, M. Komurcu and D. H. Werner, Wind Driven Optimization (WDO): A novel Nature Inspired Optimization Algorithm and its application to electromagnetics, IEEE Int. conf., Antennas and Propagation Society International Symposium (APSURSI), Toronto, pp. 1-4, 2010.
- S. Mirjalilia, S. M. Mirjalilib and A. Lewisa, Grey Wolf Optimizer, Advances in Engineering Software, Vol. 69, pp. 46-61, 2014. DOI: http://dx.doi.org /10.1016/j.advengsoft. 2013.12.007
- M. Cheng and D. Prayogo, Symbiotic Organisms Search: A new metaheuristic optimization algorithm, Computers & Structures, Vol. 139, pp. 98–112, 2014.
- Hanan A. R. Akkar and Firas R. Mahdi, Grass Fibrous Root Optimization Algorithm, International Journal of Intelligent Systems and Applications (IJISA), Vol.9, No.6, pp.15-23, 2017. DOI: 10.5815/ijisa.2017.06.02
- B. V. Chawda and J. M. Patel, Investigating performance of various natural computing algorithms, International Journal of Intelligent Systems and Applications (IJISA), Vol. 9, No.1, pp.46-59, 2017. DOI: 10.5815/ijisa. 2017.01.05
- V. Arunachalam, Optimization using Differential Evolution, Water Resources Research Report no. 60, Facility for Intelligent Decision Support, Department of Civil and Environmental Engineering, London, Ontario, Canada, July 2008.
- P. Civicioglu and E. Besdok, A conceptual comparison of the Cuckoo Search, Particle Swarm Optimization, Differential Evolution and Artificial Bee Colony algorithms, Springer, Science+Business Media B.V., July 2011.
- I. Fister Jr., D. Fister and I. Fister, A comprehensive review of Cuckoo Search: variants and hybrids, International Journal of. Mathematical Modelling and Numerical Optimization, Vol. 4, No. 4, 2013.
- Roy and S. Chaudhuri, Cuckoo Search Algorithm using Lèvy flight: A Review, International Journal of Modern Education and Computer Science, Vol. 5, No.12, pp. 10-15, Dec-2013. DOI: 10.5815/ijmecs.2013.12.02
- M. Jamil and X. S. Yang, A literature survey of benchmark functions for global optimization problems, Int. Journal of Mathematical Modeling and Numerical Optimization, Vol. 4, No. 2, pp. 150–194, 2013.
- R. A. Thakker, M. Shojaei Baghini and M. B. Patil, Low-power low-voltage analog circuit design using Hierarchical Particle Swarm Optimization, IEEE VLSI Design, pp. 427-432, 2009.