Abstract
This paper concentrates on four very similar metaheuristic optimization algorithms: Differential Evolution (DE), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Cuckoo Search (CS) algorithm. These optimization algorithms are used to solve optimization problems with real parameters having real parametric functions. This paper gives a brief discussion of these algorithms followed by the experiment over various benchmark functions. Many researchers have attempted to compare these algorithms on various benchmark functions. This work compares these algorithms on high dimensions over benchmark functions like Ackley’s function, Alpine function, Brown function, Deb function, and Powell sum function. These above algorithms are compared on the basis of time required to converge on various benchmark functions. Our experiments indicate that the CS algorithm outperforms others when the dimensions are high, whereas in some cases, it is comparable to DE.
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References
E.K. Nyarko, R. Cupec, D. Filko, A comparison of several heuristic algorithms for solving high dimensional optimization problems. Int. J. Electr. Comput. Eng. Syst. 1, 1–8 (2014)
D. Whitley, A genetic algorithm tutorial. Stat. Comput. 65–85 (1994)
Differential evolution. Available at: https://en.wikipedia.org/wiki/Differential_evolution [Online]. Accessed 9 Nov 2017
R. Storn, K. Price, Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 4, 341–359 (1997)
Particle swarm optimization. Available at: https://en.wikipedia.org/wiki/Particle_swarm_optimization [Online]. Accessed 10 Nov 2017
H. Singh, B. Singh, A comparison of optimization algorithms for standard benchmark functions. Int. J. 7 (2017)
J. Kennedy, Particle swarm optimization, in Encyclopedia of Machine Learning (Springer, US, 2011), pp. 760–766
“Cuckoo Search”. Available at: https://en.wikipedia.org/wiki/Cuckoo_search [Online]. Accessed 10 Nov 2017
A.H. Gandomi, X.S. Yang, A.H. Alavi, Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng. Comput. 29(1), 17–35 (2013)
S. Surjanovic, Ackley function. Available at: https://www.sfu.ca/~ssurjano/ackley.html [Online]. Accessed 10 Nov 2017
MathWorks. Ackley function. Available at: https://in.mathworks.com/matlabcentral/fileexchange/37000ackleyfunction?focused=5234563&tab=function [Online]. Accessed 10 Nov 2017
M. Clerc, Alpine function. Available at: http://clerc.maurice.free.fr/pso/Alpine/Alpine_Function.htm [Online]. Accessed 10 Nov 2017
Brown function. Available at: http://mathworld.wolfram.com/BrownFunction.html [Online]. Accessed 11 Nov 2017
X.S. Yang, Nature-Inspired Optimization Algorithms (Elsevier, New York, 2014)
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Singla, M., Shukla, K.K. (2019). Experimental Evaluation of Nature-Inspired Algorithms on High Dimensions. In: Krishna, C., Dutta, M., Kumar, R. (eds) Proceedings of 2nd International Conference on Communication, Computing and Networking. Lecture Notes in Networks and Systems, vol 46. Springer, Singapore. https://doi.org/10.1007/978-981-13-1217-5_60
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DOI: https://doi.org/10.1007/978-981-13-1217-5_60
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