Document Type : Review Article
Authors
University of Tafresh, Tafresh, Iran
Abstract
Meta-heuristic methods are global optimization algorithms which are widely used in the engineering issues, nowadays. In this paper, a new stochastic search for optimization is presented using variable variance Gaussian distribution sampling. The main idea in searching for algorithm is to regenerate new samples around each solution with a Guassian distribution. Numerical simulations have revealed that the new presented algorithm outperformed some evolutionary algorithms.
Keywords
[1] Richard Malek, Global optimization through meta-heuristic collaboration in a multi-agent system, 2009.
[2] F. Ramezani, S. Lotfi, Social-Based Algorithm (SBA), Appl. Soft Comput. J. 13 (2012)2837–2856.
[3] Atashpaz-Gargari, E., Lucas, C., Imperialist Competitive Algorithm: An algorithm for optimization inspired by imperialistic competition, IEEE Congress on Evolutionary Computation, Singapore, 2007, pp.4661–4667.
[4] M.Riedmiller, H.Braun, A direct adaptive method for faster back propagation learning (RPROP}, Proc. of the IEEE Intl. Conf. on Neural Networks,1993, pp. 586–591.
[5] I.G.Tsoulos, I.E.Lagaris, MinFinder: locating all the local minima of a function, Comput. Phys. Commun. Journal. 174 (2006) 166–179.
[6] A. Colorni, M. Dorigo et V. Maniezzo, Distributed Optimization by Ant Colonies, actes de la première conference europeenne sur la vie artificial, Paris, France, Elsevier Publishing. (1991) 134-142.
[7] S. Müeller, J. Marchetto, S. Airaghi, P. Koumoutsakos, Optimization Based on Bacterial Chemotaxis, IEEE Trans on Evolutionary Computation. 6 (2002) 16-29.
[8] A. P. Philipse, D. Maas., Magnetic Colloids from Mag-netotactic Bacteria: Chain Formation and Colloidal Stability, Langmuir. 18(2002) 9977-9984.
[9] D. Simon. Biogeography-based Optimization, IEEE Trans on Evolutionary Computation. 12(2008) 702-713.
[10] G. Marsaglia and T. A. Bray, A convenient method for generating normal variables, SIAM Rev. 6, 260–264, 1964
[11] G. E. P. Box and Mervin E. Muller, A Note on the Generation of Random Normal Deviates, The Annals of Mathematical Statistics (1958), Vol. 29, No. 2 pp. 610–611
[12] George Marsaglia; Wai Wan Tsang (2000). "The Ziggurat Method for Generating Random Variables". Journal of Statistical Software 5 (8). Retrieved 2007-06-20.
)