SO (complexity)

From formulasearchengine
Revision as of 10:37, 8 November 2013 by en>Yobot (Reference before punctuation detected and fixed using AWB (9585))
Jump to navigation Jump to search

Bat-inspired algorithm is a metaheuristic optimization algorithm developed by Xin-She Yang in 2010.[1] This bat algorithm is based on the echolocation behaviour of microbats with varying pulse rates of emission and loudness.[2][3]

Algorithm Description

The idealization of the echolocation of microbats can be summarized as follows: Each virtual bat flies randomly with a velocity at position (solution) with a varying frequency or wavelength and loudness . As it searches and finds its prey, it changes frequency, loudness and pulse emission rate . Search is intensified by a local random walk. Selection of the best continues until certain stop criteria are met. This essentially uses a frequency-tuning technique to control the dynamic behaviour of a swarm of bats, and the balance between exploration and exploitation can be controlled by tuning algorithm-dependent parameters in bat algorithm.

A detailed introduction of metaheuristic algorithms including the bat algorithm is given by Yang [4] where a demo program in Matlab/Octave is available, while a comprehensive review is carried out by Parpinelli and Lopes.[5] A further improvement is the development of an evolving bat algorithm (EBA) with better efficiency.[6]

A Matlab demo is available at the Matlab exchange[7]

Multi-objective Bat Algorithm (MOBA)

Using a simple weighted sum with random weights, a very effective but yet simple multiobjective bat algorithm (MOBA) has been developed to solve multiobjective engineering design tasks.[8] Another multiobjective bat algorithm by combining bat algorithm with NSGA-II produces very competitive results with good efficiency.[9]

Applications

Bat algorithm has been used for engineering design,[10] classifications.[11] A fuzzy bat clustering method has been developed to solve ergonomic workplace problems[12] An interesting approach using fuzzy systems and bat algorithm has shown a reliable match between prediction and actual data for exergy modelling.[13]

A detailed comparison of bat algorithm (BA) with genetic algorithm (GA), PSO and other methods for training feed forward neural networks concluded clearly that BA has advantages over other algorithms.[14]

References

43 year old Petroleum Engineer Harry from Deep River, usually spends time with hobbies and interests like renting movies, property developers in singapore new condominium and vehicle racing. Constantly enjoys going to destinations like Camino Real de Tierra Adentro.

Template:Swarming

  1. ↑ X. S. Yang, A New Metaheuristic Bat-Inspired Algorithm, in: Nature Inspired Cooperative Strategies for Optimization (NISCO 2010) (Eds. J. R. Gonzalez et al.), Studies in Computational Intelligence, Springer Berlin, 284, Springer, 65-74 (2010). http://arxiv.org/abs/1004.4170
  2. ↑ J. D. Altringham, Bats: Biology and Behaviour, Oxford University Press, (1996).
  3. ↑ P. Richardson, Bats. Natural History Museum, London, (2008)
  4. ↑ Yang, X. S., Nature-Inspired Metaheuristic Algoirthms, 2nd Edition, Luniver Press, (2010).
  5. ↑ Parpinelli, R. S., and Lopes, H. S., New inspirations in swarm intelligence: a survey,Int. J. Bio-Inspired Computation, Vol. 3, 1-16 (2011).
  6. ↑ P. W. Tsai, J. S. Pan, B. Y. Liao, M. J. Tsai, V. Istanda, Bat algorithm inspired algorithm for solving numerical optimization problems, Applied Mechanics and Materials, Vo.. 148-149, pp.134-137 (2012).
  7. ↑ here http://www.mathworks.com/matlabcentral/fileexchange/37582
  8. ↑ X. S. Yang, bat algorithm for multi-objective optimisation, Int. J. Bio-Inspired Computation, Vol. 3, 267-274 (2011).
  9. ↑ T. C. Bora, L. S. Coelho, L. Lebensztajn, Bat-inspired optimization approach for the brushless DC wheel motor problem, IEEE Trans. Magnetics, Vol. 48 (2), 947-950 (2012).
  10. ↑ X. S. Yang and A. H. Gandomi, Bat algorithm: a novel approach for global engineering optimization, Engineering Computations, Vol. 29, No. 5, pp. 464-483 (2012).
  11. ↑ S. Mishra, K. Shaw, D. Mishra, A new metaheuristic classification approach for microarray data,Procedia Technology, Vol. 4, pp. 802-806 (2012).
  12. ↑ Khan, K., Nikov, A., Sahai A., A Fuzzy Bat Clustering Method for Ergonomic Screening of Office Workplaces,S3T 2011, Advances in Intelligent and Soft Computing, 2011, Volume 101/2011, 59-66 (2011).
  13. ↑ T. A. Lemma, Use of fuzzy systems and bat algorithm for exergy modelling in a gas turbine generator, IEEE Colloquium on Humanities, Science and Engineering (CHUSER'2011), pp. 305-310 (2011).
  14. ↑ K. Khan and A. Sahai, A comparison of BA, GA, PSO, BP and LM for training feed forward neural networks in e-learning context, Int. J. Intelligent Systems and Applications (IJISA), Vol. 4, No. 7, pp. 23-29 (2012).
Retrieved from "https://en.formulasearchengine.com/index.php?title=SO_(complexity)&oldid=25709"