Abstract
The domain of bio-inspired computing is increasingly becoming important in today’s information era. More and more applications of these intelligent methods are being explored by information scientists for different contexts. While some studies are exploring the application of these algorithms, other studies are highlighting the improvement in the algorithms. In this study, we identify five more popular algorithms and briefly describe their scope. These methods are neural networks, genetic algorithm, ant colony optimization, particle swarm optimization, and artificial bee colony algorithms. We highlight under what context these algorithms are suitable and what objectives could be enabled by them. This would pave the path for studies conducted in the future to choose a suitable algorithm.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Aytug, H., Khouja, M., & Vergara, F. E. (2003). Use of genetic algorithms to solve production and operations management problems: a review. International Journal of Production Research, 41(17), 3955–4009.
Bounds, D.G., Lloyd, P.J., Mathew, B., & Waddell, G. (1988). A multilayer perceptron network for the diagnosis of low back pain. IEEE International Conference on Neural Networks, 481–489.
Colin, R.R., & Jonathan, E.R. (2002). Genetic algorithms-Principles and perspectives.
Dimopoulos, C., & Zalzala, A. (2000). Recent developments in evolutionary computation for manufacturing optimization: problems, solutions, and comparisons. IEEE Transactions on Evolutionary Computation, 4(2), 93–113.
Dorigo, M., & Blum, C. (2005). Ant colony optimization theory: A survey. Theoretical computer science, 344(2), 243–278.
Dorigo, M., & Birattari, M. (2010). Ant colony optimization. In Encyclopedia of machine learning (pp. 36–39). Springer US.
Dorigo, M., Birattari, M., & Stützle, T. (2006). Ant colony optimization. Computational Intelligence Magazine, IEEE, 1(4), 28–39.
Floreano, D., & Mattiussi, C. (2008). Bio-inspired artificial intelligence: Theories, methods, andtechnologies. Cambridge: MIT Press.
Gandomi, A. H., Yun, G. J., Yang, X. S., & Talatahari, S. (2013). Chaos-enhanced accelerated particle swarm optimization. Communications in Nonlinear Science and Numerical Simulation, 18(2), 327–340.
Gao, W. F., & Liu, S. Y. (2012). A modified artificial bee colony algorithm. Computers & Operations Research, 39(3), 687–697.
Grossberg, S. (1988). Nonlinear neural networks: Principles, mechanisms, and architectures. Neural networks, 1(1), 17–61.
Holland, J. H. (1975). Adaptation in natural and artificial systems: An introductory analysis with applications to biology, control, and artificial intelligence.
Hornik, K. (1991). Approximation capabilities of multilayer feedforward networks. Neural networks, 4(2), 251–257.
Kar, A.K. (2015). A hybrid group decision support system for supplier selection using analytic hierarchy process, fuzzy set theory and neural network. Journal of Computational Science, 6, 23–33.
Karaboga, D. (2005). An idea based on honey bee swarm for numerical optimization (Vol. 200). Technical report-tr06, Erciyes University, engineering faculty, computer engineering department.
Karaboga, D., Gorkemli, B., Ozturk, C., & Karaboga, N. (2014). A comprehensive survey: artificial bee colony (ABC) algorithm & applications. Artificial Intelligence Review, 42(1), 21–57.
Liao, T., Socha, K., Montes de Oca, M., Stutzle, T., & Dorigo, M. (2014). Ant colony optimization for mixed-variable optimization problems. Evolutionary Computation, IEEE Transactions on, 18(4), 503–518.
Liao, T., Stützle, T., deOca, M.A.M., & Dorigo, M. (2014). A unified ant colony optimization algorithm for continuous optimization. European J. of Operational Research, 234(3), 597–609.
Mitchell, M., Forrest, S., & Holland, J. H. (1992). The royal road for genetic algorithms: Fitness landscapes and GA performance. European Conf. on artificial life (pp. 245–254).
Oja, E. (1992). Principal components, minor components, and linear neural networks. Neural Networks, 5(6), 927–935.
Reeves, C. (2003). Genetic algorithms. Handbook of Metaheuristics, 57, 55–82.
Sadegh, N. (1993). A perceptron network for functional identification and control of nonlinear systems. IEEE Transactions on Neural Networks, 4(6), 982–988.
Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks, 61, 85–117.
Specht, D. F. (1990). Probabilistic neural networks. Neural networks, 3(1), 109–118.
Srinivas, M., & Patnaik, L. M. (1994). Adaptive probabilities of crossover and mutation in genetic algorithms. IEEE Transactions on Systems, Man and Cybernetics, 24(4), 656–667.
Yang, X. S., Cui, Z., Xiao, R., Gandomi, A. H., & Karamanoglu, M. (Eds.). (2013). Swarm intelligence and bio-inspired computation: theory and applications. Newnes.
Zhan, Z. H., Zhang, J., Li, Y., & Shi, Y. H. (2011). Orthogonal learning particle swarm optimization. IEEE Transactions on Evolutionary Computation, 15(6), 832–847.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer India
About this paper
Cite this paper
Chakraborty, A., Kar, A.K. (2016). A Review of Bio-Inspired Computing Methods and Potential Applications. In: Lobiyal, D., Mohapatra, D., Nagar, A., Sahoo, M. (eds) Proceedings of the International Conference on Signal, Networks, Computing, and Systems. Lecture Notes in Electrical Engineering, vol 396. Springer, New Delhi. https://doi.org/10.1007/978-81-322-3589-7_16
Download citation
DOI: https://doi.org/10.1007/978-81-322-3589-7_16
Published:
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-3587-3
Online ISBN: 978-81-322-3589-7
eBook Packages: EngineeringEngineering (R0)