Abstract
Swarm intelligence (SI) approaches are a group of populace-dependent, nature influenced meta-heuristic approaches that are impressed via collective intelligence of homogeneous insects, birds, etc. These algorithms simulate the behaviour of the group of homogeneous biological entities to get a global ideal solution in optimization problems, where classical optimization algorithms may fail. Examples consist of a flock of birds, colonies of bees, colonies of ants, school of fish, etc. This paper presents a comparative study of different swarm intelligence approaches: particles swarm optimization (PSO) algorithm, intelligent water drop (IWD) approach, artificial bee colony (ABC) algorithm and ant colony optimization (ACO) algorithm for the optimization of single-layer neural networks.
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Acknowledgements
The authors would like to convey special thanks to the Direction of Research and Innovation Centre in CEC-ECE Department of CGC Landran to give the special assistance that made preparation of this paper possible.
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Kaur, J., Kalra, A., Sharma, D. (2018). Comparative Survey of Swarm Intelligence Optimization Approaches for ANN Optimization. In: Singh, R., Choudhury, S., Gehlot, A. (eds) Intelligent Communication, Control and Devices. Advances in Intelligent Systems and Computing, vol 624. Springer, Singapore. https://doi.org/10.1007/978-981-10-5903-2_32
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DOI: https://doi.org/10.1007/978-981-10-5903-2_32
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