Comparative Survey of Swarm Intelligence Optimization Approaches for ANN Optimization

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 624)

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.

Keywords

Swarm intelligence Particles swarm optimization algorithm Intelligent water drop approach Artificial bee colony algorithm Ant colony optimization approach 

Notes

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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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Electronics & Communication EngineeringChandigarh Engineering CollegeLandranIndia

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