Comparative Survey of Swarm Intelligence Optimization Approaches for ANN Optimization
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.