Abstract
Over the past decade, Neural Networks have been dominating in terms of versatility and technological success. As are result it has aided numerous breakthroughs and has consequently found its application in almost every field of modern science. The Deep ANNs are constructed through a wide range of steps and processes. Optimization is an important part of the Civil Engineering, Financial Modelling et cetra. There are a lot of such algorithms that have been proposed. Swarm optimization techniques are modelled after organisms belonging to the animal kingdom and have proven to be very successful when-paired with neural networks. Periods of scarcity force organisms to develop instinctive optimization techniques that are simulated in a way that it helps solve non-linear optimization problems. This paper reviews Bacterial Foraging, Ant Colony and Grey Wolf optimization algorithms and discusses the application of these algorithms in Neural Networks and Deep learning.
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