Abstract

The problem of optimization of target functions in machine learning plays a vital role in accelerating the learning process, so much so that mapping of knowledge on the system shows the minimum error rate. An optimization algorithm iteratively executes in a search space, to find among them, the proper solutions and compares them accordingly, until the best solution is found. We present some of the most popular optimization techniques widely used presently - ant colony optimization, particle swarm optimization, artificial bee colony and bat algorithm.