A Review of Population-Based Optimization Algorithms

This paper discusses the different types of population-based optimization algorithms. It reviews several works done by a number of authors on these algorithms, highlighting their strengths and weaknesses. Specifically, this paper analyses the main components of a good optimization algorithms which are: Local Search, Global Search, and Randomness and it concludes, that to enjoy a good search, these components must be present in any good stochastic algorithm. Furthermore, the paper asserts that identification of the best solution in every iteration is a necessary criterion. The lack of any of these components, therefore, is the major of reason why some optimizations algorithms have not been as efficient and effective as envisaged at their design phases.