A Comparison Study of Biogeography based Optimization for Optimization Problems

Most optimization problems have constraints. The solutions of the problem are obtained from the final results of the search space that have satisfied the given constraints. In such cases, heuristic algorithms are capable to find the estimated solutions, but sometimes they have some limitations. This paper investigates the performance of three heuristic optimization methods: Biogeography Based Optimization (BBO), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for solving the optimization problems. We compare these algorithms in terms of their convergence time and their performance in avoiding local minima on fourteen benchmark functions. These benchmark functions are used to test optimization procedures for multidimensional and continuous optimization task. The findings reveal that BBO is a promising optimization tool that can deal with the complex optimization problems.