期刊名称：International Journal of Advances in Soft Computing and Its Applications
印刷版ISSN：2074-8523
出版年度：2013
卷号：5
期号：1
出版社：International Center for Scientific Research and Studies
摘要：Most optimization problems have constraints. The solutions of the problem are obtained from the final result 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
