出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：Global optimization is an important branch of computational mathematics that finds the applications in every walk of life. The real-world science and engineering optimization applications are becoming more and more complex in nature and are generally multimodal. The conventional optimization methods fail to optimize such complex multimodal problems. Hence there is always an increasing demand for efficient and robust optimization strategies. In recent years, algorithms based on random process have become popular and are an alternative to conventional methods in optimization. The Stochastic Algorithms are based on random process and most of them are the extraction of natural phenomenon for problem solving. The objective of this paper is to investigate and analyze stochastic algorithms on complex multimodal optimization problems. The comprehensive analysis of different stochastic algorithms is carried out on a set of standard benchmark problems with 10, 30 and 50 dimensions. The algorithmic suitability, robustness and convergence rate of each will be investigated. Finally the dependency of Stochastic Algorithms on problem dimensions are also discussed..
