doi:10.3850/978-981-08-7618-0_1368

ABSTRACT

The computational mathematics has received much attention in recent years for decision making. Since the realworld science and engineering decision making problems are complex multimodal, hence conventional optimization methods fails to optimize. The increasing demand for efficient and robust optimization strategies has invited stochastic algorithms for such complex decision making problems. These algorithms are the extraction of natural phenomenon for problem solving and are based on random process. The objective of this paper is to investigate and analyze different stochastic algorithms on complex multimodal optimization problems. The comprehensive experimental analysis of different stochastic algorithms are carried out on a set of standard complex 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 discussed.

Keywords: Evolutionary algorithm, Swarm intelligence, Multimodal.

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