Abstract
Large-scale global optimization is one of the most challenging problems in the domain of stochastic optimization. Due to high dimensionality in the entire optimization process, different types of problems may occur for finding the global optima, e.g., solution space increases exponentially, problem complexity increases, and candidate search direction also increases exponentially. So, deterministic optimization algorithms cannot perform well for this kind of problems. Differential evolutionary algorithm is a population-based, stochastic search and optimization algorithm which can be used for global optimization problems. In this paper, we present self-adaptive dynamic population-based differential evolutionary algorithm which automatically adapts its parameters including population size.
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Chauhan, S., Banerjee, S., Jana, N.D. (2015). Large-Scale Global Optimization Using Dynamic Population-Based DE. In: Mandal, D., Kar, R., Das, S., Panigrahi, B. (eds) Intelligent Computing and Applications. Advances in Intelligent Systems and Computing, vol 343. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2268-2_27
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DOI: https://doi.org/10.1007/978-81-322-2268-2_27
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