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Differential evolution (DE) is an evolutionary algorithm (EA) that uses a rather greedy and less stochastic approach to solve optimization problems than other evolutionary methods [1]. Like other EAs, DE is a population-based, stochastic global optimizer, capable of working reliably in nonlinear and multimodal environments. Due to several features such as simplicity, efficiency and global search capabilities,...
Among the existing meta-heuristic optimization algorithms, a well-known branch is the differential evolution (DE). DE is a powerful population-based algorithm of evolutionary computation field designed for solving global optimization problems which only has a few control parameters. With an eye to improve the performance of DE, in this paper, a DE approach combined with a cultural algorithm technique...
Economic Load Dispatch (ELD) problems are nonlinear constrained problems which occupy an important role in the economic operation of power system. Recently, as an alternative to the conventional mathematical approaches, evolutionary algorithms have been given much attention by researchers due to their ability to find good solutions in ELD problems. In this paper, a biogeography based-optimization...
In this paper, the parameters set of the vector Jiles-Atherton hysteresis model is obtained with an approach based on a modified Differential Evolution (MDE), using generation variant control parameters. Classical DE is a simple and efficient global optimization algorithm. It has been successfully applied to solve a wide range of real-world optimization problems. Numerical comparisons with results...
Differential evolution is an evolutionary algorithm over continuous spaces which incorporates an efficient way of self-adapting mutation using small populations. This paper uses a brushless DC wheel motor benchmark problem to investigate the performance of Differential Evolution. Results are competitive with those of other optimization methods.
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