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Publications (2)0 Total impact

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    ABSTRACT: In this paper, Differential Evolution (DE) that incorporates fuzzy control and k-nearest neighbors algorithm is proposed to tackle the economic load dispatch problem. To provide the self-terminating ability, a technique called Iteration Windows (IW) is introduced to govern the number of iteration in each searching stage during the optimization. The size of IW is controlled by a fuzzy controller, which uses the information provided by the k-nearest neighbors system to analyze the population during the searching process. The controller keeps controlling the IW till the end of the searching process. A wavelet based mutation process is embedded in the DE searching process to enhance the searching performance. The weight F of DE is also controlled by the fuzzy controller to further speed up the searching process. The proposed method is employed to solve the Economic Load Dispatch with Valve-Point Loading (ELD-VPL) Problem. It is shown empirically that the proposed method can terminate the searching process with a reasonable number of iteration and performs significantly better than the conventional methods in terms of convergence speed and solution quality.
    Fuzzy Systems (FUZZ), 2011 IEEE International Conference on; 07/2011
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    ABSTRACT: Differential Evolution (DE) is one of the evolutionary algorithms under active research. It has been successfully applied to many real world problems. In this paper, an improved DE with a novel mutation scheme is proposed. The improved DE assigns a distinct scale factor for each individual mutation based on the fitness associated with each base vector involved in the mutation. With the adoption of different scale factors for mutation, DE is capable of searching more locally around superior points and explore more broadly around inferior points. Consequently, a good balance between exploration and exploitation can be achieved. Also, an adaptive base vector selection scheme is introduced to DE. This scheme is capable of estimating the complexity of objective functions based on the population variance. When the problem is simple, it will tend to select good vectors as base vector which will lead to quick convergence. When the objective function is complex, it will select base vector randomly so that the population maintains a high exploration capability and will not be trapped into local minima so easily. A suite of 12 benchmark functions are used to evaluate the performance of the proposed method. The simulation result shows that the proposed method is promising in terms of convergence speed, solution quality and stability.
    Proceedings of the IEEE Congress on Evolutionary Computation, CEC 2011, New Orleans, LA, USA, 5-8 June, 2011; 01/2011