Abstract
This paper presents a new adaptive differential evolution technique based on logistic map for optimal distribution placement and sizing. The parameters of differential evolution that need to be selected by the user are the key factors for successful operation DE. Choosing suitable values of parameters are difficult for DE, which is usually a problem-dependent task. Unfortunately, there is no fix rule for selection of parameters. The trial-and-error method adopted generally for tuning the parameters in DE requires multiple optimization runs. Even this method can not guarantee optimal results every time and sometimes it may lead to premature convergence. The proposed method combines differential evolution with chaos theory for self adaptation of DE parameters. The performance of the proposed method is demonstrated on a sample test system. It is seen that the proposed method can avoid premature convergence and provides better convergence characteristics. The results obtained by the proposed methods are compared with other methods. The results show that the proposed technique is capable of producing comparable results.
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Mandal, K.K., Bhattacharya, B., Tudu, B., Chakraborty, N. (2011). Logistic Map Adaptive Differential Evolution for Optimal Capacitor Placement and Sizing. In: Panigrahi, B.K., Suganthan, P.N., Das, S., Satapathy, S.C. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2011. Lecture Notes in Computer Science, vol 7076. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27172-4_9
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DOI: https://doi.org/10.1007/978-3-642-27172-4_9
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