Abstract
Differential evolution (DE) is arguably one of the most powerful stochastic real-parameter optimization algorithms. DE has drawn the attention of many researchers resulting in a lot of variants of the classical algorithm with improved performance. This paper presents a new modified differential evolution algorithm for minimizing continuous space. New differential evolution operators for realizing the approach are described, and its performance is compared with several variants of differential evolution algorithms. The proposed algorithm is basedon the idea of performing biased initial population. By means of an extensive testbed it is demonstrated that the new method converges faster and with more certainty than many other acclaimed differential evolution algorithms. The results indicate that the proposed algorithm is able to arrive at high quality solutions in a relatively short time limit: for the largest publicly known problem instance, a new best solution could be found.
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Ramezani, F., Lotfi, S. (2012). The Modified Differential Evolution Algorithm (MDEA). In: Pan, JS., Chen, SM., Nguyen, N.T. (eds) Intelligent Information and Database Systems. ACIIDS 2012. Lecture Notes in Computer Science(), vol 7198. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28493-9_13
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DOI: https://doi.org/10.1007/978-3-642-28493-9_13
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