Abstract
Differential evolution is a real value encoded evolutionary algorithm for global optimization. It has gained popularity due to its simplicity and efficiency. Use of special kind of mutation and crossover operators differentiates it from other evolutionary algorithms. In recent times, it has been widely used in different fields of science and engineering. Among recently developed various variants of differential evolution, a modified technique called Modified Differential Evolution based Fuzzy Clustering (MoDEFC-V1), was proposed by the authors of this article to improve the speed and accuracy of convergence of differential evolution with a new mutation operation. However, it has a certain limitation of finding global optimum value while searching in solution space. To overcome the limitation of MoDEFC-V1, in this article, we have proposed two different improved versions of MoDEFC called MoDEFC-V2 and MoDEFC-V3 in order to do the underlying optimization such as clustering of patterns better. The effectiveness of the proposed versions is demonstrated for two synthetic and four real-life datasets. Moreover, the superiority of MoDEFC-V2 and MoDEFC-V3 is shown by comparing with state-of-the-art methods qualitatively and quantitatively. Finally, two sample independent one-tailed t-test is performed in order to judge the superiority of the results produced by the proposed versions.
J. P. Sarkar and I. Saha—Contributed equally.
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Acknowledgements
This work was partially supported by the grant from Department of Science and Technology, India (DST/INT/Pol/P-36/2016).
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Sarkar, J.P., Saha, I., Sarkar, A., Maulik, U. (2018). Improving Modified Differential Evolution for Fuzzy Clustering. In: Abraham, A., Muhuri, P., Muda, A., Gandhi, N. (eds) Hybrid Intelligent Systems. HIS 2017. Advances in Intelligent Systems and Computing, vol 734. Springer, Cham. https://doi.org/10.1007/978-3-319-76351-4_14
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