Improving Modified Differential Evolution for Fuzzy Clustering Jnanendra Prasad Sarkar 1,3 , Indrajit Saha 2(B ) , Anasua Sarkar 3 , and Ujjwal Maulik 3 1 Vodafone India Ltd., Pune, India jpsarkar@outlook.com 2 Department of Computer Science and Engineering, National Institute of Technical Teachers’ Training and Research, Kolkata, India indrajit@nitttrkol.ac.in 3 Department of Computer Science and Enginerring, Jadavpur University, Kolkata, India ashru2006@hotmail.com , umaulik@cse.jdvu.ac.in 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 engineer- ing. 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 over- come 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 clus- tering 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. Keywords: Differential Evolution · Pattern recognition · Clustering Statistical significance test 1 Introduction Clustering is a widely used unsupervised learning technique to solve various real- life pattern recognition problems. It plays an important role to find pattern and J. P. Sarkar and I. Saha—Contributed equally. c  Springer International Publishing AG, part of Springer Nature 2018 A. Abraham et al. (Eds.): HIS 2017, AISC 734, pp. 136–146, 2018. https://doi.org/10.1007/978-3-319-76351-4_14