A chaotic teaching learning based optimization algorithm for clustering problems

This paper presents a teaching learning based algorithm for solving optimization problems. This algorithm is inspired through classroom teaching pattern either students can learn from teachers or from other students. But, the teaching learning based optimization (TLBO) algorithm suffers with premature convergence and lack of tradeoff between local search and global search. Hence, to address the above mentioned shortcomings of TLBO algorithm, a chaotic version of TLBO algorithm is proposed with different chaotic mechanisms. Further, a local search method is also incorporated for effective tradeoff between local and global search and also to improve the quality of solution. The performance of proposed algorithm is evaluated on some benchmark test functions taken from Congress on Evolutionary Computation 2014 (CEC’14). The results revealed that proposed algorithm provides better and effective results to solve benchmark test functions. Moreover, the proposed algorithm is also applied to solve clustering problems. It is found that proposed algorithm gives better clustering results in comparison to other algorithms.

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