Hybridization of self organizing migrating algorithm with quadratic approximation and non uniform mutation for function optimization

Abstract

Self-organizing migrating algorithm (SOMA) is relatively a new population- based stochastic search technique for solving nonlinear global optimization problems. There has been done very less work on hybridization of SOMA with other methodologies in order to improve its performance. This paper presents hybridization of self-organizing migrating algorithm with quadratic approximation or interpolation (SOMAQI) and non-uniform mutation. This hybridization (M-SOMAQI) uses the quadratic interpolation (QI) and non-uniform mutation for creating a new solution vector in the search space. To validate the efficiency of this algorithm, it is tested on 15 benchmark test problems taken from the literature, and the obtained results are compared with SOMA and the SOMAQI. The numerical and graphical results conclude that the presented algorithm shows better performance in terms of population size, efficiency, reliability, and accuracy.