Modified Nelder-Mead self organizing migrating algorithm for function optimization and its application

Abstract

In this paper, a modified Nelder Mead Self Organizing Migrating Algorithm (mNM-SOMA) has been presented for solving unconstrained optimization problems. It is based on the hybridization of self organizing migrating algorithm (SOMA) with modified Nelder Mead (mNM) Crossover Operator. SOMA is a low population based technique that has good exploration and exploitation qualities, but sometimes converges premature to local optima solution due to lack of diversity preserve mechanism. In this paper an attempt has been made to improve the efficiency of SOMA using a modified NM crossover operator (mNM) for maintaining the diversity in the search space. mNM-SOMA has been tested on a set of 15 test problems, taken form literature and results are compared with the results obtained by self organizing migrating genetic algorithm (SOMGA), SOMA, genetic algorithm (GA) and particle swarm optimization (PSO). For better presentation, results are also analyzed graphically using a Performance Index. Besides this, mNM-SOMA has also been used to solve Frequency Modulation Sounds Parameter Identification Problem. Analysis of numerical results infers mNM-SOMA as a less expensive robust technique.