Self Organizing Migrating Algorithm with Nelder Mead Crossover and Log-Logistic Mutation for Large Scale Optimization

Abstract

This chapter presents a hybrid variant of self organizing migrating algorithm (NMSOMA-M) for large scale function optimization, which combines the features of Nelder Mead (NM) crossover operator and log-logistic mutation operator. Self organizing migrating algorithm (SOMA) is a population based stochastic search algorithm which is based on the social behavior of group of individuals. The main characteristics of SOMA are that it works with small population size and no new solutions are generated during the search, only the positions of the solutions are changed. Though it has good exploration and exploitation qualities but as the dimension of the problem increases it trap to local optimal solution and may suffer from premature convergence due to lack of diversity mechanism. This chapter combines NM crossover operator and log-logistic mutation operator with SOMA in order to maintain the diversity of population and to avoid the premature convergence. The proposed algorithm has been tested on a set of 15 large scale unconstrained test problems with problem size taken as up to 1000. In order to see its efficiency over other population based algorithms, the results are compared with SOMA and particle swarm optimization algorithm (PSO). The comparative analysis shows the efficiancy of the proposed algorithm to solve large scale function optimization with less function evaluations.