SOMGA for Large Scale Function Optimization and Its Application

Abstract

Self Organizing Migrating Genetic Algorithm (SOMGA) is a hybridized variant of Genetic Algorithm (GA) inspired by the features of Self Organizing Migrating Algorithm, presented by Deep and Dipti (IEEE Congr Evol Comput, pp 2796–2803, 2007) [1]. SOMGA extracts the features of binary coded GA and real coded SOMA in such a way that diversity of the solution space can be maintained and thoroughly exploited keeping function evaluation low. It works with very less population size and tries to achieve global optimal solution faster in less number of function evaluations. Earlier SOMGA has been used to solve problems up to 10 dimensions with population size 10 only. This chapter is brake into three sections. In first section a possibility of using SOMGA to solve large scale problem (dimension up to 200) has been analyzed with the help of 13 test problems. The reason behind extension is that SOMGA works with very small population size and to solve large scale problems (dimension 200) only 20 population size is required. On the basis of results it has been concluded that SOMGA is efficient to solve large scale global optimization problems with small population size and hence required lesser function evaluations. In second section, two real life problems from the field of engineering as an application have been solved using SOMGA. In third section, a comparison between two ways of hybridization has been analyzed. There can be two approaches to hybridize a population based technique. Either by incorporating a deterministic local search in it or by merging it with other population based technique. To see the effect of both the approaches on GA, the results of SOMGA on five test problems are compared with the results of MA (GA+ deterministic local search). Results clearly indicates that SOMGA is less expensive and effective to solve these problems.