A self-organizing migrating genetic algorithm for constrained optimization

Abstract

In this paper, a self-organizing migrating genetic algorithm for constrained optimization, called C-SOMGA is presented. This algorithm is based on the features of genetic algorithm (GA) and self-organizing migrating algorithm (SOMA). The aim of this work is to use a penalty free constraint handling selection with our earlier developed algorithm SOMGA (self-organizing migrating genetic algorithm) for unconstrained optimization. C-SOMGA is not only easy to implement but can also provide feasible and better solutions in less number of function evaluations. To evaluate the robustness of the proposed algorithm, its performance is reported on a set of ten constrained test problems taken from literature. To validate our claims, it is compared with C-GA (constrained GA), C-SOMA (constrained SOMA) and previously quoted results for these problems.