Hybridized gravitational search algorithms with real coded genetic algorithms for integer and mixed integer optimization problems

Abstract

In this paper, the Gravitational Search Algorithm (GSA) is hybridized with real coded Genetic Algorithm to solve Integer and Mixed Integer programming problems. The idea is based on two earlier papers of the authors. In the first paper, the authors proposed a methodology in which the Laplace Crossover and Power Mutation were embedded in Gravitational Search Algorithm and in the second paper, these algorithms were extended for the case of constrained optimization problems. In order to deal with integer variables, a special method is adopted. For dealing with the constraints the Deb’s technique is implemented. The original GSA and three new variants are tested on a set of benchmark problems available in literature. Based on the extensive numerical and graphical analysis of results it is concluded that one of the proposed variants outperform the original GSA and the other proposed variants. © Springer Nature Singapore Pte Ltd. 2017.