A HYBRID GENETIC ALGORITHM AND GRAVITATIONAL SEARCH ALGORITHM FOR GLOBAL OPTIMIZATION
Aizhu Zhang, Genyun Sun, Zhenjie Wang, Yanjuan Yao · Neural Network World · 2015
The laws of gravity and mass interactions inspire the gravitational search algorithm (GSA), which finds optimal regions of complex search spaces through the interaction of individuals in a population of particles.Although GSA has proven effective in both science and engineering, it is still easy to suffer from premature convergence especially facing complex problems.In this paper, we proposed a new hybrid algorithm by integrating genetic algorithm (GA) and GSA (GA-GSA) to avoid premature convergence and to improve the search ability of GSA.In GA-GSA, crossover and mutation operators are introduced from GA to GSA for jumping out of the local optima.To demonstrate the search ability of the proposed GA-GSA, 23 complex benchmark test functions were employed, including unimodal and multimodal high-dimensional test functions as well as multimodal test functions with fixed dimensions.Wilcoxon signed-rank tests were also utilized to execute statistical analysis of the results obtained by PSO, GSA, and GA-GSA.Experimental results demonstrated that the proposed algorithm is both efficient and effective.