Improved chaotic gravitational search algorithms for global optimization

Abstract

Gravitational search algorithm (GSA) has gained increasing attention in dealing with complex optimization problems. Nevertheless it still has some drawbacks, such as slow convergence and the tendency to become trapped in local minima. Chaos generated by the logistic map, with the properties of ergodicity and stochasticity, has been used to combine with GSA to enhance its searching performance. In this work, other four different chaotic maps are utilized to further improve the searching capacity of the hybrid chaotic gravitational search algorithm (CGSA), and six widely used benchmark optimization instances are chosen from the literature as the test suit. Simulation results indicate that all five chaotic maps can improve the performance of the original GSA in terms of the solution quality and convergence speed. Moreover, the four newly incorporated chaotic maps exhibit better influence on improving the performance of GSA than the logistic map, suggesting that the hybrid searching dynamics of CGSA is significantly effected by the distribution characteristics of chaotic maps.