Abstract
Gravitational search algorithm (GSA) is the one of the newest developed nature-inspired heuristics for optimization problem. It is designed based on the Newtonian gravity and has shown excellent search abilities when applying it to optimization problems. Nevertheless, GSA still has some disadvantages such as slow convergence speed and local optima trapping problems. To alleviate these inherent drawbacks and enhance the performance of GSA, chaos, which is of ergodicity and stochasticity, is incorporated into GSA by two kinds of methods. One method uses chaos to generate chaotic sequences to substitute random sequences, while another one uses chaos to act as a local search approach. The resultant hybrid algorithms, called chaotic gravitation search algorithms (CGSA1 and CGSA2), thus reasonably have advantages of both GSA and chaos. Eight widely used benchmark numerical optimization problems are chosen from the literature as the test suit. Experimental results demonstrate that both CGSA1 and CGSA2 perform better than GSA and other five chaotic particle swarm optimization.
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