A fast genetic algorithm based on energy–entropy

Based on analysis of the properties of genetic algorithm (GA), a novel improved method is provided. The energy–entropy selection is used in GA annealing selection, which can explore the solution space sufficiently and keep the population diversity. Pseudo-gradient is used to neighborhood search, which can exploit the effective information in the current population and the system information. The results of simulation tests on typical traveling salesman problem (TSP) and a real power network fault restoration show that the improved algorithm is better than GA, heuristic GA and simulated annealing GA in global optimization, which increases the convergence speed significantly.