A Gravitational Search Algorithm with Adaptive Mixed Mutation for Function Optimization
Jingsen Liu · International Journal of Performability Engineering · 2018
In order to improve the optimization accuracy and convergence speed of gravitational search algorithm, the gravitational search algorithm with the mechanism of adaptive mixed random mutation is proposed.A mutation trigger function with adaptive property is introduced into the algorithm, so that every particle has the probability of mutation at any time, and the number of particles that change in the population tends to decrease with the increase of iteration times.At the same time, in the whole optimization process of the algorithm, the uniform mutation and Laplace-normal hybrid mutation cooperate together.The uniform mutation enables the algorithm to find the global optimal area quickly, and then continue deep search with hybrid mutation to improve the optimization performance.The simulation results show that in solving the problem of extremum optimization, the improved algorithm has significantly improved optimization performance, and has high convergence accuracy and faster convergence speed.