Research of a genetic algorithm ant colony optimization based on cloud model

Genetic algorithm(GA) possesses the ability with a global search quickly and stochastically, However, it can´t make full use of system output information. It has to run large redundancy iteration for the optimal solution when searching to certain scope, So the efficiency to solve precision result s is reduced. Ant colony optimization (ACO) converges on the optimization path through information pheromone accumulation and renewal. It has the ability of parallel processing and global searching. The rate which the ant algorithm seek optimal solution is slow, because there is little pheromone information on the path early. The algorithm is based on the combination of genetic algorithm and ant algorithm. Firstly, it adopt s genetic algorithm to give information pheromone to distribute. Secondly, it makes use of the ant colony optimization to improve the precision of the solution. Thirdly, a cloud model multi-rules generator is provided with optimization of evaporator factor and the total pheromone information from ant colony optimization. So a genetic algorithm ant colony optimization (GA²CO) based on cloud model will be propose in this paper. Finally, we prove the convergence of the algorithm. The simulation results demonstrate that the algorithm has a relatively high optimization efficiency and robustness.