Cloud computing is an important component of the computer industry emerging technology, its performance of the operation directly affects the efficiency of dealing with large-scale problems.Therefore, search optimization combination intelligence algorithm is the important task of cloud computing research at present,but also has considerable challenge.In the intelligent computing field, ant colony algorithm and genetic algorithm are the most representative of two intelligent optimization algorithms, the former is the simulation of ants foraging process, the latter is a computing model of simulating the natural selection survival of the fittest process in nature. The ant colony algorithm is good at solving discrete optimization problem,reflecting its superior performance, but searching optimization process can easily premature convergence,easily appearance of non-global optimal solution.The concept of genetic algorithm is relatively simple, easy to combine with other intelligent optimization algorithms, but poor local search ability, evolution process can not make good use of information of system feedback, produce a large number of invalid iterations, etc defects.In this paper,the basis of deep research for ant colony algorithm and genetic algorithm, Aiming at the respective exist advantages and disadvantages of the two algorithms, proposed natural selection strategy and immune mechanism to improve ant colony algorithm and genetic algorithm respectively,both improved algorithms, according to exceed manner fusion,obtaining genetic-ant colony hybrid algorithm.In genetic-ant colony hybrid algorithm,first the initial pheromone distribution is generated by the fast random search of genetic algorithm, and then the pheromone is accumulated by the ant colony algorithm based on natural selection to construct the better solution set,resuing the selection,crossover,mutation operation and immune selection of immune genetic algorithm adjusts the parameters optimal combination of ant colony algorithm, finally using the optimal solution retention mechanism of genetic algorithm, in order to get the optimal solution.In this paper,genetic-ant colony hybrid optimization algorithm is deploied to solute the traveling salesman problem of cloud computing platform again. This paper designs the experiment to compare the genetic ant colony hybrid algorithm with the ant colony algorithm and the genetic algorithm in the same cloud platform environment. The simulation test results show that compared with the previous single algorithm, based on genetic-ant colony hybrid optimization algorithm is not only short of execution time, but also has better effect on solving traveling salesman problem. So as to explore the cloud platform to solve the traveling salesman problem provides a solution. |