| Social insects, groups are able to perform complex tasks, their behavior tend to show higher intelligence than individual, this is the concept of "swarm intelligence".One ant's behavior seems disorganized,but ants can always find the shortest path from their nest to the food source.The study found that ants will leave some material call "pheromone" on the path they via, this will be a guidance to the subsequent ants. Inspired by this phenomenon of ants, ant colony algorithm is proposed by Dorigo M, and it is successfully applied to solve the traveling salesman problem. Compared to the traditional algorithm like exhaustive algorithm and greedy algorithm, ant colony algorithm can find the optimal solution of the problem more quickly.A two-stage multi-objective ant colony optimization(TMACO) is proposed to solve logistics Web services composition optimization problems. First, to solve the time increasing rise by candidate services which has been dominated in the raw data, a pre-optimization strategy based on Pareto dominated ideology was proposed; secondly, since the weight of each attribute is difficult to determine, a non-dependent weight pheromone update strategy and inspiration information policy is included in the TMACO; and finally, the basic ant colony optimization is easy to fall into local optimum problem, a lazy ant strategy is proposed to solve this problem. The experimental results show that TMACO algorithm has good performance. Its optimization capability is better than the basic ant colony optimization, the improved ant colony optimization which used the distance of the solution and the ideal solution to update the pheromone, the basic genetic algorithm and improved genetic algorithm which included the introduction of individual domination strength into the environment selection. |
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