Commodity Search Algorithm based on Ant Colony Algorithm

In the Crowd Intelligent Trading Network (CIbTN), intelligent individuals connect with each other and make up a distributed network Each intelligent individual store commodity information in local node. They realize the transmission and sharing of information through addressing and routing circle of friends. The purpose of this paper is to design a commodity search algorithm based on ant colony optimization (ACO) to realize searching commodity information resources efficiently, quickly and cheaply in the network This paper designs a commodity search algorithm based on ant colony optimization. It defines the commodity information keyword pheromone and heuristic function calculation and uses the ant colony optimization's transfer probability and pheromone update mechanism to design the rules of commodity information resource search between nodes in the network, so as to reduce the blindness of search. The network environment is simulated based on Peersim simulation software, and the algorithm designed in this paper is programmed by Java language. The paper's algorithm has innovations in pheromone definition, heuristic factor setting, and pheromone update strategy. Its advantages in search success rate, average product matching degree, and number of returned product information resources can efficiently and quickly complete commodity information search.