Improved Ant Colony Algorithm And Its Application In Function Optimization And Wireless Sensor Networks

Ant colony optimization(ACO)is proposed by imitating the behavior of the ant colony in the process of feeding along the shortest path.This algorithm has the characteristics of strong robustness,easy realization and good at solving NP-hard problem,which causes extensive attention in the field of scientific research and engineering application.This paper focuses on how to improve the ant colony optimization algorithm and its application in the field of function optimization and wireless sensor networks.The main work is as follows:1.According to the characteristics of ant colony optimization,this paper focuses on the development and application of ACO in recent years,and summarizes the main improvement strategy of ant colony optimization.2.Aiming at the function optimization problem,this paper puts forward a function optimization algorithm based on improved ACO,which is mainly reflected in three aspects.The chaotic search is introduced to improve the random search strategy of ant colony by using the excellent characteristics of chaotic variables.The mutation strategy of differential evolution algorithm is introduced to effectively improve the diversity loss of the solution.Finally,referring to the particle swarm algorithm the global best position and its local optimum position are used in ACO.The ant updates the path pheromone according to these two extremum.The simulation results show that the algorithm can overcome the shortcomings of ant colony optimization,such as long search time and easy to fall into local optimum.3.Aiming at the wireless sensor network routing problem,this paper proposes an improved ACO based routing algorithm for wireless sensor networks.The key points of the algorithm are as follows.(a)the adaptive adjustment strategy of pheromone influence factors(b)the introduction of new heuristic function and path evaluation function(c)the search direction is added into the ant colony search process.The above improvements take into account the transmission distance,the transmission direction,the residual energy and the search scale,so that the ant colony can find the best path.The simulation results show that the proposed algorithm can significantly improve the energy consumption and lifetime of the network.