Improvement Of Intelligent Ant Colony Algorithm And Its Application

During the 1990s, the Italian scholar M.Dorigo, V.Maniezzo proposed a heuristic evolutionary bionic algorithm based on population by simulation the collective behavior of ant routing, and found the whole ant colony make use of pheromone to collaborate with each other to form a positive feedback that each ant follow the shortest path.In last two decades, Ant colony algorithm got extensive application in combinatorial optimization, function optimization, system identification, network route, robot path planning, data mining and cabling design of large scale integrated circuit. But many scholars realized the limitation of traditional ant colony algorithm to improve. The improvement of ant colony algorithm have two handles, one aspect is the improvement of itself, such as improvement mode of pheromone release, probability of selection method, and the other is fusion improvement on ant colony algorithm and other intelligent optimization algorithms.This paper is a detailed study of the principle, analysis, fusion improvement and the applied of ant colony algorithm.Firstly, we propose two improvement of binary ant colony algorithm. One improve- ment is binary discrete with solution space variable. We determine binary digit conversion n according to the size range, then we will convert it into the solving TSP problems on n cities. Each population interval are computed by forward and backward in every interval, which was completely different from the method of solution space is divided into many subdomains in traditional ant colony algorithm and binary ant colony algorithm. The tests show that improvement algorithm has make a lot of progress with traditional ant colony algorithm and binary ant colony algorithm in convergence rate and iterations of optimization. The other improvement is apply weighted strategy to renewal of pheromone. The test results show that improve the global convergence performance algorithm greatly, heighten the accuracy of the solution.Secondly, we propose a novel estimation of distribution algorithm by fusion improvement on ant colony algorithm and PBIL estimation of distribution algorithm. The algorithm introduce probability distribution model of PBIL algorithm to guide route choice, which greatly improve the faults that positive feedback mechanism of pheromone can easily fall into local optimum.Thirdly, we apply ant colony algorithm to the problem of web services section and optimization form Qos property optimal. Web services section is a way of solving complex problems and widely used. In last, we obtain the attribute values Qos for optimal service composition. This way has a very practical significance.Fourthly, wo apply ant colony algorithm to line planning of Baiyang Lake, which come up with the new idea for the planning staff in Master Plan of Baiyang Lake Tourism Area Development and improve the efficiency greatly.