A Route Planning Optimization Model Based on Improved Ant Colony Algorithm

Shenyang underground route planning algorithm is the shortest route planning algorithm based on ant colony algorithm (ACA). ACA has a leading position in routing and route planning with a great space for development. In view of the defects of long random search time, premature stagnation in the basic ACA, in order to facilitate the subsequent improvement of route planning in other provinces and cities, we adjusted the ACA adaptively. Route planning algorithm has stronger adaptability and machinability after adjusted. This improved ACA include three planning programming: point to point, point to plane, and Planar programming. It add the speed and accuracy of planning and reduce The calculation amount of the algorithm.

In order to enhance the safe flight performance of UAV and better complete complex tasks, it is extremely necessary to study route planning. Firstly, the feature points of the landform landform are extracted as the flight track points of the UAV, and the track planning problem is transformed into the traveling salesman problem. Secondly, an adaptive pheromone update method is proposed, and an improved ant colony algorithm for optimizing the local pheromone is proposed to avoid the basic ant colony algorithm from falling into the local optimal solution and improve the global search ability. Finally, the proposed algorithm is verified by simulation experiments. The experimental results show that the improved ant colony algorithm is practical and superior, and can effectively solve the problem of route planning and obtain the optimal path of route planning.

Image registration is an important technology in image processing and computer vision, ant colony algorithm search for the optimal registration parameters in image registration has a strong global optimization ability, but the final search to the accuracy of the registration parameters and search for a very long time. in this paper an improved ant colony algorithm is presented to improve the registration accuracy by the original ant colony algorithm and simplex algorithm has a strong local optimization, improved ant colony algorithm heuristic function in order to improve the convergence speed; Finally, the improved ant colony algorithm combined with multi-resolution registration strategy to reduce the time of image registration. The experimental results show that compared with the original ant colony algorithm, an improved ant colony algorithm can effectively improve the image registration accuracy, and can reduce the number of convergence of the algorithm.

Congestion road condition is an important factor that must be considered in urban traffic path planning, while most path planning algorithms only consider the distance factor, which is not suitable for the current complex urban traffic congestion road condition. In order to solve the above problems, this article proposes a dynamic path planning method based on improved ant colony algorithm in congested traffic. The method quantifies the main attributes of urban road length, number of lanes, incoming and outgoing traffic flow, and introduces the road factor used for replacing the distance parameters of particle swarm optimization and ant colony algorithm. In the method, the particle swarm algorithm can effectively optimize the parameters of the ant colony algorithm, and significantly improve the efficiency of ant colony algorithm, such that it is more applicable for dynamic path planning application to greatly reduce the congestion rate of path planning. In addition, this article selects some intersections in the Beijing area to carry out the dynamic path planning experiment based on the improved ant colony algorithm under congested road conditions. The experimental results show that, compared with the ant colony algorithm based on distance parameter, the proposed dynamic path planning method can effectively reduce the average congestion rate ranging from 9.73% to 13.63%.

In view of the disadvantages of the basic ant colony algorithm in solving non-deterministic polynomial problems, such as slow convergence rate and local optimal solution. In this paper, the updating rules of pheromone and the updating strategy of target city in ant colony algorithm are improved, and an improved ant colony algorithm (IACA) is proposed. In addition, it is applied to the shortest traversal path search of travel salesman problem. Taking 33 provincial capitals and berlin52 problems as test objects, this paper compares the search efficiency of the proposed algorithm with that of the basic ant colony algorithm. It can be seen from the simulation results that IACA has a faster convergence rate than the basic ant colony algorithm. IACA is obviously superior to the basic ant colony algorithm in terms of convergence and the quality of the optimal solution. It provides a new choice for solving similar optimization problems.

Application of artificial intelligence algorithms in image processing

Path planning is one of the key technologies for autonomous vehicles. Ant Colony Algorithm can effectively achieve the goal of path planning for autonomous vehicles, but the algorithm has the problems of low search efficiency and local optimal solution in path planning. Therefore, this paper improves the classical ant colony algorithm, using adaptive initial pheromone distribution range build initial pheromone distribution, at the same time improve stimulating factor enhanced heuristic search efficiency, and introduces the rollback strategy self-locking and deadlock problem and adopt preferential set limit to update pheromone strategy, help reduce blind ant search path, and reduce the redundancy of map information. The simulation results show that the improved ant colony algorithm can greatly improve the global search ability and convergence speed, and can help the autonomous vehicle to find the optimal path quickly.

In order to solve the problems of ant colony algorithm in the path planning of mobile robots, such as slow convergence speed and easy to fall into local optimum, an improved algorithm of adaptive ant colony algorithm is proposed in this paper. The algorithm improves the ant’s transition probability function by adding the number of revolutions and the angle of rotation to the heuristic function, and adding the target guide element, which can make the algorithm converge faster. The pheromone update formula is based on the wolfs food competition and distribution principle, which adaptively updates the pheromone volatility that satisfies the Cauchy distribution to avoid the local optimal solution found by the algorithm. Finally, by comparing and analyzing the statistical data results obtained by simulation, it can be concluded that the improved ant colony algorithm in this paper is better.

To improve the reliability of flight conflict resolution for UAV for safety operation in airspace, ant colony algorithm is introduced to improve the safety for UAV collision advoidance. Firstly, the airspace environment of UAV operation is described, and then path planning of UAV is elaborated, finally safety improvement of ant colony algorithm is given for UAV conflict resolution. Simulation vallidates the effectiveness and feasibility.

Track planning for drones has been a common problem. In order to ensure that the UAV can fly long distance in accordance with the predetermined path, it is necessary to set calibration points on the flight path to correct the sensor errors of the UAV. This problem can be abstracted into a path planning problem and solved by ant colony algorithm. Considering the uncertainty of correction failure in these calibration points. This paper presents an improved ant colony algorithm and set up the “Enhanced pheromone volatilization strategy“ to ensure that the UAV could reach the destination with the greatest possibility in this uncertain situation. We verify our algorithm on public data sets**. On data set 1, our algorithm has a 100% probability of reaching the destination, while the traditional ant colony algorithm has only a 61% probability of reaching the destination. On data set 2, our algorithm has a 56% probability of reaching the destination, while the traditional ant colony algorithm cannot find a path can reach the destination. The algorithm code*** in this paper is simple to implement, strong robustness, and can be extended to other scenarios.

Ant colony optimization (ACO) is a new heuristic algorithm developed by simulating ant foraging on the basis of group cooperative learning. TSP and other combinatorial optimization problems have been successfully solved. Like other heuristic search algorithms, ant colony algorithm has the disadvantage of being easily limited to local optimum. Aiming at the vehicle routing problem based on time window, the upper and lower limits of pheromone trajectory intensity are determined by analyzing the ant colony algorithm, and the transmission probability and pheromone updating method are improved to improve the convergence speed and global search ability of the algorithm. Aiming at the vehicle routing problem with time windows in logistics distribution, an improved maximum and minimum ant colony algorithm is proposed to improve the optimization performance. The algorithm can be extended to such related path optimization problems and applied.

Ant colony algorithm (ACA), which has been recently suggested for short-term load forecasting (STLF) by a large number of researchers, inspired by the food-searching behavior of ants, is an evolutionary algorithm and performs well in discrete optimization. In this paper, an improved ant colony clustering (IACC) based on ant colony algorithm was put forward. In IACC, each load data was represented by an ant, making use of the parallel optimization characteristics of ant colony algorithm and the ability of volatile quotient method to adaptively change the amount of information, with improvements have been made by changing the pheromone concentration on every path and enhancing the heuristic function to accelerate the searching process. Experiments and comparisons are done to show that the IACC is an efficient and effective approach, not only IACC increased the STLF accuracy, but also IACC is more exquisite to the similarity of load curve profile.

In this paper, the vehicle routing problem with time windows (VRPTW) was considered, and a mixed integer programming mathematic model of VRPTW was proposed in detail. Meantime, an improved ant colony algorithm (IACA) was proposed to overcome the shortcomings of premature convergence and slow convergence of conventional ant colony algorithm (ACA). The experiment results showed that the IACA can solve VRPTW effectively.

For the problems of slow convergence and easy to fall into local optimum of traditional ant colony algorithm (ACO) for robot path planning, An improved ant colony algorithm (IACO)based on grid map is proposed in this paper. Firstly, in order to improve the positive feedback ability of pheromone in the later period, an adaptive adjustment factor is introduced into the heuristic function. Secondly, the rotation function is introduced into the pheromone state transition probability to balance the relationship between path length and angle. Finally, in order to ensure the quality of participating pheromone update nodes, local optimization strategies are designed based on cross optimization and redundant point deletion, and different quality paths are updated with pheromone difference mechanism to achieve convergence of high-quality nodes. The experimental results show that IACO can make the robot obtain the global optimal path, and it will have good stability and environmental adaptability.

Focusing on the disadvantages of recent researches for roll planning that seldom consider the warm-up part and solution efficiency is low, in this paper, a parallel planning model and an improved ant colony algorithm were proposed to solve the problem. Firstly, the model considering constraints of both warm-up part and staple one was designed; Secondly, the improved ant colony algorithm was proposed where the slab cluster was used to decrease the searching scope and the local optimization to improve the efficiency of pheromone accumulation. Finally, with production data from hot strip mill, we proof the method can get the best results by comparing with other two ant colony algorithms.