Research on Route Planning based on improved Ant Colony Algorithm

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.

Ant colony optimization (ACO) algorithm is a metaheuristic inspired by the behavior of real ants in their search for the shortest path to food sources. The ACO algorithm takes on these characteristics of robust, positive feedback distributed computing, easy fusing with other algorithms. But the basic ACO algorithm has some deficiencies of premature and stagnation phenomenon in the evolution process, and is easily trapped into local optimal solution. And it is difficult to explore other solutions in the neighbor space. So a improved ACO(DPSEMACO) algorithm based on dual population strategy, bi-directional dynamic adjust evaporation factor strategy of the pheromone and parallel strategy is proposed to solve the traveling salesman problem(TSP). In the DPSEMACO algorithm, the ants are divided into the two subpopulations by borrowing the mutual cooperation mechanism of biological community, which evolve separately and exchange information timely. The bi-directional dynamic adjusting evaporation factor strategy of the pheromone is used to change the corresponding path pheromone of different subpopulations in order to avoid to trap into a local optimum. The parallel strategy can avoid falling into a local optimum. And the DPSEMACO algorithm can expand the search space and improve the overall searching performance by repeated changing the pheromone of the each subpopulation and adaptive adjusting evaporation factor. Finally, in order to prove the optimization performance of the proposed DPSEMACO algorithm, some classic TSP instances are selected from the TSPLIB in this paper. And some existing methods are selected to compare the optimization performance with the proposed DPSEMACO algorithm. The experimental results demonstrate that the proposed DPSEMACO algorithm is feasible and effective in solving TSP, and takes on a good global searching ability and high convergence speed.

The researches and applications on ant colony algorithm have made great progress in recent years. A number of results have proved the validity of the algorithm and its advantages in some fields. However, its basic shortcomings, which are long searching time and easily jumping into local optimal solution, have not been completely solved. This paper analyzes the reasons of stagnation and then introduces a new solution for avoiding stagnation, which includes the direct exchange of pheromone of some edges and dynamically setting evaporation rate for each edge. The simulation results demonstrate that the above approach is reasonable and efficient.

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.

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.

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.

According to the requirements of efficient image segmentation for the manipulator self-recognition target, a method of image segmentation based on improved ant colony algorithm is proposed in the paper. In order to avoid segmentation errors by local optimal solution and the stagnation of convergence, ant colony algorithm combined with immune algorithm are taken to traversing the whole image, which uses pheromone as standard. Further, immunization selection through vaccination optimizes the heuristic information, then it improves the efficiency of ergodic process, and shortens the time of segmentation effectively. Simulation and experimental of image segmentation result shows that this algorithm can get better effect than generic ant colony algorithm, at the same condition, segmentation time is shortened by 6.8%.

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%.

Route planning is an important part of combat planning and it is also one of the hotspots and difficult problems of military operation research. According to the analysis of general route planning optimization problem, a method of route planning optimization is discussed based on improved ACA (ant colony algorithm). A series of schemes are proposed to improve the global convergence performance of the ACA. Simulation results verify that the method can gain satisfying route plan and the method can provide some reference value to related researchers.

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.

Because the traditional ant colony algorithm has the disadvantages of slow convergence speed and easy to fall into the local optimal solution in complex environments, so the paper improves the traditional ant colony algorithm. The improved method sets the initial pheromone concentration based on the position information of the current feasible target point and the end point, to improve the early convergence speed of the algorithm. And by optimizing the pheromone update method to speed up the global convergence speed of the algorithm and reduce the generation of local optimal solutions. Increase the convergence speed of the algorithm by increasing the transfer factor and optimizing the transfer mode. Combined with the motion characteristics of AUV, the path is re-planned to further optimize the path and improve the feasibility of the algorithm. Simulation results show that compared with the traditional ant colony algorithm, the improved algorithm conforms to the motion characteristics of AUV, has a faster convergence speed, and reduces the generation of local optimal solutions.

A research about the path planning problem has been popular topic nowadays and some effective algorithms have been developed to solve this kind of problem. However, the existing algorithms to solve the path problem can only find a single optimal path, cannot satisfactorily find multiple groups of optimal solutions at the same time, and it is very necessary to propose as many solutions as possible. So this paper carries out a research on the Multi-modal Multi-Objective Path Planning (MMOPP), the objective is to find all sets of Pareto optimal path solutions from the start point to the end point in a grid map. This paper proposes a multi-modal multi-objective ant colony path planning optimization algorithm based on matrix preprocessing technology and Dijkstra algorithm (MD-ACO). Firstly, a new method of storing maps that reduces the size of the map and reduces the size of the decision space has been proposed in this paper. Secondly, using the characteristics of the Dijkstra algorithm that can quickly find the optimal path, generate an initial feasible solution about the problem, and improve the problem that the initial pheromone of ant colony algorithm is insufficient and searching for solutions is slow. Thirdly, a reasonable threshold is set for the pheromone to avoid algorithm getting stuck in local optimal solution. Finally, the algorithm is tested on the MMOPP test sets to evaluate the performance of the algorithm, and the results show that MD-ACO algorithm can solve MMOPP and get the optimal solution set.

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 algorithms analogize the social behaviour of ant colonies, they are a class of meta-heuristics which are inspired from the behavior of real ants. It was applied successfully to the well-known traveling salesman problem and other hard combinational optimization problems. In order to apply it to the classical 0/1 knapsack problem, this paper compares the difference between the traveling salesman problem and the 0/1 knapsack problem and adapts the ant colony optimization (ACO) model to meet researches' purpose. At the same time, the parameters in ACO model are modified accordingly. The experiments based on improved ant colony algorithms show the robustness and the potential power of this kind of meta-heuristic algorithm.

Vehicle routing problem is a typical NP hard problem in combinatorial optimization. This paper proposes an improved algorithm, namely Immune Ant Colony Algorithm to solve the problem of vehicle routing problem. The algorithm is based on the intelligent optimization idea- Ant Colony Algorithm and Artificial Immune Algorithm. In this paper, in order to solve the problem of vehicle routing problem effectively four aspects are modified to improve the basic ant colony algorithm. And the improved Artificial Immune Algorithm is used to solve the problem of Ant Colony Algorithm to be apt to plunge into as the local optimum solution. On the basis of these improvements, the framework of the new algorithm—Immune Ant Colony Algorithm is given. Finally, it was proved that Immune Colony Algorithm can quicken the convergence rate to obtain optimal solution and decrease computing time. In addition, the computing simulation examples show its validity.