An improved ant colony optimization algorithm based on dynamically adjusting ant number

Using the new method and roulette wheel, the traditional Ant Colony Algorithm is improved in the aspect of searching process and pheromone modification. The defects of low searching efficiency and easy to fall into local minimum in traditional Ant Colony Algorithm are remedied. the validity of the improved algorithm has been verified using a testing function.Moreover,a satisfactory optimum solution for a Power Distribution Network Planning has been obtained. And the effective design method for permanent-magnet synchronous Ant colony algorithm is a simulation based on the species evolution to solve complex optimization problem for heuristic. The idea of ant colony algorithm are simulated ants foraging behavior,That is, the use of a large number of ants in the search space in the random search,And use of information has always been to strengthen the search route, and guide other artificial ants search,At the same time, the introduction of the volatile pheromone mechanism(1). Ant colony algorithm proposed by the Italian scholars Dorigo in the twentieth century nineties. The algorithm in the traveling salesman problem (TSP), quadratic assignment problem (QAP) and shop scheduling problem (JSP) solution to achieve good results. Now ant colony algorithm has been optimized in the motor design, network distribution, function optimization and integrated circuit wiring in areas such as applied. Forestry waste from electricity distribution network from the power station involved in each location, size to meet future demand for electricity in rural areas since, at the same time for each subject since the power station capacity, radial network structure, as well as reliability requirements, such as binding. Because of lot of variables and constraints involved, spontaneous power distribution network planning is a very complex combinatorial optimization problem. In this paper, these issues of improved ant colony algorithm and proposes a multi-modal adaptive ant colony pheromone search mechanisms, and their use for the electricity distribution network optimization, and achieved good results. I. THE MATHEMATICAL MODEL OF ACA The ant optimization algorithm is mainly composed of the switching rules and the renewaling information element rule. As an example,We present the ACO algorithm applied to the TSP(Traveling Salesman Problem) for illustrating the principle of ant colony algorithm. there is the assumption N cities, traveling salesman problem are looking for an optimal travel path of the shortest route. The TSP models the situation of a travelling sales man who is required to pass through a number of cities(2). The goal of the travelling sales man is to traverse these cities(visiting each city exactly once)so that the total travelling distance is minimal. Feasible solution of the traveling salesman problem is a non-repeat sequences of all the citis. Assumeing that only m ants Add to the given n cities: dij(i,j=1,2,…,n) where dij is distance between city i with city j. bi(t) where bi(t) is the number of ants is located in city i when it's t. m= ∑ = n

The path of the mobile robot is a multi-objective optimization problem. The traditional ant colony algorithm is introduced into the path planning of the mobile robot. Due to the complexity of the robot path, the large search space and the low efficiency, it is easy to fall into the local optimum and even deadlock and other defeats. In order to enhance the efficiency and accuracy of path planning, a path planning algorithm of mobile robot based on ant colony algorithm is proposed. The path planning of mobile robot is simulated under grid environment. The experimental results show that the improved algorithm can improve the efficiency of path planning for mobile robot, safely avoid obstacles and find all the optimal solutions in the shortest time.

In industrial robot welding heat exchanger plate route planning, there are drawbacks to using the traditional ant colony algorithm (ACA), including poor search efficiency and a propensity to trend toward the local optimum. To address the above issues, first of all, the article introduces the improved pheromone volatilization factor, which is adjustable based on iteration times in the ACA. Secondly, it combines the new perturbation strategy and the cross‐mutation operation, proposes an improved genetic algorithm, and fuses it into the ACA, which increases the diversity of the ACA’s path searching and improves the ability of the local and global search. Finally, the improved ACA mechanism is verified by experiments and compared with six existing path planning algorithms (including three ACA variant algorithms and three mainstream algorithms). The experimental results show that the IACAG algorithm has excellent performance in welding heat exchanger path planning and can achieve lower calculation time and iteration times under the conditions of optimal solution and zero deviation, showing comprehensive advantages in solution quality, stability, and efficiency.

One of the key technologies of logistics robot indoor navigation is path planning. Traditional ant colony algorithm is often used in the path planning of logistics robot, which has the problems of slow convergence and local optimization. We proposed an improved algorithm, that was used in path planning for logistics robots. Introducing a heuristic factor in the path transition probability can dynamically adjust the state transition probability, so that the algorithm avoids stagnation and improves the strategy of pheromone update. The paper carried out simulation experiments on the improved algorithm through the matlab software platform. From the simulation results, we can see that the path length of the improved algorithm is shorter, and the number of iterations is reduced by 23%.

With the continuous deepening of scientific development and technological innovation, many algorithms have been derived from the research on robot path planning, including the current research on ant colony algorithm. It is often accompanied by a series of problems such as slow convergence speed, seeking optimal solutions according to local conditions, insufficient optimization ability, and inability to complement the diversity of ant colony species and convergence speed. In view of the current shortcomings, first, the initial pheromone distribution is uneven, which can avoid the useless search behavior trajectory of the ants during the movement process, thereby speeding up the solution speed and saving time; secondly, based on the advantages of the A* algorithm, the introduction into its algorithm The heuristic search function further improves the heuristic function of the traditional ant colony algorithm and maximizes the speed of the search. Considering the influence factors of energy consumption factors on the experiment in actual situations, a new heuristic function is formed by combining with traditional factors, a simulation environment is built, and the proposed method is verified and compared through a set of examples. The results show that the improved ant colony algorithm is in the optimal path. It has better performance in length and number of iterations, and has practical engineering significance for robot path planning.KeywordsAnt colony algorithmA* algorithmPath planningMATLAB simulation

A global path planning method is proposed based on improved ant colony optimization according to the slow convergence speed in mobile service robot path planning. The distribution of initial pheromone is determined by the critical obstacle influence factor. The influence factor is introduced into the heuristic information to improve the convergence speed of the algorithm at an early stage. A new pheromone update rule is presented using fuzzy control to change the value of pheromone heuristic factor and expectation heuristic factor, adjusting the evaporation rate in stages. The method achieves fast convergence and guarantees global search capability. Finally, the simulation results show that the improved algorithm not only shortens the running time of global path planning, but also has a higher probability of obtaining a global optimal solution. The convergence speed of the algorithm is better than the traditional ant colony algorithm.

Multi-objective path planning in complex three-dimensional environments is a key challenge for improving the application efficiency of unmanned aerial vehicles in rescue, logistics and other fields. This paper proposes a dynamic multi-objective ant colony optimization algorithm to address the problems of traditional ant colony algorithms such as getting stuck in local optimum, slow convergence and difficulty in adapting to dynamic environments. This paper systematically constructs a multi-objective model that integrates path length, height constraints and threat avoidance, and designs a hierarchical pheromone update strategy based on path quality and a hybrid heuristic function for environmental adaptation to balance global exploration and local optimization capabilities. Simulation experiments show that compared with traditional ant colony, particle swarm and other algorithms, this method has significant improvements in path length, smoothness and computational efficiency, especially showing better obstacle avoidance success rate and real-time re-planning ability in high dynamic scenarios. This method effectively enhances the intelligent decision-making level of unmanned aerial vehicles in complex environments and has practical application value for improving the reliability of emergency response and logistics distribution.

Path planning aims to enable autonomous robots to navigate safely and efficiently from a starting point to a target point in challenging and dynamic environments. Path planning in robotics is highly significant and still an ongoing subject of research. The increasing use of robots in various applications such as industrial automation, service robotics, and autonomous vehicles has brought forth the need for reliable and efficient path planning algorithms. The inherent capability of Voronoi diagrams to partition space based on proximity makes them an effective framework for research in path planning. Ant colony optimization, a bio-inspired optimization technique, is based on the foraging behavior of ants and is commonly employed to address the traveling salesman problem and various other combinatorial optimization problems. A hybrid method was adopted in this study by combining a Voronoi diagram and an ant colony algorithm. To create paths for the robot where it can stay as far away from obstacles as possible, a Voronoi diagram was utilized. Additionally, to find the shortest path from the starting point to the destination among these paths, ant colony optimization was employed. The main contribution of the study lies in the combination of the Voronoi diagram for obstacle avoidance and ant colony optimization for finding the optimal path. The combination of these techniques makes an effective contribution to robotic path planning by focusing on ensuring safety by avoiding obstacles while optimizing the shortest path. Experimental studies show that the hybrid method produces successful results for the desired purpose.

In order to solve the problem that mobile robot is trapped in local convergence and cannot achieve path optimization in ant colony algorithm path planning, an improved variable step size ant colony algorithm is proposed to achieve path optimization with less convergence iterations. According to the relevant characteristics of the application of ant colony algorithm in path planning, the pheromone allocation is optimized, the influence of local pheromone content on the algorithm is reduced, the ant colony is prevented from falling into local optimization when searching the path, and the weight factor is added to the transfer probability formula. improve the probability of the mobile robot moving towards the end point, effectively reduce the number of convergence iterations of the ant colony, and change the mobile robot’s mobile step size. So that it can move freely without collision within 360°, and the path length can be effectively shortened. The simulation results show that in a simple environment, the convergence iteration times and optimal path length of the improved variable step size ant colony algorithm are 2 and 28.042 m, respectively, and the convergence iteration times and optimal path length of the traditional ant colony algorithm are 25 and 29.213 m, respectively. In the complex environment, the convergence iteration times and optimal path length of the improved variable step size ant colony algorithm are 2 and 43.9602m respectively, and the convergence iteration times and optimal path length of the improved potential field ant colony algorithm are 16 and 45.1127m, respectively. Simulation results verify the effectiveness and superiority of the improved variable step size ant colony algorithm.

The traditional ant colony algorithm based on raster algorithm has slow searching speed and convergence speed in the problem of robot path planning, and it is easy to fall into the problem of local optimal. In order to solve this problem, an improved ant colony algorithm based on Wolf distribution rules and reconnaissance ant strategy is proposed, which can adjust the pheromone coefficient adaptively and obtain a more suitable proportion of pheromone allocation. Experiments show that this algorithm is superior to the traditional ant colony algorithm in terms of optimal path searching speed, quality and convergence speed in simple and complex environments.

The traditional ant colony algorithm is inefficient to search, easy to fall into algorithm stagnation and local optimization problems in UAV path planning. To ensure that the UAV can avoid obstacles and fly safely, the ant colony algorithm is improved and optimized. First, the target planning area of the UAV is modeled in three dimensions using raster method. Secondly, the update rules of pheromones are improved, and the weight factors of pheromones and heuristics are adjusted. An unmanned aerial path planning algorithm based on improved ant colony algorithm is proposed to plan a safe and optimal path for the unmanned aerial vehicle. Finally, the simulation results show that the improved algorithm has a better flight path than the traditional algorithm.

Based on the mobile robot path planning problem, on the basis of the improved grid method, this paper proposes an improved ant colony algorithm, the particle swarm optimization algorithm can be incorporated into the ant colony algorithm. Firstly, using the particle swarm optimization algorithm to search for global path roughly. At the same time of search for dynamic pheromone intelligent distribution, to get ant colony algorithm's initial pheromone distribution and according to the pheromone content to eliminate some poor path. Secondly, using ant colony algorithm for the optimal path, it was gotten by particle swarm optimization for the secondary search. Finally, get the optimal solution of path search. The simulation results show that, the fusion algorithm does better than the ant colony algorithm in terms of path optimization.

When the ant colony algorithm is used to solve the global path planning of the unmanned boat, it is prone to problems such as local optimization and too slow convergence speed. This paper combines the problems encountered in the actual application of the unmanned boat and proposes an improved ant colony algorithm. Based on the traditional ant colony algorithm, the state transition rules are optimized and the pheromone volatilization coefficient is dynamically adjusted. The simulation results show that the improved ant colony algorithm compared with the traditional ant colony algorithm, the shortest path and path finding time of the USV to the destination are optimized, and the smoothness of the USV's search path is also improved.

With the continuous development of mobile robot technology, its application fields are becoming increasingly widespread, and path planning is one of the most important topics in the field of mobile robot research. This paper focused on the study of the path planning problem for mobile robots in a complex environment based on the ant colony optimization (ACO) algorithm. In order to solve the problems of local optimum, susceptibility to deadlocks, and low search efficiency in the traditional ACO algorithm, a novel parallel ACO (PACO) algorithm was proposed. The algorithm constructed a rank-based pheromone updating method to balance exploration space and convergence speed and introduced a hybrid strategy of continuing to work and killing directly to address the problem of deadlocks. Furthermore, in order to efficiently realize the path planning in complex environments, the algorithm first found a better location for decomposing the original problem into two subproblems and then solved them using a parallel programming method-single program multiple data (SPMD)-in MATLAB. In different grid map environments, simulation experiments were carried out. The experimental results showed that on grid maps with scales of 20 $ \times $ 20, 30 $ \times $ 30, and 40 $ \times $ 40 compared to nonparallel ACO algorithms, the proposed PACO algorithm had less loss of solution accuracy but reduced the average total time by 50.71, 46.83 and 46.03%, respectively, demonstrating good solution performance.

To determine the optimal trajectory of mobile robots in a complex environment and to enhance the effectiveness of search efficiency and path optimization, an improved ant colony (ACO) algorithm is proposed. A fusion algorithm of improved particle swarm optimization (PSO) and ACO algorithm is proposed to solve for optimal paths. Firstly, we improve the PSO and adaptively adjust the inertia weight to facilitate the particle search. Besides, we disturb the particles with the chaotic variables to increase the convergence speed. Secondly, the pheromone distribution on the path is adjusted based on the optimal solution of PSO to solve the initial pheromone insufficiency issue in the ACO algorithm. Then, to optimize the update strategy, we adaptively adjust the pheromone intensity values. We also provide a wide range of control schemes for updating the parameters for balancing the search capability and convergence. The results in the simulation environment show that our algorithm is more effective than other improved algorithms. The improved fusion algorithm converges faster and finds shorter optimal path. The method can improve the comprehensive performance and achieve the rapid path planning for mobile robots.