Network Optimization of RPTB Express based on Ant Colony Algorithm

Inverse transient radiation analysis in one-dimensional participating slab using improved Ant Colony Optimization algorithms

Based on the associated network of passenger and freight transport in the New Western Land-Sea Corridor, the structure and characteristics of passenger and freight transport network in different years are analyzed using passenger volume and freight volume, and the resilience of the network is analyzed using the disturbance model. Finally, the coordinated development of passenger transport and logistics is discussed by applying a coupling coordination model. The results show that, from 2000 to 2018, the New Western Land-Sea Corridor has realized the evolution of the spatial pattern of passenger transport network from “two center and numerous branches” to “three centers and three auxiliaries”. Moreover, the evolution of the spatial pattern of the freight transport network changed from “two centers and one auxiliary” to “one center and multiple auxiliaries”. In terms of network tenacity, the disturbance strategy based on node degree value has the strongest damage to the performance of passenger and freight transport networks. The coupling coordination value is gradually rising from a low level and finally develops to an ordered trend. Therefore, it is proposed that passenger transport development could be supported from the “growth pole” and “zone” development modes, while the logistics development would adopt the “axis” development pattern and focus on the protection of the vital city nodes in the corresponding network. In this way, it can realize the effective connection of multiple paths, improve the tenacity of network and boost hub economy, consummate the regional coordination mechanism, and finally promote the advancement of the New Western Land-Sea Corridor.

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.

In order to solve the optimization of new energy bus line network, a new energy bus line network based on ant colony algorithm and low-carbon concept is proposed. Firstly, the model of public transport network is established combined with OD matrix, and the improved ant colony algorithm is used to iteratively optimize the initial suboptimal route set, optimize the objective function, and finally obtain the optimal public transport route set. Secondly, the improved ant colony algorithm based on simulated annealing solves the design of public transport network, and the optimization scheme greatly reduces the number of passenger transfers and the total travel time of passengers. Finally, simulated annealing algorithm, basic ant colony algorithm, and simulated annealing improved ant colony algorithm are used to optimize the objective function. It is proved that the improved ant colony algorithm of simulated annealing can find the optimal solution of the three algorithms when solving the problem of public transport network design, which is better than the solution of basic ant colony and simulated annealing algorithm. The solution efficiency is 10.6 times that of simulated annealing algorithm and 3.5 times that of basic ant colony algorithm.

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.

Distribution network reconfiguration is a very complex and large-scale combinatorial optimization problem. In network reconfiguration, whether an effective solution can be obtained is a key issue. Aiming at the problems in network reconstruction by traditional algorithm, such as long time required, more times of power flow calculation and high network loss, a network optimization design algorithm based on improved ant colony algorithm for high voltage power distribution network is proposed. After analyzing the operating characteristics of the high voltage power distribution network, the network topology of the high voltage power distribution network is described by constructing a hierarchical variable-structure distribution network model. A mathematical model of distribution network reconstruction considering the opportunity constraint with the minimum network loss as the objective function is established. The power flow distribution is calculated by using the pre-push back-generation method combined with the hierarchical structure of the distribution network. The maximum and minimum ant colony algorithm is introduced to improve the pheromone updating method of the traditional ant colony algorithm, and the search range is expanded, so that the algorithm can jump out of the local optimization trap to realize the accurate solution of the power distribution network reconstruction model. The experimental results show that compared with the current network reconstruction algorithm, the proposed algorithm requires less time for convergence, less power flow calculation, and lower network loss.

Designing well-functioning and fair transport networks is not a trivial task, given the large space of solutions and constraints one must satisfy. Moreover, different spatial segregation sources can render some transportation network interventions unfair to specific groups. It is thereby crucial to optimize the transportation system while mitigating the disproportional benefits it can lead to. In this paper, we explore the trade-off between efficiency and fairness in the Transport Network Design Problem (TNDP), via the use of Deep Reinforcement Learning (Deep RL). We formulate different fairness definitions as reward functions - inspired by Equal Sharing of Benefits, Narrowing the Gap, and Rawl's justice theory. We apply our method to Amsterdam (The Netherlands) and Xi'an (China) and show that vanilla Deep RL can lead to biased outcomes. By considering different fair rewards, however, we can shed light on possible compromises between fairness and efficiency in the TNDP.

Learning the structure of Bayesian networks (BNs) has received increasing attention. Based on score+search methods, many heuristic algorithms have been introduced to search the optimal network with the maximum score metric. To overcome the drawback of ant colony optimization (ACO) in solving the BN structure learning, this paper introduces a new algorithm for learning BNs based on the hybrid ACO and differential evolution (DE). In the proposed hybrid algorithm, the entire ant colony is divided into different groups, among which DE operators are adopted to lead the evolutionary process. Differ from the widely used methodologies that combine ACO with constraint-based techniques, our work mainly focuses on improving the inherent search capability of ACO. Experimental results show that the hybrid algorithm outperforms the basic ACO in learning BN structure in terms of convergence and accuracy.

Reactive power optimization of distribution network is an effective means to ensure the safe and economic operation of distribution network, and it is an important measure to improve the voltage quality of distribution network. The reactive power optimization of distribution network can improve the voltage distribution of distribution network system, improve the voltage quality of users, reduce the power loss of power transmission, reduce the power cost, and improve the power transmission and distribution capacity and stable operation level of distribution network. This paper studies the optimality of ant colony algorithm and artificial bee colony algorithm for distribution network reactive power optimization. Taking the minimum system active power loss as the objective function, the mathematical model of distribution network reactive power optimization is established. Based on IEEE33 node system, the simulation experiments of distribution network reactive power optimization based on ant colony algorithm and artificial bee colony algorithm are carried out respectively, and the experimental results are compared and analyzed. The experimental results show that both algorithms can improve the system node voltage, the artificial bee colony algorithm has better performance in reducing the efficiency of system active power loss, and the ant colony algorithm runs faster.

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

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.

To improve the stability of network data transmission and reduce the network consumption, the routing optimisation of computer network using ant colony optimisation (ACO) algorithm is studied in this article. A mathematical model of routing optimisation is introduced and analysed to explain possible approaches to improve the performance of traditional ACO algorithms. Our scheme introduces two factors: time delay and bandwidth to the heuristic function, reflecting the comprehensive information of each link to promote the comprehensiveness of ants in finding optimum solution. Then, the advanced strategy of state transition rule and pheromone updating rules are also proposed to raise the convergence speed of ACO. In simulations, we test relative factors including success rate, speed, and transmission delay of path finding. In the weighted connected graph with 20 points, the optimal path can be achieved using fewer iterations. In three standard network test data sets of MATLAB, the success rate gets 93.1%, 92.4% and 90.5% respectively. By the comparison of transmission delay and processing time, it also shows performance compared to classic ACO algorithms.

Scheduling for a job shop production system is an integral aspect of production management. Scheduling operations must minimize stock, waste, and idle time and ensure on-time delivery of goods in a time window problem. In this study, due date is considered as an interval instead of a time point. This study addresses scheduling with a time window of job shop scheduling problem (JSP) and yields a solution that is close to the time window. The total penalty due to earliness and tardiness is minimized. As the problem is NP-hard, a mathematical model of the JSP with a time window is initially constructed, and data are then simulated. Solutions are obtained by ant colony optimization (ACO) programs written in C-language and are compared with the best solution obtained using LINGO 7.0 to determine the efficiency and robustness. Test results indicate that ACO is extremely efficient. Solution time using ACO is less than that using LINGO. Hence, ACO is both effective and efficient, which are two qualities stressed in business management.

Emergency material scheduling optimization model and algorithms: A review

Currently, Pingyang County, Cangnan County, and Longgang City are promoting the integrated development of the three regions.Against this background, the transportation system, especially the highway passenger transport network, serves as an infrastructure carrier that connects different regions and facilitates the flow of factors.Its structural characteristics and operational efficiency directly affect the realization degree and development quality of regional integration.Taking Longgang City, Pingyang County, and Cangnan County in Zhejiang Province as the research area, this study utilizes highway passenger transport schedule data from 2020 and 2025, and applies social network analysis method, including network density, node centrality, and node connection strength, to investigate the spatiotemporal evolution of the structural characteristics of the highway passenger transport network among these three regions.The findings are as follows: 1) In terms of passenger transport network density, the density of the highway passenger transport network in 2025 was 22.23, which was higher than 16.67 in 2020.The passenger transport connection strength of the highway passenger transport network in 2025 was enhanced, indicating that the connections between nodes have become closer and the overall level of transportation integration of the highway passenger transport network has been improved.2) From the perspective of passenger transport connection strength between nodes, the number of node pairs with strong passenger transport connections in 2025 was greater than that in 2020, while the number of node pairs with weak passenger transport connections was How to cite this paper: