Adding new lines on the basis of the existing public transport network is an important way to improve public transport operation networks and the quality of urban public transport service. Aiming at the problem that existing routes are rarely considered in the previous research on public transportation network planning, a public transportation network optimization method based on an ant colony optimization (ACO) algorithm coupled with the existing routes is proposed. First, the actual road network and existing bus lines were abstracted with a graph data structure, and the integration with origin–destination passenger flow data was completed. Second, according to the ACO algorithm, combined with the existing line structure constraints and ant transfer rules at adjacent nodes, new bus-line planning was realized. Finally, according to the change of direct passenger flow in the entire network, the optimal bus-line network optimization scheme was determined. In the process of node transfer calculation, the algorithm adopts the Softmax strategy to realize path diversity and increase the path search range, while avoiding premature convergence and falling into local optimization. Moreover, the elite ant strategy increases the pheromone release on the current optimal path and accelerates the convergence of the algorithm. Based on existing road network and bus lines, the algorithm carries out new line planning, which increases the rationality and practical feasibility of the new bus-line structure.
This paper addresses the optimal path selection problem for economic corridors, which is a significant issue in the field of geo-economics. The paper has utilized the spatiotemporal characteristics of geo-economics and identified the development needs in this field to propose an improved ant colony optimization (ACO) strategy. The proposed strategy focuses on enhancing the heuristic function, functional area setting, and pheromone updating strategy. The heuristic factors and transfer probabilities have been improved to couple the path nature, which were based on an analysis of the factors that influence geo-economics. This improvement enhances the applicability of the ACO to path selection problems in macrospace. Additionally, the paper has differentiated the priority of path nodes by setting functional areas, which adds directionality to path selection. The improved ACO has been applied to analyze the optimal path in macroscopic economic space. The experimental validation was conducted in the Indo-Pacific region and economic corridors in China within this region, and corresponding potential geo-economic hubs were analyzed. The experimental results were validated using the Mann−Whitney U test and an evaluation method based on path effectiveness. The feasibility and objectivity of the proposed method were verified. This research provides a valuable exploration of the problem of path selection in macrospace and time and provides decision aid for the construction and adjustment development of a country’s geo-economic relations in a given region. It is a technical reference for establishing sustainable development strategies and national and regional economic planning. Overall, this work contributes significantly to the field of geo-economics and demonstrates the effectiveness of the proposed method through experimental validation.
Agent-based combat simulation is an important research method in the field of military science and system simulation. Behaviour decision model plays the key role in the design of combat simulation agents. The behaviour tree (BT) designed by nonplayer characters (NPCs) in the game provides an efficient and concise method for the construction of combat simulation agents and has been widely used. Because the rationality of BT construction directly affects the rationality of agent decision logic, designing a reasonable BT has become a crucial step. The design of the operational agent BT not only relies on rich tactical experience but also needs to repeatedly adjust and optimize the BT according to the operational deduction and simulation results. To avoid unreasonable BT design caused by lack of experience and eliminate the process of repeated debugging, a modelling method of a combat simulation agent that combines reinforcement learning and the BT method was proposed. This method not only makes the design process of BT more automatic but also simplifies the experience requirements of the combat simulation agent designers. Experiments show that RL-BT effectively integrates the reinforcement learning method and can endow combat simulation agents with battlefield learning ability while making independent decisions. The agent based on RL-BT for decision modelling can continuously adjust and optimize the decision process through experience accumulation, and its performance in combat simulation is significantly better than that of the agent using the original BT.
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