Research on Fire Evacuation in University Libraries Based on the Fuzzy Ant Colony Optimization Algorithm

Cities are in a period of rapid urban development and high-rise buildings are constantly emerging. The characteristics of a fire in a high-rise building are the rapid spread of the fire, the difficulty of fighting the fire, and the difficulty of evacuation. Intelligent fire evacuation requires dynamic planning of paths in fire field, it is necessary to automatically adjust the evacuation route in the building according to the real-time information of the fire. In this paper, an improved bidirectional ant colony algorithm is proposed to optimize fire evacuation routes. In order to improve the global search capability of the algorithm, a bidirectional search strategy with the A* algorithm is designed for the ant colony algorithm, the blindness of the algorithm is reduced in the initial search, the pheromone update strategy is improved, and the convergence speed of the algorithm is increased. The fire scene information is combined with the steering penalty coefficient to improve the algorithm’s evaporation coefficient, heuristic function and transition probability, avoid the risk of falling into the local optimum, improve the search efficiency of the algorithm and the smoothness of the path, and effectively avoid areas affected by the fire. The effectiveness of the algorithm is verified by simulation.

The continued increase in human lifespans is accelerating the aging of the population in most countries. The increase in elderly care facilities corresponds to a relative increase in the number of people who are vulnerable in the context of evacuation. For example, an elderly care hospital is a representative facility for vulnerable populations as regards evacuation, as residents in such a hospital cannot evacuate themselves in the event of a fire. It is therefore necessary for such buildings to formulate detailed evacuation plans that consider the walking situations of their residents in the event of a disaster. In this study, the fire dynamics simulator (FDS) was used to calculate the available safety escape time (ASET)—which is the point at which evacuation is impossible from the ignition time of the fire—for inpatients in nursing hospitals. The required safety escape time (RSET), which is the time required to move to a safe place, was calculated by reflecting a patient’s evacuation speed using Pathfinder. In addition, the evacuation route was simulated with three variables—stairs, elevators, and ramps—along with the movement pattern. The simulations of evacuation performance evaluation based on time analysis showed that there were differences in the number of fatalities depending on the choice of evacuation routes and movement patterns for each disabled group. Evacuation using ramps was confirmed to reduce fatalities from at least 48 to up to 60 people compared to evacuation using stairs or elevators. The usage of ramp evacuation in elderly care hospitals has proved to be superior to other evacuation routes in ensuring the safety of vulnerable persons during fire evacuation.

Collective data mining in the ant colony decision tree approach

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

There are many bottlenecks that limit the computing power of the Mobile Web3D and they need to be solved before implementing a public fire evacuation system on this platform. In this study, we focus on three key problems: (1) The scene data for large-scale building information modeling (BIM) are huge, so it is difficult to transmit the data via the Internet and visualize them on the Web; (2) The raw fire dynamic simulator (FDS) smoke diffusion data are also very large, so it is extremely difficult to transmit the data via the Internet and visualize them on the Web; (3) A smart artificial intelligence fire evacuation app for the public should be accurate and real-time. To address these problems, the following solutions are proposed: (1) The large-scale scene model is made lightweight; (2) The amount of dynamic smoke is also made lightweight; (3) The dynamic obstacle maps established from the scene model and smoke data are used for optimal path planning using a heuristic method. We propose a real-time fire evacuation system based on the ant colony optimization (RFES-ACO) algorithm with reused dynamic pheromones. Simulation results show that the public could use Mobile Web3D devices to experience fire evacuation drills in real time smoothly. The real-time fire evacuation system (RFES) is efficient and the evacuation rate is better than those of the other two algorithms, i.e., the leader-follower fire evacuation algorithm and the random fire evacuation algorithm.

Ant-TD: Ant colony optimization plus temporal difference reinforcement learning for multi-label feature selection

The present study aims to conduct a quantitative evaluation of evacuation route safety using the Ant Colony Optimization (ACO) algorithm for risk management in central Tokyo. Firstly, the similarity in safety was focused on while taking into consideration road blockage probability. Then, by classifying roads by means of the hierarchical cluster analysis, the congestion rates of evacuation routes using ACO simulations were estimated. Based on these results, the multiple evacuation routes extracted were visualized on digital maps by means of Geographic Information Systems (GIS), and their safety was evaluated. Furthermore, the selection of safe evacuation routes between evacuation sites for cases when the possibility of large-scale evacuation after an earthquake disaster is high is made possible. As the evaluation method is based on public information, by obtaining the same geographic information as the present study, it is effective in other areas, regardless of whether the information is from the past or future. Therefore, in addition to spatial reproducibility, the evaluation method also has high temporal reproducibility. Because safety evaluations are conducted on evacuation routes based on quantified data, the selected highly safe evacuation routes have been quantitatively evaluated, and thus serve as an effective indicator when selecting evacuation routes.

An investigation of fire evacuation performance in irregular underground commercial building affected by multiple parameters

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.

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.

Multi-objective evacuation routing optimization problem is defined to find out optimal evacuation routes for a group of evacuees according to multiple evacuation objectives. For improving the evacuation efficiency, we abstracted the evacuation zone as a positive-point-charge-potential-field-like model (PPCPF-like model), and we proposed PPCPF-ACO algorithm to solve this problem based on the proposed model. In PPCPF-ACO algorithm, we use non-dominated sorting based roulette wheel routing method (NSRWR) to further improve evacuation efficiency. In Wuhan Sports Center case, we compared PPCPF-ACO with HMERP-ACO (hierarchical multi-objective evacuation routing problem - ant colony optimization) and traditional ACO according to three evacuation objectives, namely, total evacuation time, total evacuation route length and cumulative congestion degree. The experimental results show that PPCPF-ACO has a better performance than HMERP-ACO algorithm and traditional ACO algorithm while solving multi-objective evacuation routing optimization problem.

A hybrid algorithm using ant and bee colony optimization for feature selection and classification (AC-ABC Hybrid)

This paper presents a method of optimized PID parameter self-adapted ant colony algorithm with aberrance gene, based on ant colony algorithm. This method overcomes genetic algorithm’s defects of repeated iteration, slower solving efficiency, ordinary ant colony algorithm’s defects of slow convergence speed, easy to get stagnate, and low ability of full search. For a given system, the results of simulation experiments which compare to the result of Z-N optimization and evolution of genetic algorithm optimization and evolution of ant colony system optimization, it has more excellent performance in finding best solution and convergence, the PID parameters also have optimality, system possesses dynamic controlling and performance. The experiments show that this method has its practical value on controlling other objection and process.

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

Ant colony optimization (ACO) is a popular bionic technology optimization method commonly used for shortest path algorithms. However, this method is associated with various issues, including slow convergence rates and deep local optimal solutions. To address these problems, an improved ant colony optimization algorithm is proposed in this paper, utilizing a new heuristic function and an attractive update strategy to enhance the convergence speed and searchability of the ant colony algorithm. The integration of the artificial potential field function formula correctly guides the overall planning path of the ant colony, allowing it to bypass obstacles using the potential field force and disconnect redundant paths. Simulation experiments show that the algorithm proposed in this paper has obtained a better path and fewer iterations compared to the IAACO improved algorithm. This confirms that the algorithm is effective and reliable in path planning, particularly in complex scenarios and special environments.