Improved Colony Algorithm Research Articles

Application Analysis of Physical Fitness Test Performance Management Based on Improved Ant Colony Algorithm

Objectives: With the continuous development of quality education in China, higher education pays more and more attention to the development of physical education. However, in the process of China’s development, there is a lack of corresponding evaluation system for physical fitness test results. Methods: Therefore, this paper proposes the analysis and research application of fitness test performance management based on ant colony algorithm. Firstly, it expounds the development status of physical education performance management. Results: Then, aiming at the corresponding shortcomings, this paper puts forward the application analysis of the fitness test performance management platform based on the mixed mode of cloud theory, and carries out a series of comprehensive tests on the system. Conclusion: According to the performance test of all aspects of the database, the test results show that the system is feasible.

Application and Analysis of Improved Ant Colony Algorithm in Physical Fitness Test

Objectives: The ant colony algorithm is an algorithm that the Italian scholar sums up by studying the living habits of the creatures, and algorithm model established by inspiration according to ants finding things in the shortest path. Methods: In this paper, through the establishment of algorithm model based on an ant colony algorithm, all kinds of problems in physical fitness test were solved, which makes the physical test more efficient and convenient. Results: Through the testing and use of the algorithm model, it is found that the ant colony algorithm established in this paper can meet the requirements, can plan the information of physical fitness test as a whole, Conclusion: and help to deal with the problems of physical tests, so it is a good performance algorithm.

The Determining the Flight Routes of Unmanned Aerial Vehicles Groups Based on Improved Ant Colony Algorithms

It is proposed to use an improved ant colony algorithm to determine the flight paths of unmanned aerial vehicles groups to the objects of intrest. A study was conducted on the application of the MAX-MIN Ant System to simultaneously determine the flight paths of several groups of unmanned aerial vehicles from different airfields to different objects of interest. Obstacles in the path of the unmanned aerial vehicles flight are also taken into account. As an example, the problem of a unmanned aerial vehicles breakthrough of an air defense system is considered. The number of unmanned aerial vehicles required to destroy the object of impact with a given probability is taken into account. The efficiency of the algorithm in the conditions of non - stationary environment is also investigated. Keywords— unmanned aerial vehicle, group, ant colony algorithm, route, flight, optimization

Smooth Path Planning of Mobile Robot Based on Improved Ant Colony Algorithm

Aiming at the problems of slow convergence, easy to fall into local optimum, and poor smoothness of traditional ant colony algorithm in mobile robot path planning, an improved ant colony algorithm based on path smoothing factor was proposed. Firstly, the environment map was constructed based on the grid method, and each grid was marked to make the ant colony move from the initial grid to the target grid for path search. Then, the heuristic information is improved by referring to the direction information of the starting point and the end point and combining with the turning angle. By improving the heuristic information, the direction of the search is increased and the turning angle of the robot is reduced. Finally, the pheromone updating rules were improved, the smoothness of the two-dimensional path was considered, the turning times of the robot were reduced, and a new path evaluation function was introduced to enhance the pheromone differentiation of the effective path. At the same time, the Max-Min Ant System (MMAS) algorithm was used to limit the pheromone concentration to avoid being trapped in the local optimum path. The simulation results show that the improved ant colony algorithm can search the optimal path length and plan a smoother and safer path with fast convergence speed, which effectively solves the global path planning problem of mobile robot.

Research on Path Planning for Mobile Robot Based on Improved Ant Colony Algorithm

Path planning of mobile robots is an important research content in mobile robot design. In view of the characteristics of classic ant colony algorithm, such as long distance, low efficiency and easy to fail into dead zone, a new pheromone updating algorithm and global/local pheromone distribution models are proposed. The grid method is used to construct two-dimensional plane space with obstacles and the simulation experiment is carried out. The simulation results show that under the improved algorithm, the robot path planning efficiency is high and the path distance is short, which verifies the validity of the model.

Research on Robot Path Planning Based on Improved Ant Colony Algorithm under Computer Background

With the development of artificial intelligence technology, the intelligent requirement of robot path planning (hereinafter referred to as PP) is higher and higher. Therefore, people gradually study the optimization of PP algorithm more and more mature. At present, the traditional robot has been unable to meet the requirements of task efficiency, which requires the robot to have the function of autonomous and safe path finding. Therefore, we must optimize the traditional PP algorithm, which can find a time-consuming and safe path in many feasible paths. Therefore, PP is the basis of the exploration task. Traditional ant colony algorithm (hereinafter referred to as ACA) has many problems, such as slow convergence speed, low efficiency, easy local optimization and other defects, which have many restrictions on robot PP. Therefore, this paper proposes an Improved ACA (hereinafter referred to as IACA), which can optimize the path selection by improving pheromone. Firstly, this paper analyzes the TMR (hereinafter referred to as TMR) PP and the principle of IACA. Then, the PP of TMR is simulated and tested, which reduces the cycle times. Therefore, the IACA can effectively improve the search efficiency of the optimal path, which improves the overall performance of PP.

Retraction: Research on Robot Path Planning Based on Improved Ant Colony Algorithm under Computer Background (J. Phys.: Conf. Ser. 1992 032050)

This article has been retracted by IOP Publishing following an allegation that raises concerns this article may have been created, manipulated, and/or sold by a commercial entity. In addition, IOP Publishing has seen no evidence that reliable peer review was conducted on this article, despite the clear standards expected of and communicated to conference organisers. The authors of the article have been given opportunity to present evidence that they were the original and genuine creators of the work, however at the time of publication of this notice, IOP Publishing has not received any response. IOP Publishing has analysed the article and agrees there are enough indicators to cause serious doubts over the legitimacy of the work and agree this article should be retracted. The authors are encouraged to contact IOP Publishing Limited if they have any comments on this retraction. Retraction published: 9 September 2022

Mission-oriented cooperative 3D path planning for modular solar-powered aircraft with energy optimization

Modular Solar-Powered Aircraft (M-SPA) is a kind of High-Altitude Long-Endurance (HALE) aircraft which exploits the mission advantage of swarm UAV and the HALE advantage of large aspect-ratio SPA. M-SPA’s separated mode and combined mode give it the potential to maximize the mission efficiency with limited solar energy. In this paper, firstly, oriented by the mission of maximizing the cruise area, the overall design of the M-SPA is modeled, including the energy model, the aerodynamic model and the flight environment settings. Secondly, by analyzing the energy consumption of the flight modes, we design a multi-phase flight mission strategy. Then, a 24-hour three-dimensional (3D) flight profile of the M-SPA is optimized, including the sub-SPA cooperative path planning in the separation mode. Finally, inspired by the Traveling Salesman Problem (TSP), an improved Ant Colony Algorithm (ACA) is exploited to find the optimal path for each sub-SPA, which is further developed into a dynamic separation and combination scheme for the M-SPA. The simulation results show that the mission performance of the M-SPA outperforms that of the conventional SPA, and explicitly, the mission coverage of the M-SPA is slightly less than a linear increase under comparable simulation conditions.

Contactless Distribution Path Optimization Based on Improved Ant Colony Algorithm

In the context of the normalization of the epidemic, contactless delivery is becoming one of the most concerned research areas. In the severe epidemic environment, due to the frequent encounter of bayonet temperature measurement, road closure, and other factors, the real-time change frequency of each traffic information is high. In order to improve the efficiency of contactless distribution and enhance user satisfaction, this paper proposes a contactless distribution path optimization algorithm based on improved ant colony algorithm. First of all, the possible traffic factors in the epidemic environment were analyzed, and the cost of each link in the distribution process was modeled. Then, the customer satisfaction is analyzed according to the customer service time window and transformed into a cost model. Finally, the total delivery cost and user satisfaction cost were taken as the optimization objectives, and a new pheromone updating method was adopted and the traditional ant colony algorithm was improved. In the experiment, the effectiveness of the proposed model and algorithm is verified through the simulation optimization and comparative analysis of an example.

A Dynamic Load Balancing Strategy Based on Improved Ant Colony Algorithm

Load balancing technique plays a fundamental role in distribution, while facing numerous challenges. It is difficult for ordinary load algorithms to cope with complex scenarios, and it is likely that uneven load will lead to downtime of a single server, thus causing an avalanche effect and eventually breaking down the whole cluster. This paper proposes a load balancing strategy based on improved ant colony algorithm, considers the influence of task length and processing efficiency in the algorithm based on the original ant colony algorithm, and takes the load rate of nodes as an important index for ants to select path. In addition, the load coefficient includes CPU, memory, IO, and network bandwidth into the calculation range, which makes the obtained server load condition more accurate. Finally, test data of different algorithms are obtained through experiments, and the advantages and disadvantages of this load balancing strategy are verified after horizontal comparison.

Multi-dimensional QoS Cloud Computing Task Scheduling Strategy Based on Improved Ant Colony Algorithm

This paper proposed a multi-dimensional QoS cloud computing task scheduling algorithm based on improved ant colony algorithm, considering QoS demand of users and load balancing of cloud platform comprehensively. First, this paper defines a QoS model composed of the completion time and execution cost of tasks, and defines the cloud platform load balancing constraint function. Secondly, in view of the shortcomings of ant colony algorithm such as slow convergence speed and easy to fall into local optimum, the pheromone update method and expected heuristic function are improved, and the pheromone strength is dynamically changed. Finally, the simulation is carried out in cloudsim and compared with the ACS algorithm and the MMAS algorithm. Experimental results show that the algorithm in this paper is better than these two algorithms in terms of user satisfaction and cloud platform load.

Prediction and Estimation of Book Borrowing in the Library: Machine learning

In the library, the prediction and estimation of book borrowing plays an important role in library work. Based on the data mining method, this paper analyzed the prediction and estimation of book borrowing. Firstly, the radial basis function neural network (RBFNN) was analyzed. Then, the improved ant colony algorithm (IACO) was used to obtain the optimal parameters of RBFNN, and then the IACO-RBFNN model was established to realize the prediction and estimation of book borrowing. The results showed that the improved model had advantages in training time, iteration times, and error compared with BPNN and RBFNN. The results of book prediction and estimation showed that the results obtained by the IACO-RBFNN model were closer to the actual book borrowing situation, with smaller error and higher precision (97.09%), and its precision was 11.18% and 4.74% higher than BPNN and RBFNN respectively. The training time and testing time of the IACO-RBFNN model were 5.12 s and 1.03 s, respectively, which were significantly shorter than the other two methods. The results show that the IACO-RBFNN model has a good performance in the prediction and estimation of book borrowing and can be further promoted and applied in practice.

Research on Route Planning based on improved Ant Colony Algorithm

In order to enhance the safe flight performance of UAV and better complete complex tasks, it is extremely necessary to study route planning. Firstly, the feature points of the landform landform are extracted as the flight track points of the UAV, and the track planning problem is transformed into the traveling salesman problem. Secondly, an adaptive pheromone update method is proposed, and an improved ant colony algorithm for optimizing the local pheromone is proposed to avoid the basic ant colony algorithm from falling into the local optimal solution and improve the global search ability. Finally, the proposed algorithm is verified by simulation experiments. The experimental results show that the improved ant colony algorithm is practical and superior, and can effectively solve the problem of route planning and obtain the optimal path of route planning.

Path Planning of Robot Based on Improved Ant Colony Algorithm in Computer Technology

Mobile robot is a complex control system which integrates environment perception, path planning, dynamic decision making and behavior execution. After decades of development, ant colony algorithm has achieved good results in solving global optimization problems and is widely used in various fields. Although it has the ability to find the optimal solution, it has some defects: slow convergence speed and easy to fall into the local optimal solution. An improved ant colony algorithm is proposed to search through the improved it, select the path independently according to the number of pheromones, and improve by adjusting the heuristic factor function. The reason for this analysis is improved and optimized[1].

Research on Global Path Planning of Artificial Intelligence Robot Based on Improved Ant Colony Algorithm

With the rapid development of science and technology and the improvement of people’s living standards and enjoyment consciousness, robots have come into people’s field of vision more and more. People’s use of robots in production and life has greatly increased. As an important function of the normal use of the robot and evaluating the quality level of the robot, the careful design and optimization of the path planning is the most important. This study discusses the basic situation of robot global path planning, the application, advantages and improvement measures of ant colony algorithm in robot path planning, which provides help for the further development and popularization of robot.

Research on Multi-Agent Task Optimization and Scheduling Based on Improved Ant Colony Algorithm

As a key problem of cloud computing, the performance of task scheduling strategy may seriously affect the efficiency and service quality of the system. With the purpose of achieving balanced task scheduling with optimal completion time, ant colony optimization (ACO) algorithm is adopted as task scheduling strategy in this paper. Considering the problem of premature convergence to local optimal solution of ACO, an improved Max-Min Ant System (MMAS) is introduced to find the global optimal solution. Based on the study of pheromone’s updating mechanism of MMAS, MMAS is applied as task scheduling strategy with reasonable mapping from the objective of shortest path to shortest completion time. The task scheduling strategy based on MMAS has been simulated from views of tasks’ number, size and load balance, and the results shows that MMAS task scheduling strategy is with a better performance on completion time and load balance than ACO based strategy.?

Traveling Salesman Problems With Replenishment Arcs and Improved Ant Colony Algorithms

The traveling salesman problem (TSP), can be used as a typical combinatorial optimization problem, to describe a wide variety of practical engineering optimization problems in various fields. In this study, the problem of personnel and equipment utilization in the transportation industry was abstracted, a more general class of TSPs with replenishment arcs was proposed, an optimization model to minimize the total travel time was established, the ant colony optimization algorithm to solve the standard TSP was improved, and an improved ant colony algorithm based on dynamic heuristic information was designed. Simulation experiments showed that the algorithm can account for the cumulative mileage constraint and search for the shortest path, effectively solving the TSP with replenishment arcs.

基于灰狼优化器改进蚁群算法的物流配送路径优化算法

基于灰狼优化器改进蚁群算法的物流配送路径优化算法

Modelling and characteristics of hybrid coupling hydro-pneumatic suspension for a seven-axle vehicle

A hybrid coupling scheme of parallel connection and contralateral cross-linking was proposed based on the characteristics of high centroid position and low rollover threshold on hydro-pneumatic suspension (HPS) for a seven-axle elevated platform vehicle. A dynamic model with 20 DOFs which can comprehensively reflect vehicle coupling vibration including vertical-lateral-longitudinal-transverse and the joint simulation model of Simulink and AMESim were established based on the structural characteristics of the vehicle. Furthermore, the random road surface model linking the wheels was constructed by filter shaping white noise. Based on the model of hybrid coupling HPS, the study of joint simulation and test on the whole vehicle were conducted, and improved ant colony algorithm (IACA) was applied to the multi-objective optimisation study of HPS parameter matching. The comparison between simulation and test data shows that the weighted acceleration PSD curve is in good agreement, and the relative error of weighted acceleration RMS is small, which indicated that the theoretical model was accurate. The results of simulation and testing show that the optimal parameter matching scheme output by IACA can improve vehicle ride comfort and stability to a significant extent.

Research on Reactive Power Optimization for Distribution Network Based on Dual Population Improved Ant Colony Algorithm

Ant Colony Algorithm is a heuristic search algorithm based on probability selection. It fits for solving the reactive power optimization problem of distribution network, but at the same time, easily falling into the problems of local optimal solution. So Dual Population Improved Ant Colony Algorithm is used to study Reactive Power Optimization Solution. Finally, with an actual example calculation and analysis, and node voltage comparison with and without compensation, the results are proved to be satisfactory. It verified the effectiveness and feasibility of the algorithm and the results show that the algorithm has better effect on optimization.