Application Of Ant Colony Algorithm Research Articles

Application of ant colony algorithm to optimize waste transport distribution routes in Tegal

Application of ant colony algorithm to optimize waste transport distribution routes in Tegal

Optimization of Dynamic Obstacle Avoidance Path of Multirotor UAV Based on Ant Colony Algorithm

In this paper, the real-time path avoidance problem of multirotor UAV in the case of sudden obstacles in two-dimensional environment is studied. The principle, model, and application of ant colony algorithm are analyzed. On this basis, the adaptive dynamic window ant colony algorithm is proposed, and the adaptive dynamic window method is designed; the heuristic function of adding obstacle detection factors and the double pheromone update strategy are made to the ant colony algorithm, and the improved ant colony algorithm is used to replan the path within the dynamic window that can be automatically adjusted to achieve the purpose of obstacle avoidance. A real-time simulation experiment of path planning was conducted by constructing an environment map in MATLAB. The simulation results show that with the continuous increase of the number of sudden obstacles, the real-time replacement path of multirotor UAV also gradually increases, and when approaching the obstacles, the replacement path is more dense, indicating that the adaptive window ant colony algorithm can be applied to dynamic path replacement, and the multirotor UAV can realize dynamic obstacle avoidance path optimization under the condition of sudden obstacles in a short time.

Research on the Application of Ant Colony Algorithm in University Teaching Management Service System

The ant colony algorithm is based on the real ant colony foraging behavior research algorithm, based on the ant colony algorithm university teaching management service system, through the development of one-click automatic course scheduling, drag-and-drop Manual course scheduling, the multi-mode, service-oriented perspective to promote college teaching management, greatly improve the efficiency of college teaching management services, fully adapt to the intelligent college teaching management environment. Based on the ant colony algorithm, this paper analyzes the background of the application of ant colony algorithm in university teaching management service system, and puts forward the application strategy of ant colony algorithm in University Teaching Management Service System.

Evidence Collection and Qualitative Analysis of Electronic Data in the Background of Artificial Intelligence

With the rise and rapid development of mobile communication, intelligent terminal and artificial intelligence technology, in recent years, China has made some progress in the credibility of digital forensics. This paper proposes a forensics method based on artificial intelligence, which makes use of the sequence and dependence characteristics of the previous stage of artificial intelligence to establish an ex post forensics model. When problems occur in the forensics stage, it can feed back to the previous stage and find the problem stage. This paper mainly introduces the forensics and cross examination analysis of electronic data under the background of artificial intelligence. In this paper, we use electronic data forensics and cross examination analysis under the background of artificial intelligence, use a * algorithm for indoor path planning and design, and reasonably analyze the feasibility of Dijkstra algorithm and adjacency table model. This paper analyzes the proof system of the validity of artificial intelligence forensics, uses the evolved expert system to judge the validity of forensics methods and data, and formally proves the integrity and consistency of data in the process of forensics from a theoretical point of view. The experimental results show that the forensic and cross examination analysis of electronic data under the background of artificial intelligence increases the efficiency of the algorithm by 16%. The limitations of the design and research of indoor path planning algorithm for complex environment, and the methods and ways of providing good indoor path planning for the application of ant colony algorithm are analyzed, discussed and summarized, so as to enrich the research results of the academic community.

Computer Application of Game Map Path Finding Based on Fuzzy Logic Dynamic Hierarchical Ant Colony Algorithm

Ant colony algorithm has a wide range of application value in robot path planning. When the robot encounters obstacles, it can find the optimal path in the model space according to relevant performance indexes, and the collision times from the starting point to the target position are the least. In this paper, the research significance of ant colony algorithm in game map path finding is briefly discussed, and the application of ant colony algorithm in map path finding is explained for readers’ reference.

Research on Robot path Planning algorithm based on Biological heuristic Machine Learning algorithm

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.

RETRACTED ARTICLE: Application of ant colony algorithm and artificial intelligence in training simulation of athletes in sports arena

In order to improve the reduction degree of sports competition venue simulation, based on ant colony algorithm, this paper constructs a sports competition venue simulation method based on ant colony algorithm and artificial intelligence, and restores the competition venue and athletes through simulation. Moreover, based on the kinematics model of MPC and athletes, this paper designs a path tracking controller by establishing an error model, designing an appropriate objective function, and system constraints, and this paper adds a path re-planner before the tracking control layer. In addition, this paper selects the appropriate reference point, designs the appropriate obstacle avoidance function and writes it into the objective function to design a path re-planner based on MPC, design a variety of route movement methods, and perform autonomous movement and obstacle avoidance according to constraints. Finally, in order to study the performance of the model, this paper simulates the actual playing field and the competition situation. The research results show that the model constructed in this paper is in high conformity with the actual situation, so it can be used in practice.

Research on Application of Ant Colony Algorithm Based on Multi-sensor Image Fuzzy Fusion in Image Recognition

In the current rapidly developing society, image recognition technology is currently an important means that can provide a full range of dynamic data. It plays an important role in economics, politics and other fields. Fuzzy image fusion algorithms have opened up to solve these problems in image recognition. Based on the current wide application of image recognition, this paper studies the research of multi-sensor image fuzzy fusion algorithm in the application of high-definition image recognition. Aiming at the characteristics of infrared and visible image fusion, an infrared-visible image fusion algorithm based on non-down sampling transformation and hybrid particle swarm algorithm is proposed. For low-frequency sub-images, an improved weighted average method based on area average is used for neighborhood fusion. For high-frequency sub-images, a hybrid particle swarm optimization algorithm is used to select the threshold, and the neighborhood algorithm based on average gradient selection is used for fusion. This algorithm can effectively improve the fusion effect of fused images. The experimental research results show that combining the characteristics of gradient intensity and phase consistency, the characteristic method of compressing the information while retaining important information, which is convenient for real-time processing, is used for related research on sensor image fusion.

Application of Ant Colony Algorithm to Air Route Planning in Helicopter Submarine Searching

Air route planning system is the core of antisubmarine call-search mission which is carried out by an ASW helicopter, and the efficiency of mission completion is decided by the algorithm adopted. Considering actual army training and combat, this paper analyses the basic elements of responding-antisubmarine air route planning system systematically, constitutes assessment index of air route planning system, presents the constraint conditions which should be considered by the system and carries out simulation calculation by ant colony algorithm. Finally, this paper summarizes the applicable circumstances of responding-antisubmarine air route planning methods based on ant colony algorithm, which will be of great value to improve army combat and training efficiency.

Analysis of Energy Optimized Routing Algorithms for Improved Ant Colony in WSN

Level routing LEACH protocol has uneven cluster head distribution, and each cluster head rotation takes place throughout the network, which will cause excessive energy consumption. However, the application of ant colony algorithm in multi-hop routing still has the problem of unbalanced energy consumption. By dividing the network nodes into regions, we can use the residual energy of the nodes as a reference factor to improve the method. By introducing node energy into transfer probability, this paper improves ant colony routing algorithm. The simulation results show that the improved algorithm can overcome the shortcomings of classical ant colony algorithm, which will achieve efficient and real-time optimal routing.

City Delivery Routes Planning Based on the Ant Colony Algorithm

For any company that sells its products in the networks of city stores, the urgent issue is the optimal delivery of their goods. During routing it is necessary to take into account many restrictions caused by specific conditions of transportation process in the city: number of cargoes, nature of cargoes, delivery time, structure of fleet and its presence, work time of enterprises for load matching, drivers’ working hours, loading capacity, road congestion etc. These days, the process of efficient manual routing is difficult because of many restrictions and delivery points wherein it is almost impossible to take into account the road congestion for specific routes. Today's companies are increasingly interested in outsourcing. One of the options for routes planning for enterprises is to use special software products that allow to plan optimal routes according to the chosen criteria and under specific conditions. The process of formation of routes using the Ant Logistics service, based on the Ant Colony optimization algorithm are analysed in the paper. Comparing the two options of forming routes to serve one of the largest retail chains in Kharkiv with the application of Ant Logistics service, it has been elucidated that the application of Ant Colony algorithm is more optimal than the Clarke-Wright algorithm based on delivery routes indicators.

Application of Ant Colony Algorithm in Selecting Shortest Engineering Path

In engineering surveying, topographic maps can be used to find the shortest path. Traditional methods identify the smallest radius based on the design slope before finding the shortest path. This paper, by using the ant colony algorithm and MATLAB software, manages to select the shortest path in engineering practice, and compares the result with that obtained using the genetic algorithm. The results show that the ant colony algorithm can be applied in selecting the shortest path in engineering projects and it can deliver a stable and optimal result after 240 times of iteration. However, the genetic algorithm cannot achieve that even after 400 times of iteration. Therefore, the ant colony algorithm is superior to the genetic algorithm in the aspect of the calculation speed, the stability of achieving the optimal solution, and selection of the shortest path.

Constructing rural e-commerce logistics model based on ant colony algorithm and artificial intelligence method

In order to study the application of ant colony algorithm in rural e-commerce logistics mode, the third-party distribution model was adopted as the logistics method with Xuzhou City’s fruit and vegetable agricultural products as the key research object. Through the model construction, the problem of the third-party distribution model is analyzed. Based on the ant colony algorithm, the shortest path and cost are calculated using MATLAB software. The cost and efficiency problems under different variables are analyzed and the model evaluation is carried out. Finally, the difficulties at the urban and rural end of the third-party distribution model are solved. The results show that the method improves the distribution efficiency and reduces the logistics cost. Therefore, it is conducive to the advancement of rural e-commerce.

Application of ant colony Algorithm and particle swarm optimization in architectural design

By studying the development of ant colony algorithm and particle swarm algorithm, this paper expounds the core idea of the algorithm, explores the combination of algorithm and architectural design, sums up the application rules of intelligent algorithm in architectural design, and combines the characteristics of the two algorithms, obtains the research route and realization way of intelligent algorithm in architecture design. To establish algorithm rules to assist architectural design. Taking intelligent algorithm as the beginning of architectural design research, the authors provide the theory foundation of ant colony Algorithm and particle swarm algorithm in architectural design, popularize the application range of intelligent algorithm in architectural design, and provide a new idea for the architects.

Logistics Terminal Distribution Mode and Path Optimization Based on Ant Colony Algorithm

In order to discuss the logistics distribution that directly affects the satisfaction of consumers for the entire online shopping activities, this article mainly studies the logistics terminal distribution mode and path optimization, and combined with the application of ant colony algorithm in the traveling salesman problem, analyses the basic principle and implementation process of ant colony algorithm. In addition, through reference to map and field research, we consider the route length and road conditions (slope and congestion) of the the regional distribution point, and collect and draw the geographic information surrounding area A. Moreover, some key parameters in ant colony algorithm in value are chosen, and with the collected information as a concrete example, MATLAB simulation is carried out for the logistics terminal distribution path optimization based on ant colony algorithm, and its scientific nature and feasibility are verified. The simulation results showed that the ant colony algorithm has good feasibility so that it can be widely applied. As a result, it is concluded that the application of ant colony algorithm has great significance in the exploration of the logistics terminal distribution path optimization.

Effectiveness of Ant Colony Optimization for Weighted Page Rank Algorithm in Web Access

In general the web is growing very rapidly and data generation is also vast and high. Search Engines play an eminent role in retrieving data from web. The user searching for a topic in a web and it retrieves more than hundreds of searchresults as websites. Among the all websites it is difficult for the user to access all the web pages to find relevant information. Weighted Page rank algorithms play a dominant role to make navigation easier to the user. The popularity of a web page depends on the number of its in links and out links and each webpage gets a proportional page rank value. This algorithm considers only link structure not thecontent of the page, so it returns lesssignificant pages to the user query. To overcome the above issues the study focuses on Ant Colony optimization. This study proposes application of ant colony algorithm for modified weighted page rank algorithm. The ACO concept will discovery of redundant components, use clustering based on the structure similarity or web behavior for user and similar WebPages matching. User and webpage similarity matching using Ant colony Optimization based clustering will leads to better access of the webpage in less time and required webpage.

Application of ant colony algorithm for vehicle routing problems

Application of ant colony algorithm for vehicle routing problems

Application of ant colony algorithm to geochemical anomaly detection

The methods for geochemical anomaly detection are commonly based on statistical models that require assumption of the sample population to satisfy a particular distribution. In practice, the assumption of a particular distribution may degrade the performance of geochemical anomaly detection. In this paper, an ant colony algorithm is used to detect geochemical anomalies. The new method does not require assumption that the geochemical data satisfy a particular distribution. Applying this method to detect geochemical anomalies, we only need to put a number of “virtual ants” randomly into a geochemical grid map and let each ant complete its iterative search process. When the algorithm gets converged, the ants tend to aggregate in the geochemical anomalous regions where geochemical element concentration values are significantly greater than surrounding background. The number of times each grid point is visited by ants can be recorded in ant density data for geochemical anomaly identification. The ant density data are almost not affected by regional variations of geochemical background, thus they are suitable for identifying geochemical anomalies using a threshold method. As an illustration, the ant colony algorithm is applied to detect geochemical anomalies in interpolated concentration data of Au, Ag, Cu, Pb, and Zn in the Altay district in northern Xinjiang in China. The results show that the ant colony algorithm can properly identify geochemical anomalies. Anomalies detected by the ant colony algorithm occupy 9.5% of the study area and contain 36% of the known mineral deposits; and anomalies identified using the Youden index method occupy 16.4% of the study area and contain 56% of the known mineral deposits. Therefore, the ant colony algorithm can serve as a feasible swarm intelligence paradigm for geochemical anomaly detection.

Application of Ant Colony Algorithm in Multi-objective Optimization Problems

In actual application and scientific research, multi-objective optimization is an extremely important research subject. In reality, many issues are related to the simultaneous optimization under multi-objective conditions. The research subject of multi-objective optimization is getting increasing attention. In order to better solve some nonlinear, restricted complex multi-objective optimization problems, based on the current studies of multi-objective optimization and evolutionary algorithm, this paper applies the ant colony algorithm to multi-objective optimization, and proves through experiments that multi-objective ant colony algorithm can converge the real Pareto front of the standard test function more quickly and accurately, and can also maintain the distributivity of the better solution.

Применение алгоритмов муравьиной колонии в решении задачи Штейнера

студент, ФГБОУ ВПО «Оренбургский государственный университет», Оренбург, Россия esandman56@yandex.ruАннотация. Статья посвящена анализу известной и актуальной NP-полной задачи Штейнера, зачастую решаемой с применением му-равьиных алгоритмов. Актуальность данной задачи связана с бурным ро-стом компьютерных сетей, а также возможностью применения в других областях науки и техники, таких как прокладка силовых кабелей, проек-тирование СБИС, задачи логистики. В статье формализуется математи-ческое описание задачи Штейнера. Рассматривается возможность приме-нения различных эвристических «природных» алгоритмов для решения данной задачи. Проводится аналогия между поведением муравьиной колонии и работой алгоритма для решения NP-полной задачи Штейне-ра. Выделяются и описываются характерные особенности различных вариантов муравьиного алгоритма, разработанные другими авторами, приводятся результаты исследования их эффективности и сравнения с