Traditional Ant Colony Algorithm Research Articles

Energy-Saving Virtual Machine Placement Method for User Experience in Cloud Environment

Green computing focuses on the energy consumption to minimize costs and adverse environmental impacts in data centers. Improving the utilization of host computers is one of the main green cloud computing strategies to reduce energy consumption, but the high utilization of the host CPU can affect user experience, reduce the quality of service, and even lead to service-level agreement (SLA) violations. In addition, the ant colony algorithm performs well in finding suitable computing resources in unknown networks. In this paper, an energy-saving virtual machine placement method (UE-ACO) is proposed based on the improved ant colony algorithm to reduce the energy consumption and satisfy users’ experience, which achieves the balance between energy consumption and user experience in data centers. We improve the pheromone and heuristic factors of the traditional ant colony algorithm, which can guarantee that the improved algorithm can jump out of the local optimum and enter the global optimal, avoiding the premature maturity of the algorithm. Experimental results show that compared to the traditional ant colony algorithm, min-min algorithm, and round-robin algorithm, the proposed algorithm UE-ACO can save up to 20%, 24%, and 30% of energy consumption while satisfying user experience.

3D path planning for a robot based on improved ant colony algorithm

Path planning is an important issue in the field of robotics research. Compared with traditional two-dimensional (2D) path planning, three-dimensional (3D) path planning is closer to practical applications. In this paper, a new improved ant colony algorithm is proposed to solve the problem of slow convergence speed, low efficiency and the tendency of falling into the local optimal solution of the traditional ant colony algorithm for the 3D path planning. There are three main improved steps in the novel ant colony algorithm in 3D path planning. In the first step, a pseudo-random state transition strategy is adopted to ensure the global search ability of the algorithm. In the second step, the pheromone update and the pheromone increment calculation method are used to accelerate the convergence speed of the algorithm which can ensure the quality of the solution. In the end step, a security value function of the heuristic function is used to ensure the security of path. In addition, the conditional fallback method is used to ensure the global search ability of the algorithm. Simulation results show that the new improved ant colony algorithm can find a feasible three-dimensional path quickly and efficiently.

Multi-factor of path planning based on an ant colony optimization algorithm

ABSTRACT We propose an improved ant colony algorithm for avoiding obstacles in complex static environments that addresses the problems of a single evaluation factor and low path quality of the traditional ant colony algorithm in path planning. The improvements are: 1) a fuzzy planner is constructed according to the comprehensive evaluation method of fuzzy mathematics and the analytic hierarchy process to comprehensively evaluate and determine the impact of environmental factors, 2) the probability selection formula of the ant colony algorithm is optimized, 3) the pheromone update formula is optimized, and 4) the corner system mechanism is introduced as a post-processing method of path optimization to further smooth the path. Results from simulation experiments of the traditional ant colony algorithm were analysed and compared with those of the improved ant colony algorithm, showing that the latter has a stronger path planning ability and higher algorithm efficiency, resulting in a smoother path with a lower negative impact by environmental factors. Thus, the proposed algorithm is expected to provide a computational basis for effective multi-factor path planning in realistic environments, thereby saving human and material resources.

Application of Improved Ant Colony Algorithm in Optimal Planning of Dynamic Distribution Network

In this paper, the analysis is carried out in order to optimize the dynamic distribution network. The analysis is based on the basic principles of distribution network planning technology. It summarizes the ant colony algorithm and the improved ant colony algorithm, and proposes an improved ant colony algorithm for the distribution network planning the mathematical model construction method, and finally verifying the example. The results show that the improved ant colony algorithm has more advantages than the traditional ant colony algorithm, and it is more in line with the basic principles. The digital model of the distribution network planning has been successfully implemented, and it has a good performance in the calculation examples. The application of improved ant colony algorithm to optimize the planning of dynamic distribution network can improve the quality of the planning scheme, indicating that the algorithm has higher application value.

Optimal path selection for logistics transportation based on an improved ant colony algorithm

The ant colony algorithm, as a heuristic intelligent optimisation algorithm, has succeeded in solving many real-world problems, such as the vehicle routing. However, the traditional ant colony algorithm has suffered from several shortcomings, including the premature stagnation and slow convergence. To address these issues, an improved ant colony algorithm is proposed in this paper. The main contribution is to adaptively adjust key parameters during the evolution. Later the proposed algorithm is validated by addressing the vehicle routing problem. Two real-world datasets are collected from two logistic enterprises separately (i.e., YUNDA and YTO) based in Huainan City, China. Comprehensive experiments have been performed by applying the proposed algorithm to search for the optimal path. Meanwhile, the comparison between the traditional ant colony algorithm and the improved algorithm has been conducted accordingly. Experimental result shows that the proposed algorithm achieves better performance in minimising routing path and reducing the computational cost.

Power Line Communications Networking Method Based on Hybrid Ant Colony and Genetic Algorithm

When solving the routing problem with traditional ant colony algorithm, there is scarce in initialize pheromone and a slow convergence and stagnation for the complex network topology and the time-varying characteristics of channel in power line carrier communication of low voltage distribution grid. The algorithm is easy to fall into premature and local optimization. Proposed an automatic network algorithm based on improved transmission delay and the load factor as the evaluation factors. With the requirements of QoS, a logical topology of power line communication network is established. By the experiment of MATLAB simulation, verify that the improved Dynamic hybrid ant colony genetic algorithm (DH_ACGA) algorithm has improved the communication performance, which solved the QoS routing problems of power communication to some extent.

Path planning of mobile robot based on adaptive ant colony algorithm

The traditional ant colony algorithm has some problems, such as low search efficiency, slow convergence speed and local optimum. To solve those problems, an adaptive heuristic function is proposed, heuristic information is updated by using the shortest actual distance, which ant passed. The reward and punishment rules are introduced to optimize the local pheromone updating strategy. The state transfer function is optimized by using pseudo-random state transition rules. By comparing with other algorithms’ simulation results in different simulation environments, the results show that it has effectiveness and superiority on path planning.

A Wireless Mesh Multipath Routing Protocol Based on Sorting Ant Colony Algorithm

Abstract In this paper, the traditional ant colony algorithm has a slow convergence rate for routing optimization of Mesh networks. A multi-path routing protocol based on improved ant colony algorithm, Fortified Ant protocol, is proposed. The protocol first adds a sorting algorithm based on the ant colony algorithm, and introduces the concept of elite ants to improve the speed of routing optimization. Secondly, this paper also studies the multipath transmission of self-organizing networks. The simulation results show that compared with ADOV, DSR and AOC routing algorithms, the algorithm can quickly find multiple paths with better quality, with fast convergence and overhead. Less advantage.

Intelligent traffic cloud computing system based on ant colony algorithm

With the growth of data volume in transportation system, requirements of big data technologies are rapidly increasing. This paper presented an improved ant colony algorithm by using data analysis technologies of cloud computing and data mining. And the influence of different spatio-temporal feature fusion methods on the steering wheel angle value of intelligent vehicles is explored by feature fusion method. Furthermore, time-constrained and space-constrained networks are utilized to extract the key features that affect the steering wheel angle value. Experimental results show that the proposed algorithm improves the efficiency of data processing and information search by 35%, comparing to traditional ant colony algorithm. It is very effective in the shortest path analysis of ITS. Our research shows that the application of real-time information in the logistics distribution system can make the planning process more dynamic and the prediction results closer to reality.

High-Frequency Path Mining-Based Reward and Punishment Mechanism for Multi-Colony Ant Colony Optimization

To solve the problem of falling into local optimum and poor convergence speed of traditional ant colony algorithm, this paper proposes a High-frequency path mining-based Reward and Punishment mechanism for multi-colony Ant Colony Optimization (HRPACO). Firstly, the pheromone concentration on the path of effective strong association is rewarded adaptively according to the lift of association rules to accelerate the convergence speed. Secondly, the pheromone concentration on the path of minimum spanning tree is punished adaptively according to the support of association rules to improve the diversity of the colony. The interaction of reward and punishment mechanism can effectively balance the diversity and convergence. Finally, a self-evolutionary mechanism based on Gaussian filter is proposed to adaptively adjust the pheromone concentration by dynamic smoothing of the pheromone matrix, so as to help the colony jump out of the local optimum. The TSP is used to verify the performance of the algorithm. The simulation results show that the proposed algorithm can effectively accelerate the convergence speed and improve the accuracy of solution, especially for large-scale problems. Meanwhile, path planning is used to verify the feasibility of the proposed algorithm. The simulation results show that the algorithm can find an effective and better path even in the environment of complex obstacles.

A Shared Parking Space Optimization Model to Alleviate China’s Parking Problem Considering Travelers’ Tiered Credit Risk

ABSTRACT A shared parking space optimization (SPSO) model is established to alleviate China’s parking problem considering travelers’ tiered credit risk. The SPSO model has the following characteristics: travelers’ possibilities for default are measured by credit value; travelers with different credit tiers are classified and managed; the success of the shared parking space will be affected by the traveler’s credit value. In order to solve the SPSO model, we transform it into a vertex coloring problem of an undirected graph and then modify the ant colony algorithm (ACA) by improving in two aspects of pheromone concentration and termination rule of the traditional ACA algorithm. Numerical experiments of different sizes are performed to evaluate the validity and efficiency of the proposed model. This study may cater for the deficiencies in the research of credit risk in the implementation of shared parking mechanism to a certain extent.

An improved ant colony optimization algorithm based on particle swarm optimization algorithm for path planning of autonomous underwater vehicle

The motion control of autonomous underwater vehicle (AUV) has got more and more attention because AUV has been used in many applications in recent years. In order to find the optimal path for AUV to reach the specified destination in complex undersea environment, an improved ant colony optimization (ACO) algorithm based on particle swarm optimization (PSO) algorithm is proposed. Due to the various constraints, such as the limited energy and limited visual distance, the improved ACO algorithm uses improved pheromone update rule and heuristic function based on PSO algorithm to make AUV find the optimal path by connecting the chosen nodes of the undersea environment while avoiding the collision with the complex undersea terrain (static obstacles). The improved ACO algorithm based on PSO algorithm can overcome disadvantages of the traditional ACO algorithm, such as falling into local extremum, poor quality, and low accuracy. Experiment results demonstrate that improved ACO algorithm is more effective and feasible in path planning for autonomous underwater vehicle than the traditional ant colony algorithm.

Path planning for drones reconnaissance based on ant colony algorithms

Firstly, the traditional ant colony algorithm is introduced. Then it compares the difference between drone reconnaissance path planning problem and TSP. improving the traditional ant colony algorithm, which constrains the state transfer equation of ant colony algorithm so that the algorithm can simultaneously Planning multiple paths. Finally, we conducted an example analysis of road detection in Puerto Rico with the DroneGo disaster response system, and covered the island with a minimum number of drones.

The Application of Adaptive Ant-colony A* Hybrid Algorithm Based on Objective Evaluation Factor in RoboCup Rescue Simulation Dynamic Path Planning

This paper proposed an adaptive A* Hybrid algorithm combining the advantages of ant colony and A* algorithm. Improvements of the traditional ant-colony algorithm was suggested, and the Target Evaluation Factor based on global dynamic information was introduced to promote the performance of ant path decision. In additional, an adaptive pheromone updating strategy was designed to balance and speed up the convergence rate. Test results showed that this algorithm can effectively guide agents to get an optimal path to ensure the efficient completion of tasks.

A Cellular Ant Colony Algorithm for Path Planning Using Bayesian Posterior Probability

In order to solve the problem of slow convergence rate in traditional ant colony algorithm for UAV path planning, a new cellular ant colony algorithm is proposed. First, we construct a sector prediction area in grid environment map. Then we build heuristic and obstacle repulsion functions of target nodes in the prediction area. Using these functions, we can get the Bayesian conditional probability and posterior estimation of target nodes. In the end, we select the node with the largest posterior estimation as the next path node. The simulation results show that the new algorithm has better global search ability. Furthermore, using the sector prediction area makes the planned path more consistent with the UAV flight characteristics. And the designed functions in the sector prediction area speeds up the path search process.

Application of Hybrid Ant Colony Algorithm in Urban Traffic Path Planning

To plan the urban traffic path using the ant colony algorithm, the composition and functional division of the mobile robot are analyzed. The TSP (Traveling Salesman Problem) is used to deeply understand the traditional ant colony algorithm. Then, based on this, the improvement scheme of the traditional ant colony algorithm is analyzed. The results showed that the artificial potential field method and the A* algorithm improved the performance of the ant colony algorithm. At the initial stage of the search path, the blindness and randomness of the ant colony algorithm due to insufficient pheromone concentration in each path were solved. The local optimal path is avoided with the development of algorithm iteration. Therefore, the improved ant colony algorithm is superior to the traditional ant colony algorithm.

Improvement of culture media efficiency in Internet of Things based on global numerical ant colony algorithm

Exploring the cultural media under the Internet of Things is not only an inevitable need to understand the cultural landscape of the Internet of Things era, but also an extension of the people themselves in the new medium. Understanding the Internet of Things as a new paradigm of media organization and production, it should clarify the technology and body in the Internet of Things, so it can clarify the objective for a relationship with the media. The Internet of Things solves the virtual interdimensional problem of information dissemination and social interaction, opening up a new vision of cultural communication. However, the Internet of Things technology makes the ideological and cultural communication channels more diverse and more complex. How to improve the efficiency of cultural media is the hotspot of current research. Therefore, this article takes cultural media as the research object and adopts the Internet of Things technology to construct the IoT culture media efficiency optimization model of the global numerical ant colony algorithm. Firstly, the population space and belief space of the cultural algorithm are redesigned by using the new bi-level evolutionary mechanism. Then, the maximum and minimum ant colony system is used to construct the population space, and the current optimal solution is improved by the 3-opt cross-transform operation in the belief space. Finally, the simulation experiments are set up to verify the effectiveness of the proposed algorithm. The experimental results show that the global numerical ant colony algorithm based on the Internet of Things is better than the traditional ant colony algorithm and the cultural ant colony algorithm, and the convergence speed is faster, which further improves the efficiency of the cultural media.

Study on an Optimal Path Planning for a Robot Based on an Improved ANT Colony Algorithm

To solve the path planning problems of rescuing and coal exploring robot in three-dimensional space environment, a path planning method of rescuing and coal exploring robot based on the improved ant colony algorithm was proposed. Firstly, a three-dimensional model was built with the mountainous elevation data and grid method. Furthermore, on the basis of the traditional ant colony algorithm, node transition probability, node selection way and pheromone update method were respectively optimized and improved through introducing a new heuristic function factor, node random selection mechanism and update strategy of pheromone that includes the local updating and global updating of pheromone. Finally, the feasibility and effectiveness of ant colony algorithm was simulated and tested with MATLAB software. The simulation results showed that the traditional ant colony algorithm and improved ant colony algorithm both could search out a security optimal path for rescuing & coal exploring robot in three dimensional space environment. Under the different task requirements, comparing with the traditional ant colony algorithm, the improved ant colony algorithm could effectively shorten the searching path length and reduce the path searching time. Moreover, the improved ant colony algorithm also showed a greater decision-making ability and better convergence performance. The simulation results indicated the improved ant colony algorithm should be correct, feasible and effective.

Brainstorming-Based Ant Colony Optimization for Vehicle Routing With Soft Time Windows

In this paper, we propose a novel ant colony optimization algorithm based on improved brainstorm optimization (IBSO-ACO) to solve the vehicle routing problem with soft time windows. Compared with the traditional ant colony algorithm, the proposed IBSO-ACO can better address the local optimum problem, since we have carefully designed an improved brainstorming optimization algorithm to update the solutions obtained by the ant colony algorithm, which enhance the solution diversity and the global search ability. Furthermore, we use the classification method to accelerate the convergence of the proposed algorithm. The extensive experimental results have confirmed that the proposed IBSO-ACO algorithm can achieve a lower routing cost at a high convergence rate than the traditional ant colony algorithm and the simulated annealing ant colony algorithm.

Path Optimization of Mobile Sink Node in Wireless Sensor Network Water Monitoring System

In the water area monitoring of the traditional wireless sensor networks (WSNs), the monitoring data are mostly transmitted to the base station through multihop. However, there are many problems in multihop transmission in traditional wireless sensor networks, such as energy hole, uneven energy consumption, unreliable data transmission, and so on. Based on the high maneuverability of unmanned aerial vehicles (UAVs), a mobile data collection scheme is proposed, which uses UAV as a mobile sink node in WSN water monitoring and transmits data wirelessly to collect monitoring node data efficiently and flexibly. In order to further reduce the energy consumption of UAV, the terminal nodes are grouped according to the dynamic clustering algorithm and the nodes with high residual energy in the cluster are selected as cluster head nodes. Then, according to the characteristics of sensor nodes with a certain range of wireless signal coverage, the angular bisection method is introduced on the basis of the traditional ant colony algorithm to plan the path of UAV, which further shortens the length of the mobile path. Finally, the effectiveness and correctness of the method are proved by simulation and experimental tests.