Improved Colony Algorithm Research Articles

An Information Classification Optimization Model Based on Improved Ant Colony Algorithm

In the information management process, it is necessary to classify all information optimization to provide the support for quickly query the search target providing this, a classification optimization method based on improved ant colony algorithm is proposed in this paper. That is, extract the characteristics of information attribute, and then establish the optimization classification model based on the characteristics to achieve the classification of information. Experimental results show that using the improved algorithm for classification information, the classification accuracy can be improved to meet the actual demand data management.

Improved Ant Colony Algorithm Based on PSO and its Application on Cloud Computing Resource Scheduling

This paper introduces PSO algorithm into ant colony optimization algorithm so that an improved ant colony optimization algorithm named ACA-PSO is proposed. The ACA-PSO algorithm can get more effective optimal solutions by using PSO algorithm to do crossover operation and mutation operation so as to avoid trapping in local optimum. Finally, the simulation experiment reflects that the ACA-PSO algorithm speeds the convergence up which is more suitable for resource scheduling in cloud computing.

Image Segmentation Algorithm Based on Improved Ant Colony Algorithm

Image Segmentation Algorithm Based on Improved Ant Colony Algorithm

Global Path Planning Method for USV System Based on Improved Ant Colony Algorithm

An improved ant colony algorithm based grid environment model for global path planning method for USV was introduced. The main idea of the improved ant colony algorithm was distributing each ant route dynamically. When the active ant was selecting the next route, this algorithm program determined the nearest direction to the end point. There were many possible route points which were distributed artificially. Thereby, the probability for each ant to choose the right direction was increased. The simulating results demonstrate that the improved ant colony algorithm in this paper is very suitable for solving the question of global path planning for USV system in the complex oceanic environment where there are a lot of obstacles. At the same time, this method costs less time, and the path is very smooth.

Optimization Planning based on Improved Ant Colony Algorithm for Robot

As the ant colony algorithm has the defects in robot optimization path planning such as that low convergence cause local optimum, an improved ant colony algorithm is proposed to apply to the planning of path finding for robot. This algorithm uses the search way of exhumation ant to realize the complementation of advantages and accelerate the convergence of algorithm. The experimental result shows that the algorithm of this paper make the optimization planning of robot more reasonable

Study on Fast Codeword Search Based on Improved Ant Colony Algorithm

Vector quantization technology is an efficient and competitive method for data compression, but it is not easy to be implemented because of the comparatively high computation complexity it requires during the coding and decoding process. This paper presents a method of Dual Population Ant Colony Algorithm Codeword Quick Search (DPACAS), exploiting the mechanism of ant trace the optimal path through the pheromones remained, and the behavior pattern of making objects together by picking up and putting down them. It uses Parallel Ant Colony algorithm to sufficiently accelerate the convergence of the ant colony. When the scale of the codebook becomes larger, by setting parameters reasonably and exchanging the pheromones between two species, it broadens the search space, reduces the search time and improves the algorithmic global convergence effectively.

Range expansion of cooperative mobile wireless network based on swarm intelligence optimization

We research maximum range expansion using a fixed number of mobile wireless nodes along a line which do distributed MIMO (Multiple Input Multiple Output) in diversity configuration, also known as cooperative transmission (CT). The emphasis is on optimal clusters and locations of these clusters to maximize the multi-hop reach along the line based on the swarm intelligence algorithms which are the Glowworm Swarm Optimization (GSO) and Improved Ant Colony Algorithm (IACA). We build the CT model which cannot be solved by conventional method, so we solve it by using GSO and IACA algorithms to get the optimal result. In GSO algorithm, every mobile wireless node is considered as a glowworm, and the intensity of signs is the intensity of luciferin. The direction of movement is determined as well as the direction of movement function under the constraint of outage probability in CT model. The contribution to the IACA is to modify the heuristic function and the pheromone update rule based on the optimization function to avoid local optimal result. Simulation experiments compared GSO, IACA with ACA and Exhaustive Attack Method (EAM), which proves GSO and IACA are effective, and the advantage of IACA is high accuracy and GSO is time saving.

An Improved Ant Colony Algorithm for Genome Rearrangements

An Improved Ant Colony Algorithm for Genome Rearrangements

Research of In-Car Navigation Based on Improved Ant Colony Algorithm

Determining how to select path efficiently in complex transportation networks was one of the main problems in-car navigation systems. For the drawbacks of slow convergence and easy to fall into local optimal solution of basic ant colony algorithm in solving the optimal path problem, a method of improving the expect-heuristic function is proposed in this paper, which enhances search direction and improves the convergence rate. Meanwhile, with the introduction of a new strategy to update the pheromone on ant colony system, the contradiction that convergence speed brings stagnation is balanced. The results show that the improved ant colony algorithm is easier to get the optimal solution compared with basic ant colony algorithm, and the convergence speed is faster, having a good navigation effect.

Quantitative estimating size of deep defects in multi-layered structures from eddy current NDT signals using improved ant colony algorithm

Detection and quantitative estimation of deep defects in multi-layered structures is an essential task in a range of technological applications, such as maintaining the integrity of structures, enhancing the safety of aging aircraft, and assuring the quality of products. A novel approach to accurately quantify the two-dimensional axisymmetric deep defect size from eddy current nondestructive testing (NDT) signals is presented here. The method uses a finite element forward model to simulate the underlying physical process and an improved ant colony algorithm (IACA) to solve the inverse problem. Experiments are carried out. The performance comparison between the IACA method and the least square method is shown. The comparison results demonstrate the feasibility and validity of the IACA method. Between them, the IACA method gives a better estimation performance than the least square method at present.

Logistics Cost Optimization in Total Life Cycle of Power Grid Engineering Project Using Improved Ant Colony Algorithm

Effective control of the logistics costs of the power grid engineering cost project can minimize the total cost, and improve the economic efficiency of it. This paper regards the minimal logistics cost of the power grid engineering cost project in the total life cycle as goal, and puts forward the project logistics time-cost trade-off optimization model based on improved ant colony algorithm. This model integrates the two objectives of time and cost into a single objective in terms of adaptive weight approach, and this approach is applied to ant colony algorithm to search for solutions. The result of the case study indicates that this approach is very effective for determining practical solutions, and has strong global searching ability and efficiency. So the model is very suitable for solving the problem of power grid engineering cost project logistics cost optimization.

An Improved Ant Colony Algorithm Based on Path Optimization Strategy for TSP

Traveling salesman problem (TSP) is not only a combinatorial optimization problem but also a classical NP-hard problem, which has high application value. Ant colony algorithm (ACA) is very effective for solving TSP problem, but basic ant colony algorithm has drawbacks of low convergence rate and easily trapping in local optimal solution. An improved ant colony algorithm was proposed. It used path optimization strategy to exchange the position of cities to find the better solution for TSP. Simulation results show the improved algorithm has better optimal solution and higher efficiency.

Research and Application of an Improved Ant Colony Algorithm

Ant colony algorithm is a bionic algorithm which is used to optimize the shortest path in graph. But the traditional ant colony algorithm has some disadvantages, such as slow convergence speed, easy to fall into local optimum, high complexity and so on. In this paper, it focus on the problems of slow convergence speed and easily falling into local optimum and contribute the local pheromone updating strategy and global pheromone updating strategy, it also optimize the routing formula and local search method after analyzing the problems. It conducts some simulation experiments about our optimization scheme and the traditional ant colony algorithm in Matlab environment, by comparing the results of experiments, the optimization scheme proposed can get a better search path in different examples and the μ (t) function can effectively reduce iterations.

Research of Multiple Constrained Qos Routing Based on Improved Ant Colony Algorithm

This paper mainly discusses the Multiple Constrained Qos Routing Algorithm, in the analysis of the basic on the basis of mathematical model of Qos routing. It has proposed a routing model with an objective function, integrated optimization of multiple Qos constraints. Finally, the optimization of ant colony algorithm applied to solve the problem of multiple constraints Qos. Through simulation experiments proved that the algorithm to find the path to maximum degree meets Qos requirements.

Obstacle Avoidance Path Planning for Harvesting Robot Manipulator Based on MAKLINK Graph and Improved Ant Colony Algorithm

To the problems of real-time obstacle avoidance path planning for apple harvesting robot manipulator in dynamic and unstructured environment, a method based on improved ant colony algorithm is presented. Firstly, Vector description is utilized to describe the area where obstacles such as branches is located as irregular polygon in free space, and MAKLINK graph is used to build up the environment space model. Then, the improved Dijkstra algorithm is used to find the initial walk path for apple harvesting robot manipulator. Finally, the improved ant colony algorithm is applied to optimize the initial path. The experiment result shows that the proposed method is simple and the robot manipulator can avoid the branches to pick the apple successfully in a relatively short time.

DC Motor Control Using PID Controller Based on Improved Ant Colony Algorithm

This paper presents analysis for two different types of controllers for speed control of separately excited DC motor. Improved Ant colony algorithm has been analyzed and presented as an adapted optimization algorithm for tuning PID controller parameters. Conventional PID has been applied for the same machine. Comparison between the two controllers is provided based on step response parameters like maximum overshoot, rise time and settling time.

Road Network Vulnerability Analysis Based on Improved Ant Colony Algorithm

We present an improved ant colony algorithm-based approach to assess the vulnerability of a road network and identify the critical infrastructures. This approach improves computational efficiency and allows for its applications in large-scale road networks. This research involves defining the vulnerability conception, modeling the traffic utility index and the vulnerability of the road network, and identifying the critical infrastructures of the road network. We apply the approach to a simple test road network and a real road network to verify the methodology. The results show that vulnerability is directly related to traffic demand and increases significantly when the demand approaches capacity. The proposed approach reduces the computational burden and may be applied in large-scale road network analysis. It can be used as a decision-supporting tool for identifying critical infrastructures in transportation planning and management.

Using Improved Ant Colony Algorithm to Investigate EMU Circulation Scheduling Problem

High-speed railway is one of the most important ways to solve the long-standing travel difficulty problem in China. However, due to the high acquisition and maintenance cost, it is impossible for decision-making departments to purchase enough EMUs to satisfy the explosive travel demand. Therefore, there is an urgent need to study how to utilize EMU more efficiently and reduce costs in the case of completing a given task in train diagram. In this paper, an EMU circulation scheduling model is built based on train diagram constraints, maintenance constraints, and so forth; in the model solving process, an improved ACA algorithm has been designed. A case study is conducted to verify the feasibility of the model. Moreover, contrast tests have been carried out to compare the efficiency between the improved ACA and the traditional approaches. The results reveal that improved ACA method can solve the model with less time and the quality of each representative index is much better, which means that efficiency of the improved ACA method is higher and better scheduling scheme can be obtained.

Knowledge-Domain Semantic Searching and Recommendation Based on Improved Ant Colony Algorithm

To obtain accurate search results and advocate the use of human effort in discovering knowledge, we propose a method based on Ant Colony Algorithm (ACA). The proposed method simulates the behavior of ants searching for food. Specific features such as the behavior of ants searching for food, their established search paths, and the ant “neighborhood” profile are investigated. The investigation results reveal that the behavior of people searching for useful information resembles that of ants searching for food. We also use semantic annotation and the decreasing matrix dimension approach to accelerate the food searching process and shorten the distance between the query starting points and the ultimate answers. A user behavior model is constructed based on personal and domain ontologies. Experimental evaluation with the enhanced ACA has two parts: (1) estimating the efficiency of information retrieval with user interests considered and (2) identifying how to weigh usage and rate user data during recommendation.

Research on 3D Route Planning for UAV in Low-Altitude Penetration Based on Improved Ant Colony Algorithm

It has discussed the approach to establish the models of terrain, threat and the evaluation of route cost in the route planning for UAV in low-altitude penetration. An Ant Algorithm is introduced into UAV route planning, since stagnation may appear during searching in use of traditional ant colonies algorithm,this paper introduces yaw angle to improve heuristic information, establishes the prior search set, makes ant colony algorithm the more rapid and effective search to the best route, simulation results prove the efficiency of the planning method.