Pheromone-Based Ant Colony Algorithm for Optimal Proliferation of Research

2013 ◽  
Vol 734-737 ◽  
pp. 3152-3157
Author(s):  
Lei Lei Deng
Keyword(s):  
Optimization Design ◽  
Ant Colony Algorithm ◽  
Optimization Problems ◽  
Climatic Conditions ◽  
Water Saving ◽  
Ant Colony ◽  
Path Model ◽  
Route Optimization ◽  
Path Optimization ◽  
Field Plots

In order to realize the water-saving irrigation of field plots path pipeline deployment management and control, the pheromone of ant colony algorithm for optimization design of. Ant colony algorithm (ACA) is a kind of Bionic Engineering with the development of the optimization algorithm, is mainly based on ant foraging in the search for the shortest path model and form. This article attempts in the existing ant colony algorithm combinatorial optimization of real defect on the basis of field plots, to coordinate as a data source, An improved ant colony algorithm for field plots wiring path design, thereby improving the ant colony algorithm in an iterative process to update the optimal solution ability, Finally in the same number of iterations to find path shorter, cost less rules, solve agricultural water-saving irrigation pipeline path optimization deployment problem. And in the VC++ program validation path optimization problems. The test results show that, under the same climatic conditions, route optimization design results can be deployed for water saving irrigation pipeline layout management provides reference basis and data support.

scholarly journals The Research on Low Carbon Logistics Routing Optimization Based on DNA-Ant Colony Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Liyi Zhang ◽  
Ying Wang ◽  
Teng Fei ◽  
Hongwei Ren
Keyword(s):  
Carbon Emissions ◽  
Optimization Model ◽  
Ant Colony Algorithm ◽  
Optimization Problems ◽  
Ant Colony ◽  
Path Optimization ◽  
Low Carbon ◽  
Economic Activities ◽  
Logistics Industry ◽  
Routing Optimization

As the energy conservation and emission reduction and sustainable development have become the hot topics in the world, low carbon issues catch more and more attention. Logistics, which is one of the important economic activities, plays a crucial role in the low carbon development. Logistics leads to some significant issues about consuming energy and carbon emissions. Therefore, reducing energy consumption and carbon emissions has become the inevitable trend for logistics industry. Low carbon logistics is introduced in these situations. In this paper, from the microcosmic aspects, we will bring the low carbon idea in the path optimization issues and change the amount of carbon emissions into carbon emissions cost to establish the path optimization model based on the optimization objectives of the lowest cost of carbon emissions. According to different levels of air pollution, we will establish the double objectives path optimization model with the consideration of carbon emissions cost and economy cost. Use DNA-ant colony algorithm to optimize and simulate the model. The simulation indicates that DNA-ant colony algorithm could find a more reasonable solution for low carbon logistics path optimization problems.

scholarly journals Network Intrusion Detection using a Combination of Fuzzy Clustering and Ant Colony Algorithm

2021 ◽  
Vol 5 (2) ◽  
pp. 11-19
Author(s):  
Yadgar Sirwan Abdulrahman
Keyword(s):  
Intrusion Detection ◽  
Ant Colony Algorithm ◽  
Optimization Problems ◽  
Detection System ◽  
Ant Colony ◽  
Detection Accuracy ◽  
Ant Colony Optimization Algorithm ◽  
Network Intrusion ◽  
Secure Network ◽  
Main Component

As information technology grows, network security is a significant issue and challenge. The intrusion detection system (IDS) is known as the main component of a secure network. An IDS can be considered a set of tools to help identify and report abnormal activities in the network. In this study, we use data mining of a new framework using fuzzy tools and combine it with the ant colony optimization algorithm (ACOR) to overcome the shortcomings of the k-means clustering method and improve detection accuracy in IDSs. Introduced IDS. The ACOR algorithm is recognized as a fast and accurate meta-method for optimization problems. We combine the improved ACOR with the fuzzy c-means algorithm to achieve efficient clustering and intrusion detection. Our proposed hybrid algorithm is reviewed with the NSL-KDD dataset and the ISCX 2012 dataset using various criteria. For further evaluation, our method is compared to other tasks, and the results are compared show that the proposed algorithm has performed better in all cases.

scholarly journals Contactless Distribution Path Optimization Based on Improved Ant Colony Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Feng Wu
Keyword(s):  
Customer Service ◽  
User Satisfaction ◽  
Ant Colony Algorithm ◽  
Time Window ◽  
Cost Model ◽  
Ant Colony ◽  
Path Optimization ◽  
Traffic Information ◽  
Research Areas ◽  
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.

Comparison of GA, ACO algorithm, and PSO algorithm for path optimization on free-form surfaces using coordinate measuring machines

2021 ◽  
Author(s):  
Yueping Chen ◽  
Naiqi Shang
Keyword(s):  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Path Optimization ◽  
Inspection Time ◽  
Free Form ◽  
Coordinate Measuring Machines ◽  
Ant Colony Optimization Algorithm ◽  
Measuring Machine ◽  
Free Form Surfaces

Abstract Coordinate measuring machines (CMMs) play an important role in modern manufacturing and inspection technologies. However, the inspection process of a CMM is recognized as time-consuming work. The low efficiency of coordinate measuring machines has given rise to new inspection strategies and methods, including path optimization. This study describes the optimization of an inspection path on free-form surfaces using three different algorithms: an ant colony optimization algorithm, a genetic algorithm, and a particle swarm optimization algorithm. The optimized sequence of sampling points is obtained in MATLAB R2020b software and tested on a Leitz Reference HP Bridge Type Coordinate Measuring Machine produced by HEXAGON. This study compares the performance of the three algorithms in theoretical and practical conditions. The results demonstrate that the use of the three algorithms can result in a collision-free path being found automatically and reduce the inspection time. However, owing to the different optimization methodologies, the optimized processes and optimized times of the three algorithms, as well as the optimized paths, are different. The results indicate that the ant colony algorithm has better performance for the path optimization of free-form surfaces.

scholarly journals Optimization design of reconfiguration algorithm for high voltage power distribution network based on ant colony algorithm

Open Physics ◽  
2018 ◽  
Vol 16 (1) ◽  
pp. 1094-1106 ◽  
Author(s):  
Zhen Li ◽  
Yao Zhang ◽  
Muhammad Aqeel Ashraf
Keyword(s):  
High Voltage ◽  
Power Distribution ◽  
Optimization Design ◽  
Power Flow ◽  
Ant Colony Algorithm ◽  
Distribution Network ◽  
Network Reconstruction ◽  
Ant Colony ◽  
Power Distribution Network ◽  
Power Flow Calculation

Abstract Distribution network reconfiguration is a very complex and large-scale combinatorial optimization problem. In network reconfiguration, whether an effective solution can be obtained is a key issue. Aiming at the problems in network reconstruction by traditional algorithm, such as long time required, more times of power flow calculation and high network loss, a network optimization design algorithm based on improved ant colony algorithm for high voltage power distribution network is proposed. After analyzing the operating characteristics of the high voltage power distribution network, the network topology of the high voltage power distribution network is described by constructing a hierarchical variable-structure distribution network model. A mathematical model of distribution network reconstruction considering the opportunity constraint with the minimum network loss as the objective function is established. The power flow distribution is calculated by using the pre-push back-generation method combined with the hierarchical structure of the distribution network. The maximum and minimum ant colony algorithm is introduced to improve the pheromone updating method of the traditional ant colony algorithm, and the search range is expanded, so that the algorithm can jump out of the local optimization trap to realize the accurate solution of the power distribution network reconstruction model. The experimental results show that compared with the current network reconstruction algorithm, the proposed algorithm requires less time for convergence, less power flow calculation, and lower network loss.

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