scholarly journals Swarm Intelligence from Natural to Artificial Systems: Ant Colony Optimization

2016 ◽  
Vol 8 (1) ◽  
pp. 9-17 ◽  
Author(s):  
Deepa O. ◽  
Senthilkumar A.
Keyword(s):  
Ant Colony Optimization ◽  
Swarm Intelligence ◽  
Ant Colony

scholarly journals Comparative assessment of the optical-electronic images segmentation quality by the ant colony optimization and the artificial bee colony

2021 ◽  
pp. 104-113
Author(s):  
Г.В. Худов ◽  
І.А. Хижняк
Keyword(s):  
Ant Colony Optimization ◽  
Swarm Intelligence ◽  
Artificial Bee Colony ◽  
Visual Assessment ◽  
Comparative Assessment ◽  
Ant Colony ◽  
Bee Colony ◽  
Segmentation Methods ◽  
Segmentation Quality

The article discusses the methods of swarm intelligence, namely, an improved method based on the ant colony optimization and the method of an artificial bee colony. The goal of the work is to carry out a comparative assessment of the optical-electronic images segmentation quality by the ant colony optimization and the artificial bee colony. Segmentation of tonal optical-electronic images was carried out using the proposed methods of swarm intelligence. The results of the segmentation of optical-electronic images obtained from the spacecraft are presented. A visual assessment of the quality of segmentation results was carried out using improved methods. The classical errors of the first and second kind of segmentation of optoelectronic images are calculated for the proposed methods of swarm intelligence and for known segmentation methods. The features of using each of the proposed methods of swarm intelligence are determined. The tasks for which it is better to use each of the proposed methods of swarm intelligence are determined.

scholarly journals Image retrieval based on swarm intelligence

2021 ◽  
Vol 11 (6) ◽  
pp. 5390
Author(s):  
Shahbaa I. Khaleel ◽  
Ragad W. Khaled
Keyword(s):  
Ant Colony Optimization ◽  
Swarm Intelligence ◽  
Clustering Algorithm ◽  
High Efficiency ◽  
Water Drop ◽  
Optimization Method ◽  
Modern Technology ◽  
Ant Colony ◽  
Query Image ◽  
Fuzzy Method

To keep pace with the development of modern technology in this information technology era, and the immense image databases, whether personal or commercial, are increasing, is requiring the management of these databases to strong and accurate systems to retrieve images with high efficiency. Because of the swarm intelligence algorithms are great importance in solving difficult problems and obtaining the best solutions. Here in this research, a proposed system is designed to retrieve color images based on swarm intelligence algorithms. Where the algorithm of the ant colony optimization (ACOM) and the intelligent water drop (IWDM) was used to improve the system's work by conducting the clustering process in these two methods on the features extracted by annular color moment method (ACM) to obtain clustered data, the amount of similarity between them and the query image, is calculated to retrieve images from the database, efficiently and in a short time. In addition, improving the work of these two methods by hybridizing them with fuzzy method, fuzzy gath geva clustering algorithm (FGCA) and obtaining two new high efficiency hybrid algorithms fuzzy ant colony optimization method (FACOM) and fuzzy intelligent water drop method (FIWDM) by retrieving images whose performance values are calculated by calculating the values of precision, recall and the f-measure. It proved its efficiency by comparing it with fuzzy method, FGCA and by methods of swarm intelligence without hybridization, and its work was excellent.

scholarly journals Swarm Intelligence based efficient routing algorithm for platooning in VANET through Ant Colony Optimization

2019 ◽  
Vol 8 (9) ◽  
pp. 1238-1244
Keyword(s):  
Ant Colony Optimization ◽  
Swarm Intelligence ◽  
Routing Protocols ◽  
Ad Hoc ◽  
Routing Algorithm ◽  
Optimization Technique ◽  
Ant Colony ◽  
Delivery Ratio ◽  
Average Delay ◽  
Size Number

Today’s era is of smart technology, Computing intelligence and simulations. Many areas are now fully depended on simulation results for implementing real time workflow. Worldwide researchers and many automobile consortium are working to make intelligent Vehicular Ad hoc Network but till yet it is just a theory-based permutation. If we take VANET routing procedures then it is mainly focussing on AODV, DSDV and DSR routing protocols. Similarly, one more area of Swarm Intelligence is also attained attention of industry and researchers. Due the behavior of dynamic movement of vehicle and ants, Ant Colony Optimization is best suited for VANET performance simulations. Much of the work has already done and in progress for routing protocols in VANET but not focused on platooning techniques of vehicle nodes in VANET. In our research idea, we came up with a hypothesis that proposes efficient routing algorithm that made platooning in VANET optimized by minimizing the average delay waiting and stoppage time. In our methodology, we have used OMNET++, SUMO, Veins and Traci for testing of our hypothesis. Parameters that we took into consideration are end-to-end delay as an average, packet data delivery ratio, throughput, data packet size, number of vehicle nodes etc. Swarm Intelligence has proved a way forward in VANET scenarios and simulation for more accurate results. In this paper, we implemented Ant Colony Optimization technique in VANET simulation and proved through results that if it integrates with VANET routing scenarios then result will be at its best.

scholarly journals Ant Colony Optimization: A Swarm Intelligence based Technique

2013 ◽  
Vol 73 (10) ◽  
pp. 30-33 ◽  
Author(s):  
Manju Manju ◽  
Chander Kant
Keyword(s):  
Ant Colony Optimization ◽  
Swarm Intelligence ◽  
Ant Colony

The Swarm Intelligence Optimization Algorithm and its Application

2013 ◽  
Vol 711 ◽  
pp. 659-664
Author(s):  
Li Shan Li
Keyword(s):  
Particle Swarm Optimization ◽  
Ant Colony Optimization ◽  
Swarm Intelligence ◽  
Optimization Algorithm ◽  
Pso Algorithm ◽  
Ant Colony ◽  
Shuffled Frog Leaping Algorithm ◽  
Swarm Optimization ◽  
Shuffled Frog Leaping ◽  
Swarm Intelligence Optimization

In the article, three kinds of swarm intelligence optimization algorithm are discussed including the ant colony optimization (ACO) algorithm, the particle swarm optimization (PSO) algorithm and the shuffled frog leaping algorithm (SFLA). The principle, development and application of each algorithm is introduced. Finally, an example of TSP is used to test the performance of ACO.

A Comprehensive Review of Ant Colony Optimization (ACO) based Energy-Efficient Routing Protocols for Wireless Sensor Networks

2014 ◽  
Vol 3 (3) ◽  
pp. 33-55 ◽  
Author(s):  
Anand Nayyar ◽  
Rajeshwar Singh
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Ant Colony Optimization ◽  
Swarm Intelligence ◽  
Routing Protocols ◽  
Energy Efficient ◽  
Ant Colony ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Energy Efficient Routing

Wireless Sensor Networks (WSNs) have always been a hot area of researchers for finding more solutions towards making WSN network more energy efficient and reliable. Energy efficient routing is always a key challenging task to enhance the network lifetime and balance energy among the sensor nodes. Various solutions have been proposed in terms of energy efficient routing via protocol development, various techniques have also been incorporated like Genetic Algorithm, Swarm Intelligence etc. The main aim of this research paper to study all the routing protocols which are energy efficient and are based on Ant Colony Optimization (ACO). This paper also highlights the pros and cons of each of routing protocol which has been developed on lines of Energy Efficiency and has also been compared among one another to find which protocol outwits one another. Further, we conclude that Swarm Intelligence being a novel and bio-inspired field has contributed as well as contributing much in the area of improving routing issues of sensor networks.

A survey and comparative analysis of the various routing protocols of Internet of Things

2017 ◽  
Vol 13 (3) ◽  
pp. 264-281 ◽  
Author(s):  
Shailja Agnihotri ◽  
K.R. Ramkumar
Keyword(s):  
Comparative Analysis ◽  
Internet Of Things ◽  
Ant Colony Optimization ◽  
Swarm Intelligence ◽  
Routing Protocols ◽  
Optimization Technique ◽  
Network Size ◽  
Routing Algorithms ◽  
Ant Colony ◽  
Content Type

Purpose The purpose of this paper is to provide insight into various swarm intelligence-based routing protocols for Internet of Things (IoT), which are currently available for the Mobile Ad-hoc networks (MANETs) and wireless sensor networks (WSNs). There are several issues which are limiting the growth of IoT. These include privacy, security, reliability, link failures, routing, heterogeneity, etc. The routing issues of MANETs and WSNs impose almost the same requirements for IoT routing mechanism. The recent work of worldwide researchers is focused on this area. Design/methodology/approach The paper provides the literature review for various standard routing protocols. The different comparative analysis of the routing protocols is done. The paper surveys various routing protocols available for the seamless connectivity of things in IoT. Various features, advantages and challenges of the said protocols are discussed. The protocols are based on the principles of swarm intelligence. Swarm intelligence is applied to achieve optimality and efficiency in solving the complex, multi-hop and dynamic requirements of the wireless networks. The application of the ant colony optimization technique tries to provide answers to many routing issues. Findings Using the swarm intelligence and ant colony optimization principles, it has been seen that the protocols’ efficiency definitely increases and also provides more scope for the development of more robust, reliable and efficient routing protocols for the IoT. Research limitations/implications The existing protocols do not solve all reliability issues and efficient routing is still not achieved completely. As of now no techniques or protocols are efficient enough to cover all the issues and provide the solution. There is a need to develop new protocols for the communication which will cater to all these needs. Efficient and scalable routing protocols adaptable to different scenarios and network size variation capable to find optimal routes are required. Practical implications The various routing protocols are discussed and there is also an introduction to new parameters which can strengthen the protocols. This can lead to encouragement of readers, as well as researchers, to analyze and develop new routing algorithms. Social implications The paper provides better understanding of the various routing protocols and provides better comparative analysis for the use of swarm-based research methodology in the development of routing algorithms exclusively for the IoT. Originality/value This is a review paper which discusses the various routing protocols available for MANETs and WSNs and provides the groundwork for the development of new intelligent routing protocols for IoT.

Sign in / Sign up

Export Citation Format

Share Document