Ant Colony Optimization and Swarm Intelligence

2004 ◽  
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.

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.

scholarly journals Liquid temperature prediction in bubbly flow using ant colony optimization algorithm in the fuzzy inference system as a trainer

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Meisam Babanezhad ◽  
Iman Behroyan ◽  
Ali Taghvaie Nakhjiri ◽  
Azam Marjani ◽  
Amir Heydarinasab ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Ant Colony Optimization ◽  
Swarm Intelligence ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Ant Colony ◽  
Output Data ◽  
Inference System ◽  
Learning Framework ◽  
Logic System

AbstractIn the current research paper a novel hybrid model combining first-principle and artificial intelligence (AI) was developed for simulation of a chemical reactor. We study a 2-dimensional reactor with heating sources inside it by using computational fluid dynamics (CFD). The type of considered reactor is bubble column reactor (BCR) in which a two-phase system is created. Results from CFD were analyzed in two different stages. The first stage, which is the learning stage, takes advantage of the swarm intelligence of the ant colony. The second stage results from the first stage, and in this stage, the predictions are according to the previous stage. This stage is related to the fuzzy logic system, and the ant colony optimization learning framework is build-up this part of the model. Ants movements or swarm intelligence of ants lead to the optimization of physical, chemical, or any kind of processes in nature. From point to point optimization, we can access a kind of group optimization, meaning that a group of data is studied and optimized. In the current study, the swarm intelligence of ants was used to learn the data from CFD in different parts of the BCR. The learning was also used to map the input and output data and find out the complex connection between the parameters. The results from mapping the input and output data show the full learning framework. By using the AI framework, the learning process was transferred into the fuzzy logic process through membership function specifications; therefore, the fuzzy logic system could predict a group of data. The results from the swarm intelligence of ants and fuzzy logic suitably adapt to CFD results. Also, the ant colony optimization fuzzy inference system (ACOFIS) model is employed to predict the temperature distribution in the reactor based on the CFD results. The results indicated that instead of solving Navier–Stokes equations and complex solving procedures, the swarm intelligence could be used to predict a process. For better comparisons and assessment of the ACOFIS model, this model is compared with the genetic algorithm fuzzy inference system (GAFIS) and Particle swarm optimization fuzzy inference system (PSOFIS) method with regards to model accuracy, pattern recognition, and prediction capability. All models are at a similar level of accuracy and prediction ability, and the prediction time for all models is less than one second. The results show that the model’s accuracy with low computational learning time can be achieved with the high number of CIR (0.5) when the number of inputs ≥ 4. However, this finding is vice versa, when the number of inputs < 4. In this case, the CIR number should be 0.2 to achieve the best accuracy of the model. This finding could also highlight the importance of sensitivity analysis of tuning parameters to achieve an accurate model with a cost-effective computational run.

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