The document discusses ant colony optimization, an algorithm inspired by ant behavior, and its application to routing protocols in wired and wireless networks. It explains how biological ants are able to find the shortest path to food sources using pheromone trails, and how this concept was adapted into an algorithm where simulated ants leave virtual pheromone trails to probabilistically find optimal paths. The algorithm is demonstrated through simulations where "ant agents" explore networks and progressively reinforce shorter routing paths between nodes. Research examples show the ant colony approach can find higher quality routes with lower overhead compared to traditional routing protocols like OSPF.
KohonAnts: A Self-Organizing Ant Algorithm for Clustering and Pattern Classif...Antonio Mora
You can try it at:
http://geneura.ugr.es/~amorag/kohonants/KAnts.zip
This is the presentation of KohonAnts (KANTS), an hybrid Ant Colony and Self-organizing Map algorithm for clustering and pattern classification.
Esta es la presentación de KohonAnts (KANTS), un algoritmo que combina conceptos de los algortimos de hormigas y los mapas autoorganizativos y que se puede utilizar para agrupamiento (clustering) o clasificación de patrones.
Ant Colony (-based) Optimisation – a way to solve optimisation problems based on the way that ants indirectly communicate directions to each other we call Stigmergy.
Ant Colony Optimization for Load Balancing in CloudChanda Korat
here the presentation gives the natural behavior of ants and how the that logic is applicable to cloud for load balancing is discussed here with detailed literature survey.
KohonAnts: A Self-Organizing Ant Algorithm for Clustering and Pattern Classif...Antonio Mora
You can try it at:
http://geneura.ugr.es/~amorag/kohonants/KAnts.zip
This is the presentation of KohonAnts (KANTS), an hybrid Ant Colony and Self-organizing Map algorithm for clustering and pattern classification.
Esta es la presentación de KohonAnts (KANTS), un algoritmo que combina conceptos de los algortimos de hormigas y los mapas autoorganizativos y que se puede utilizar para agrupamiento (clustering) o clasificación de patrones.
Ant Colony (-based) Optimisation – a way to solve optimisation problems based on the way that ants indirectly communicate directions to each other we call Stigmergy.
Ant Colony Optimization for Load Balancing in CloudChanda Korat
here the presentation gives the natural behavior of ants and how the that logic is applicable to cloud for load balancing is discussed here with detailed literature survey.
Swarm Intelligence: An Application of Ant Colony OptimizationIJMER
Swarm intelligence, a branch of artificial intelligence is a part which discusses the collective
behaviour of social animals such as ants, fishes, termites, birds, bacteria. The collective behaviour of
animals to achieve target can be used in practical applications. One of the applications is ant colony
optimization. Ongoing research of ACO, there are diverse applications namely data mining, image
processing, power electronic circuit design etc. One of that is network routing. By using ACO, we can
find the shortest path in network routing
Ant colony optimization based routing algorithm in various wireless sensor ne...Editor Jacotech
Wireless Sensor Network has several issues and challenges due to limited battery backup, limited computation capability, and limited computation capability. These issues and challenges must be taken care while designing the algorithms to increase the Network lifetime of WSN. Routing, the act of moving information across an internet world from a source to a destination is one of the vital issue associated with Wireless Sensor Network. The Ant Colony Optimization (ACO) algorithm is a probabilistic technique for solving computational problems that can be used to find optimal paths through graphs. The short route will be increasingly enhanced therefore become more attractive. The foraging behavior and optimal route finding capability of ants can be the inspiration for ACO based algorithm in WSN. The nature of ants is to wander randomly in search of food from their nest. While moving, ants lay down a pheromone trail on the ground. This chemical pheromone has the ability to evaporate with the time. Ants have the ability to smell pheromone. When selecting their path, they tend to select, probably the paths that has strong pheromone concentrations. As soon as an ant finds a food source, carries some of it back to the nest. While returning, the quantity of chemical pheromone that an ant lay down on the ground may depend on the quantity and quality of the food. The pheromone trails will lead other ants towards the food source. The path which has the strongest pheromone concentration is followed by the ant which is the shortest paths between their nest and food source. This paper surveys the ACO based routing in various Networking domains like Wireless Sensor Networks and Mobile Ad Hoc Networks.
This presentation provides an introduction to the Ant Colony Optimization topic, it shows the basic idea of ACO, advantages, limitations and the related applications.
Algoritmo de Optimización basado en Colonias de Hormigas (OCH) para la resolución del problema de búsqueda del camino óptimo, para una unidad militar en un campo de batalla, considerando los criterios de Rapidez y Seguridad.
Estudio del paránetro de ponderación de objetivos (Lambda).
-------------------------------------------------------
Ant Colony Optimization algorithm for solving the Multiobjective military unit pathfinding problem in the battlefield, considering two criteria: Speed and Safety.
Study of the objective balance parameter (Lambda).
A Study of Parallel Approaches in MOACOs for solving the Bicriteria TSPAntonio Mora
In this work, the parallelization of some Multi-Objective Ant Colony Optimization (MOACO) algorithms has been performed. The aim is to get a better performance, not only in running time (usually the main objective when a distributed approach is implemented), but also improving the spread of solutions over the Pareto front (the ideal set of solutions). In order to do this, colony-level (coarse- grained) implementations have been tested for solving the Bicriteria TSP problem, yielding better sets of solutions, in the sense explained above, than a sequential approach.
Swarm Intelligence: An Application of Ant Colony OptimizationIJMER
Swarm intelligence, a branch of artificial intelligence is a part which discusses the collective
behaviour of social animals such as ants, fishes, termites, birds, bacteria. The collective behaviour of
animals to achieve target can be used in practical applications. One of the applications is ant colony
optimization. Ongoing research of ACO, there are diverse applications namely data mining, image
processing, power electronic circuit design etc. One of that is network routing. By using ACO, we can
find the shortest path in network routing
Ant colony optimization based routing algorithm in various wireless sensor ne...Editor Jacotech
Wireless Sensor Network has several issues and challenges due to limited battery backup, limited computation capability, and limited computation capability. These issues and challenges must be taken care while designing the algorithms to increase the Network lifetime of WSN. Routing, the act of moving information across an internet world from a source to a destination is one of the vital issue associated with Wireless Sensor Network. The Ant Colony Optimization (ACO) algorithm is a probabilistic technique for solving computational problems that can be used to find optimal paths through graphs. The short route will be increasingly enhanced therefore become more attractive. The foraging behavior and optimal route finding capability of ants can be the inspiration for ACO based algorithm in WSN. The nature of ants is to wander randomly in search of food from their nest. While moving, ants lay down a pheromone trail on the ground. This chemical pheromone has the ability to evaporate with the time. Ants have the ability to smell pheromone. When selecting their path, they tend to select, probably the paths that has strong pheromone concentrations. As soon as an ant finds a food source, carries some of it back to the nest. While returning, the quantity of chemical pheromone that an ant lay down on the ground may depend on the quantity and quality of the food. The pheromone trails will lead other ants towards the food source. The path which has the strongest pheromone concentration is followed by the ant which is the shortest paths between their nest and food source. This paper surveys the ACO based routing in various Networking domains like Wireless Sensor Networks and Mobile Ad Hoc Networks.
This presentation provides an introduction to the Ant Colony Optimization topic, it shows the basic idea of ACO, advantages, limitations and the related applications.
Algoritmo de Optimización basado en Colonias de Hormigas (OCH) para la resolución del problema de búsqueda del camino óptimo, para una unidad militar en un campo de batalla, considerando los criterios de Rapidez y Seguridad.
Estudio del paránetro de ponderación de objetivos (Lambda).
-------------------------------------------------------
Ant Colony Optimization algorithm for solving the Multiobjective military unit pathfinding problem in the battlefield, considering two criteria: Speed and Safety.
Study of the objective balance parameter (Lambda).
A Study of Parallel Approaches in MOACOs for solving the Bicriteria TSPAntonio Mora
In this work, the parallelization of some Multi-Objective Ant Colony Optimization (MOACO) algorithms has been performed. The aim is to get a better performance, not only in running time (usually the main objective when a distributed approach is implemented), but also improving the spread of solutions over the Pareto front (the ideal set of solutions). In order to do this, colony-level (coarse- grained) implementations have been tested for solving the Bicriteria TSP problem, yielding better sets of solutions, in the sense explained above, than a sequential approach.
Migration study on a Pareto-based island model for MOACOsAntonio Mora
This is the presentation of the paper of the same title at the Genetic and Evolutionary Computation Comference (GECCO) 2013.
The work describes an analysis of a proposed island (distribution) model for Multi-Objective Ant Coloy Optimization algorithms.
It presents the results of some different neighbourhood topologies and migration rates.
Many notable inventions are being inspired by nature’s ingenuity, numerous engineering problems were solved by mimicking the results of the hidden intelligence, products of hundreds of millions years of trial and error. The nature is a testimony to the amazing ability of ordered-chaos to lead to unbelievably innovative solutions, often for nearly unsolvable problems. Nature, the world's largest innovation lab, created astonishing solutions to problems science haven't figured out yet. Let's explore a small subset of those - algorithms.
An introduction to Swarm Intelligence, the most popular algorithms used and the applications of swarm intelligence.
This presentation talks about the Ant Colony Optimization and the Particle Swarm Optimization, while mentioning the other algorithms used.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Performance Evaluation of Different Network Topologies Based On Ant Colony Op...ijwmn
All networks tend to become more and more complicated. They can be wired, with lots of routers, or wireless, with lots of mobile node. The problem remains the same, in order to get the best from the network; there is a need to find the shortest path. The more complicated the network is, the more difficult it is to manage the routes and indicate which one is the best. The Nature gives us a solution to find the shortest path. The ants, in their necessity to find food and brings it back to the nest, manage not only to explore a vast area, but also to indicate to their peers the location of the food while bringing it back to the nest. Most of the time, they will find the shortest path and adapt to ground changes, hence proving their great efficiency toward this difficult task. The purpose of this paper is to evaluate the performance of different network topologies based on Ant Colony Optimization Algorithm. Simulation is done in NS-2.
Secured Rural Communication Development Using Mobile Ad-hoc Networkijsrd.com
Rural areas where resource of communication lacks and the area like earthquake zone require a strong communication channel. India is one of the fastest growing countries in the world but fact is there are more than 600 villages still lack the proper communications. Mobile ad-hoc network is one of the networks which often requires seamless ubiquitous network, which can be utilized to structure the wireless mode of communication, for the transmission of strategic information among mobile agents in areas where no rigid infrastructure can be set up for a network. Mobile ad-hoc network is best suited for this kind of situation because of the flexibility and scalability it offers. No central administration is required for such network, as it works on temporary connection links, making the network ubiquitous. The prime challenge in implementing such network with multi-agent communication is the need of an efficient routing protocol for multi-hop connectivity, frequently changing topology and dynamic routes. This paper proposes a new multi-agent based routing algorithm to facilitate the reliable communication in rural areas network. The proposed algorithm is named as Ant Algorithm for Mobile Ad-hoc Network. The proposed algorithm is inspired from the ant colony optimization metaheuristic and is based on swarm intelligence, uses simple node-level management with probabilistic multi-path routing which highly resembles to the behavior of real world harvester ants to find the shortest path between their nest and the food source. This paper will discuss about the security ethics of the Adhoc network. Techniques that will make MANET more secure and Algorithms for security in MANET.
PERFORMANCE ANALYSIS OF ANTHOCNET ROUTING PROTOCOL FOR HYBRID AD HOC NETWORKKhushbooGupta145
Mobile Ad hoc Networks (MANETs) are communication networks which consist of wireless nodes, placed together in an ad hoc manner, i.e. with minimal prior planning. The random node movement unpredictable behavior makes the topology very dynamic in nature. MANETs poses substantially different challenges to routing protocols than more traditional wired networks. The classification of MANETs protocols are Proactive based, Reactive based or Bio-inspired routing protocols. AntHocNet is a bio-inspired routing protocol based on ant colony optimization (ACO) which has many parallels with biology
thus the solutions of biology can also be used to solve the problems of computer networks.
This paper discusses the implementation and performance analysis of the AntHocNet algorithm which is based on the nature-inspired Ant Colony Optimization framework for
routing in mobile MANETs. AntHocNet is an adaptive hybrid algorithm which combines the reactive route setup process with the proactive maintenance process. The reactive route setup is carried out at the start of a communication session or whenever the source of a current session has no more routing information available for the destination. The proactive route maintenance is run for the entire duration of the session. Its aim is to keep
information about existing routes up to date and explore new routes. During simulation, the performance of AntHocNet is compared with DSR and AODV routing protocols.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
JMeter webinar - integration with InfluxDB and Grafana
Ant Colony Algorithm
1. Ant Colony Optimization An adaptative nature inspired algorithm explained, concretely implemented, and applied to routing protocols in wired and wireless networks.
12. Adaptation of the Ants-based algorithm to routing protocols Ants will start from A the nest and look for D the food. At every step, they will upgrade the routing tables and as soon as the first one reaches the food, the best path will be known, thus allowing communication from D to A. E D B A F C Nest Food
![Plan ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)