- Ant colony optimization (ACO) is an algorithm inspired by the behavior of real ant colonies. It is used to find solutions to discrete optimization problems.
- The algorithm operates by simulating ants walking on the problem graph, depositing and following virtual pheromone trails, with the goal of eventually finding the shortest route. Ants have a higher probability of following paths marked by strong pheromone concentrations.
- Over time, the pheromone trails reinforce, guiding more ants toward the best solutions. Pheromone also gradually evaporates, preventing stagnation on locally optimal solutions. After many iterations, the shortest path emerges as the strongest trail.
This presentation provides an introduction to the Ant Colony Optimization topic, it shows the basic idea of ACO, advantages, limitations and the related applications.
In computer science and operation research, the ant colony optimization algorithm (ACO) is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graph.
Optimization techniques: Ant Colony Optimization: Bee Colony Optimization: Tr...Soumen Santra
Optimization techniques: Ant Colony Optimization: Bee Colony Optimization: Traveling Salesman Problem
Features of Ant Colony
Features of Ant
Features of other Optimization Techniques
Algorithm
Flow Charts
This presentation provides an introduction to the Ant Colony Optimization topic, it shows the basic idea of ACO, advantages, limitations and the related applications.
In computer science and operation research, the ant colony optimization algorithm (ACO) is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graph.
Optimization techniques: Ant Colony Optimization: Bee Colony Optimization: Tr...Soumen Santra
Optimization techniques: Ant Colony Optimization: Bee Colony Optimization: Traveling Salesman Problem
Features of Ant Colony
Features of Ant
Features of other Optimization Techniques
Algorithm
Flow Charts
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.
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.
Various Metaheuristic algorithms For Securing VANETKishan Patel
Metaheuristic can be considered as a "master strategy that guides and modifies other heuristics to produce solutions. Generally metaheuristic is used for solving problem in ad hoc networks.
All networks tend to become more and more complicated. They can be wired, with lots of routers, or wireless, with lots of mobile nodes… 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. Thus, they know where their nest is, and also their destination, without having a global view of the ground. 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 project is to provide a clear understanting of the Ants-based algorithm, by giving a formal and comprehensive systematization of the subject. The simulation developed in Java will be a support of a deeper analysis of the factors of the algorithm, its potentialities and its limitations. Then the state-of-the-arts utilisation of this algorithm and its implementations in routing algorithms, mostly for mobile ad hoc networks, will be explained. Results of recent studies will be given and resume the current employments of this great algorithm inspired by the Nature.
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
it will describe how ants will follow the shortest path among available routes for food and shelter. It also enumerate the application of this concept in computer and allied fields.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
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Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
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Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
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.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
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.
2. Ant Colony Optimization (ACO)
Overview
“Ant Colony Optimization (ACO) studies
artificial systems that take inspiration
from the behavior of real ant colonies
and which are used to solve discrete
optimization problems.”
4. Ant Colony Optimization (ACO)
A C O
• Ant Colony Optimization is another family of optimization algorithms
inspired by pheromone-based strategies of ant foraging.
• ACO algorithms were originally conceived to find the shortest route in
travelling salesman problems.
• In ACO several ants travel across the edges that connect the nodes of
the graph while depositing virtual pheromones.
• PHEROMONES : a chemical substance secreted externally by some
animals(especially insects) that influence the physiology or behavior
of other animals(insects) of same species.
5. Ant Colony Optimization (ACO)
A C O
• Ants that travel on the shortest path will be able to make more return
trips and deposit more pheromones in a given amount of time.
• Consequently, that path will attract more ants in a positive feedback
loop.
• ACO assumes that virtual pheromones evaporates ,thus reducing the
probability that long paths are selected.
• Pheromone evaporation has also the advantage of avoiding the
convergence to a locally optimal solution. If there were no
evaporation at all, the paths chosen by the first ants would tend to be
excessively attractive to the following ones. In that case, the
exploration of the solution space would be constrained.
7. Ant Colony Optimization (ACO)
• Almost blind.
• Incapable of achieving complex tasks alone.
• Rely on the phenomena of swarm intelligence for survival.
• Capable of establishing shortest-route paths from their colony to feeding sources
and back.
• Use stigmergic communication via pheromone trails.
Ants….
8. Ant Colony Optimization (ACO)
• Follow existing pheromone trails with high probability.
• What emerges is a form of autocatalytic behavior: the more ants follow a trail,
the more attractive that trail becomes for being followed.
• The process is thus characterized by a positive feedback loop, where the
probability of a discrete path choice increases with the number of times the
same path was chosen before.
Ants ,contd.
9. Ant Colony Optimization (ACO)
Ants ,contd.
It is well known that the primary means for ants to form and maintain the line is a
pheromone trail. Ants deposit a certain amount of pheromone while walking, and each
ant probabilistically prefers to follow a direction ,rich in pheromone.
ℙ 𝐶 < ℙ 𝐵 < ℙ 𝐴
ℙ(𝐴)
ℙ(𝐵)
ℙ(𝐶)
10. Ant Colony Optimization (ACO)
E
D
CH
B
A
(b)
30 ants
30 ants
15 ants
15 ants
15 ants
15 ants
t = 0
d = 0.5
d = 0.5
d = 1
d = 1
E
D
CH
B
A
(a)
E
D
CH
B
A
(c)
30 ants
30 ants
20 ants
20 ants
10 ants
10 ants
t = 1
τ = 30
τ = 30
τ = 15
τ = 15
Initial state:
no ants
11. Ant Colony Optimization (ACO)
Pheromone trails
Shortest path around an obstacle
This elementary behavior of real ants can be used to explain how they can find the
shortest path that reconnects a broken line after the sudden appearance of an
unexpected obstacle has interrupted the initial path.
Ants are moving on a
straight line that connects
a food source to their nest.
Let us consider the following scenario:
12. Ant Colony Optimization (ACO)
Pheromone trails
Shortest path around an obstacle
This elementary behavior of real ants can be used to explain how they can find the
shortest path that reconnects a broken line after the sudden appearance of an
unexpected obstacle has interrupted the initial path.
An obstacle appears on
the path.
13. Ant Colony Optimization (ACO)
Pheromone trails
Shortest path around an obstacle
This elementary behavior of real ants can be used to explain how they can find the
shortest path that reconnects a broken line after the sudden appearance of an
unexpected obstacle has interrupted the initial path.
Those ants which are just in front of the obstacle
cannot continue to follow the pheromone trail
and therefore they have to choose between
turning right or left. In this situation we can
expect half the ants to choose to turn right and
the other half to turn left.
14. Ant Colony Optimization (ACO)
Pheromone trails
Shortest path around an obstacle
This elementary behavior of real ants can be used to explain how they can find the
shortest path that reconnects a broken line after the sudden appearance of an
unexpected obstacle has interrupted the initial path.
Those ants which choose, by chance, the shorter
path around the obstacle will more rapidly
reconstitute the interrupted pheromone trail
compared to those which choose the longer
path. Thus, the shorter path will receive a
greater amount of pheromone per time unit and
in turn a larger number of ants will choose the
shorter path.
15. Ant Colony Optimization (ACO)
Pheromone trails
Shortest path around an obstacle
This elementary behavior of real ants can be used to explain how they can find the
shortest path that reconnects a broken line after the sudden appearance of an
unexpected obstacle has interrupted the initial path.
Shortest path is being obtained.
16. Ant Colony Optimization (ACO)
Pheromone trails
Shortest path from the nest to the food source
Ants are able, without using any spatial Information, to identify a sudden appearance of
a food source around their nest, and to find the shortest available path to it.
Let us describe the algorithm:
A small amount of ants travel
randomly around the nest.
N
17. Ant Colony Optimization (ACO)
Pheromone trails
Shortest path from the nest to the food source
Ants are able, without using any spatial Information, to identify a sudden appearance of
a food source around their nest, and to find the shortest available path to it.
One of the ants find food source.
S
N
18. Pheromone trails
Shortest path from the nest to the food source
When ant finds food, it returns to
the nest while laying down
pheromones trail.
S
N
Ants are able, without using any spatial Information, to identify a sudden
appearance of a food source around their nest, and to find the shortest
available path to it.
19. Ant Colony Optimization (ACO)
Pheromone trails
Shortest path from the nest to the food source
Ants are able, without using any spatial Information, to identify a sudden
appearance of a food source around their nest, and to find the shortest
available path to it.
When other ants find a pheromone trail,
they are likely not to keep travelling at
random, but to instead follow the trail.
S
N
20. Ant Colony Optimization (ACO)
Pheromone trails
Shortest path from the nest to the food source
If an ant eventually find food by following
a pheromone trail, it returning to the nest
while reinforcing the trail with more
pheromones.
S
N
Ants are able, without using any spatial Information, to identify a sudden
appearance of a food source around their nest, and to find the shortest
available path to it.
21. Ant Colony Optimization (ACO)
Pheromone trails
Shortest path from the nest to the food source
Due to their stochastic behavior,
some ants are not following the
pheromone trails, and thus uncover
more possible paths.
S
N
Ants are able, without using any spatial Information, to identify a sudden
appearance of a food source around their nest, and to find the shortest
available path to it.
22. Ant Colony Optimization (ACO)
Pheromone trails
Shortest path from the nest to the food source
Over time, however, the pheromones
trails starts to evaporate, thus
reducing its attractive strength.
S
N
Ants are able, without using any spatial Information, to identify a sudden
appearance of a food source around their nest, and to find the shortest
available path to it.
23. Ant Colony Optimization (ACO)
Pheromone trails
Shortest path from the nest to the food source
Shortest path is being obtained.
S
N
Ants are able, without using any spatial Information, to identify a sudden
appearance of a food source around their nest, and to find the shortest
available path to it.
24. Ant Colony Optimization (ACO)
• Let us consider the algorithm more formally. The number of ants M is usually equal
to the number of nodes N in the graph.
• A small amount of virtual pheromones is deposited on all edges of the beginning of
the search.
• The probability 𝑝 𝑖𝑗
𝑘
that ant k chooses the edge from node i to node j.
𝑝 𝑖𝑗
𝑘
=
𝜏𝑖𝑗
𝑎
𝜂𝑖𝑗
𝑏
ℎ ℰ 𝐽 𝑘
𝐻
𝜏𝑖ℎ
𝑎
𝜂𝑖ℎ
𝑏
Where 𝜏𝑖𝑗 = amount of virtual pheromones on that edge .
𝜂𝑖𝑗 = visibility of the node computed as the inverse of the edge length
1
𝑙 𝑖𝑗
.
constant a & b weight the importance of the two factors.
Formal ANT Algorithm
25. Ant Colony Optimization (ACO)
• If a = 0, ants choose solely on the basis of shortest distance .
• Conversely if b = 0, ants choose solely on the basis of the pheromones amount.
• The divider in the fraction sums up the pheromones and visibility values for the
edges H that are available at the node where the ants sits as long as they belong to
the set 𝐽 𝑘 of the nodes that the ants k has not yet visited .
• as soon as the ant visits a node , this is deleted from the list 𝐽 𝑘 .
• Once all the ants have completed a tour of the graph, each ant k retraces its own
path and deposits an amount of pheromones ∆τ𝑖𝑗
𝑘
on the travelled edges according
to
• where 𝐿 𝑘
= total length of the path found by ant k.
• Q is a constant , which is set to be the length of the shortest path estimated with a
simple heuristic method.
∆τ𝑖𝑗
𝑘
=
𝑄
𝐿 𝑘
Formal ANT Algorithm , contd.
26. Ant Colony Optimization (ACO)
• The amount of pheromones on each edge after all M ants have retraced their
own paths is equal to
• Before starting all ants again in a search for the shortest path, pheromone levels
evaporate according to
• Where 0 ≤ ρ ≥ 1 is the coefficient of pheromone evaporation.
• This concludes one iteration of the algorithm. This process is repeated for several
Hundred iterations until satisfactory short path has been found.
∆ 𝑇𝑖𝑗= 𝑘
𝑀
∆τ𝑖𝑗
𝑘
τ𝑖𝑗
𝑡+1
= (1 - ρ) τ𝑖𝑗
𝑡
∆ 𝑇𝑖𝑗
Formal ANT Algorithm , contd.
27. Ant Colony Optimization (ACO)
• Positive Feedback accounts for rapid discovery of good solutions.
• Virtual ants discover and maintain several short paths in addition to the best
one because of the probabilistic edge choice.
• Distributed computation avoids premature convergence.
• The greedy heuristic helps find acceptable solution in the early solution in the
early stages of the search process.
• The collective interaction of a population of agents.
Some inherent advantages
28. Ant Colony Optimization (ACO)
• Slower convergence than other Heuristics.
• Performed poorly for TSP problems larger than 75 cities.
• No centralized processor to guide the AS towards good solutions
Disadvantages in Ant Systems
29. Ant Colony Optimization (ACO)
• ACO is a recently proposed meta-heuristic approach for solving hard
combinatorial optimization problems.
• Artificial ants implement a randomized construction heuristic which makes
probabilistic decisions.
• The cumulated search experience is taken into account by the adaptation of
the pheromone trail.
• ACO Shows great performance with the “ill-structured” problems like network
routing.
• In ACO Local search is extremely important to obtain good results.
Conclusions