Swarm intelligence is a biologically inspired field that studies how social behaviors emerge from the interactions between individuals in a decentralized system. It draws inspiration from natural systems like bird flocking and ant colonies. Particle swarm optimization and ant colony optimization are two popular swarm intelligence algorithms. PSO mimics bird flocking by having particles update their velocities based on their own experience and the swarm's experience. ACO mimics ant foraging behavior by having artificial ants deposit and follow pheromone trails to iteratively find optimal solutions. Both algorithms have been applied to problems like optimization and routing.
This Presentation were Made By BugsBusters team from faculty of Computers and information, Helwan University - Egypt
IMPORTANT NOTE !!!
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Swarm intelligence (SI) is the collective behavior of decentralized, self-organized systems, natural or artificial. The concept is employed in work on artificial intelligence. The expression was introduced by Gerardo Beni and Jing Wang in 1989, in the context of cellular robotic systems.
This Presentation were Made By BugsBusters team from faculty of Computers and information, Helwan University - Egypt
IMPORTANT NOTE !!!
Do not view this online or it will not be compatible Download it to view videos and see original slides :))
Swarm intelligence (SI) is the collective behavior of decentralized, self-organized systems, natural or artificial. The concept is employed in work on artificial intelligence. The expression was introduced by Gerardo Beni and Jing Wang in 1989, in the context of cellular robotic systems.
Particle swarm optimization is a heuristic global optimization method and also an optimization algorithm, which is based on swarm intelligence. It comes from the research on the bird and fish flock movement behavior. The algorithm is widely used and rapidly developed for its easy implementation and few particles required to be tuned. The main idea of the principle of PSO is presented; the advantages and the shortcomings are summarized. At last this paper presents some kinds of improved versions of PSO and research situation, and the future research issues are also given.
محاضرات متقدمة تدرس لطلاب حاسبات بنى سويف السنة الثالثة لتنمية قدراتهم البحثية وهذة الموضوعات تدرس على مستوى الدكتوراة - - نريد تميز طلاب حاسبات ليتميزو فى البحث العلمى -
This presentation provides an introduction to the Ant Colony Optimization topic, it shows the basic idea of ACO, advantages, limitations and the related applications.
A presentation on PSO with videos and animations to illustrate the concept. The ppt throws light on the concept, the algo, the application and comparison of PSO with GA and DE.
Particle swarm optimization is a heuristic global optimization method and also an optimization algorithm, which is based on swarm intelligence. It comes from the research on the bird and fish flock movement behavior. The algorithm is widely used and rapidly developed for its easy implementation and few particles required to be tuned. The main idea of the principle of PSO is presented; the advantages and the shortcomings are summarized. At last this paper presents some kinds of improved versions of PSO and research situation, and the future research issues are also given.
محاضرات متقدمة تدرس لطلاب حاسبات بنى سويف السنة الثالثة لتنمية قدراتهم البحثية وهذة الموضوعات تدرس على مستوى الدكتوراة - - نريد تميز طلاب حاسبات ليتميزو فى البحث العلمى -
This presentation provides an introduction to the Ant Colony Optimization topic, it shows the basic idea of ACO, advantages, limitations and the related applications.
A presentation on PSO with videos and animations to illustrate the concept. The ppt throws light on the concept, the algo, the application and comparison of PSO with GA and DE.
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.
Comparative Study of Ant Colony Optimization And Gang SchedulingIJTET Journal
Abstract— Ant Colony Optimization (ACO) is a well known and rapidly evolving meta-heuristic technique. All optimization problems have already taken advantage of the ACO technique while countless others are on their way. Ant Colony Optimization (ACO) has been used as an effective algorithm in solving the scheduling problem in grid computing. Whereas gang scheduling is a scheduling algorithm that is used to schedule the parallel systems and schedules related threads or processes to run simultaneously on different processors. The threads that are scheduled are belonging to the same process, but they from different processes in some cases, for example when the processes have a producer-consumer relationship, when all processes come from the same MPI program.
SWARM INTELLIGENCE FROM NATURAL TO ARTIFICIAL SYSTEMS: ANT COLONY OPTIMIZATIONFransiskeran
Successful applications coming from biologically inspired algorithm like Ant Colony Optimization (ACO)
based on artificial swarm intelligence which is inspired by the collective behavior of social insects. ACO
has been inspired from natural ants system, their behavior, team coordination, synchronization for the
searching of optimal solution and also maintains information of each ant. At present, ACO has emerged as
a leading metaheuristic technique for the solution of combinatorial optimization problems which can be
used to find shortest path through construction graph. This paper describe about various behavior of ants,
successfully used ACO algorithms, applications and current trends. In recent years, some researchers
have also focused on the application of ACO algorithms to design of wireless communication network,
bioinformatics problem, dynamic problem and multi-objective problem.
A Multi-Objective Ant Colony System Algorithm for Virtual Machine PlacementIJERA Editor
Virtual machine placement is a process of mapping virtual machines to physical machines. The optimal placement is important for improving power efficiency and resource utilization in a cloud computing environment. In this paper, we propose a multi-objective ant colony system algorithm for the virtual machine placement problem. The goal is to efficiently obtain a set of non-dominated solutions (the Pareto set) that simultaneously minimize total resource wastage and power consumption. The proposed algorithm is tested with some instances from the literature. Its solution performance is compared to that of an existing algorithm. The results show that the proposed algorithm is more efficient and effective than the methods we compared it to.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Courier management system project report.pdfKamal Acharya
It is now-a-days very important for the people to send or receive articles like imported furniture, electronic items, gifts, business goods and the like. People depend vastly on different transport systems which mostly use the manual way of receiving and delivering the articles. There is no way to track the articles till they are received and there is no way to let the customer know what happened in transit, once he booked some articles. In such a situation, we need a system which completely computerizes the cargo activities including time to time tracking of the articles sent. This need is fulfilled by Courier Management System software which is online software for the cargo management people that enables them to receive the goods from a source and send them to a required destination and track their status from time to time.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
TECHNICAL TRAINING MANUAL GENERAL FAMILIARIZATION COURSEDuvanRamosGarzon1
AIRCRAFT GENERAL
The Single Aisle is the most advanced family aircraft in service today, with fly-by-wire flight controls.
The A318, A319, A320 and A321 are twin-engine subsonic medium range aircraft.
The family offers a choice of engines
Automobile Management System Project Report.pdfKamal Acharya
The proposed project is developed to manage the automobile in the automobile dealer company. The main module in this project is login, automobile management, customer management, sales, complaints and reports. The first module is the login. The automobile showroom owner should login to the project for usage. The username and password are verified and if it is correct, next form opens. If the username and password are not correct, it shows the error message.
When a customer search for a automobile, if the automobile is available, they will be taken to a page that shows the details of the automobile including automobile name, automobile ID, quantity, price etc. “Automobile Management System” is useful for maintaining automobiles, customers effectively and hence helps for establishing good relation between customer and automobile organization. It contains various customized modules for effectively maintaining automobiles and stock information accurately and safely.
When the automobile is sold to the customer, stock will be reduced automatically. When a new purchase is made, stock will be increased automatically. While selecting automobiles for sale, the proposed software will automatically check for total number of available stock of that particular item, if the total stock of that particular item is less than 5, software will notify the user to purchase the particular item.
Also when the user tries to sale items which are not in stock, the system will prompt the user that the stock is not enough. Customers of this system can search for a automobile; can purchase a automobile easily by selecting fast. On the other hand the stock of automobiles can be maintained perfectly by the automobile shop manager overcoming the drawbacks of existing system.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Quality defects in TMT Bars, Possible causes and Potential Solutions.PrashantGoswami42
Maintaining high-quality standards in the production of TMT bars is crucial for ensuring structural integrity in construction. Addressing common defects through careful monitoring, standardized processes, and advanced technology can significantly improve the quality of TMT bars. Continuous training and adherence to quality control measures will also play a pivotal role in minimizing these defects.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
2. What is an Algorithm?
The word Algorithm means ” A set of rules to be followed in calculations or other problem-solving
operations ” Or ” A procedure for solving a mathematical problem in a finite number of steps that
frequently by recursive operations “.
Therefore Algorithm refers to a sequence of finite steps to solve a particular problem.
Algorithms can be simple and complex depending on what you want to achieve.
3. Conventional methods of computing...
Popular Conventional methods that have been widely used includes.
1) Mathematical optimization algorithms.. (such as Newton's method and gradient descent method that
use derivatives to locate a local minimum)
2) Direct search method (eg.. Simplex method and Nelder-mead method that use a search pattern to
locate optima).
3) Enumerative approaches (such as Dynamic programming)
Limitation
1) Several assumptions about the problem in order to suit the particular method.
2) Flexibility to solve particular problem as it is.
3) May obstruct the possibility of modeling the problem closer to reality.
4) The efficiency of algorithm varies depending on the complexity of the problem.
5) The conventional nonlinear optimization solvers are not applicable for problems with non differentiable &
/or discontinuous function relationship
4. • Bio-inspired computing, short for biologically
inspired computing, is a field of study that loosely
knits together subfields related to the topics of
connectionism, social-behavior and emergence.
• It relies heavily on the fields of biology, computer
science and mathematics. Briefly put, it is the use
of computers to model the living phenomena, and
simultaneously the study of life to improve the
usage of computers.
• Biologically inspired computing is a major subset
of Natural Computation.
7. Swarming –
The Definition
Aggregation of similar animals,
generally cruising in the same
direction
o Termites swarm to build colonies
o Birds swarm to find food
o Bees swarm to reproduce
8. Why do animals swarm?
o To forage better
o To migrate
o As a defense against predators
Social Insects have survived for millions of years.
14. Swarming - Characteristics
Simple rules for each individual
No central control
◦ Decentralized and hence robust
Emergent
◦ Performs complex functions
15. Learn from insects
Computer Systems are getting complicated
Hard to have a master control
Swarm intelligence systems are:
◦ Robust
◦ Relatively simple
16. Swarm Intelligence - Definition
“any attempt to design algorithms or distributed problem-solving devices inspired by the
collective behavior of social insect colonies and other animal societies”
[Bonabeau, Dorigo, Theraulaz: Swarm Intelligence]
Solves optimization problems
20. Particle Swarm Optimization
Particle swarm optimization imitates human or insects social behavior.
Individuals interact with one another while learning from their own
experience, and gradually move towards the goal.
It is easily implemented and has proven both very effective and quick when
applied to a diverse set of optimization problems.
21. Bird flocking is one of the best example of PSO in nature.
One motive of the development of PSO was to model human social behavior.
22. Applications of PSO
Neural networks like Human tumor analysis, Computer numerically controlled milling
optimization;
Ingredient mix optimization;
Pressure vessel (design a container of compressed air, with many constraints).
Basically all the above applications fall in a category of finding the global maxima of a continuous,
discrete, or mixed search space, with multiple local maxima.
23. Algorithm of PSO
Each particle (or agent) evaluates the function to maximize at each point it visits in spaces.
Each agent remembers the best value of the function found so far by it (pbest) and its co-
ordinates.
Secondly, each agent know the globally best position that one member of the flock had found,
and its value (gbest).
24. Algorithm – Phase 1 (1D)
Using the co-ordinates of pbest and gbest, each agent calculates its new velocity as:
vi = vi + c1 x rand() x (pbestxi – presentxi)
+ c2 x rand() x (gbestx – presentxi)
where 0 < rand() <1
presentxi = presentxi + (vi x Δt)
31. Ant Colony Optimization - Biological Inspiration
o Inspired by foraging behavior of ants.
o Ants find shortest path to food source from nest.
o Ants deposit pheromone along traveled path which is used by
other ants to follow the trail.
o This kind of indirect communication via the local environment is
called stigmergy.
o Has adaptability, robustness and redundancy.
32. Foraging behavior of Ants
2 ants start with equal probability of going on either path.
33. Foraging behavior of Ants
The ant on shorter path has a shorter to-and-fro time from it’s nest to the
food.
34. Foraging behavior of Ants
The density of pheromone on the shorter path is higher because of 2 passes by
the ant (as compared to 1 by the other).
36. Foraging behavior of Ants
Over many iterations, more ants begin using the path with higher pheromone,
thereby further reinforcing it.
37. Foraging behavior of Ants
After some time, the shorter path is almost exclusively used.
38. Generic ACO
Formalized into a metaheuristic.
Artificial ants build solutions to an optimization problem and exchange info on their quality vis-
à-vis real ants.
A combinatorial optimization problem reduced to a construction graph.
Ants build partial solutions in each iteration and deposit pheromone on each vertex.
39. Ant Colony Metaheuristic
ConstructAntSolutions: Partial solution extended by adding an edge based on stochastic
and pheromone considerations.
ApplyLocalSearch: problem-specific, used in state-of-art ACO algorithms.
UpdatePheromones: increase pheromone of good solutions, decrease that of bad
solutions (pheromone evaporation).
40. Various Algorithms
First in early 90’s.
Ant System (AS):
◦ First ACO algorithm.
◦ Pheromone updated by all ants in the iteration.
◦ Ants select next vertex by a stochastic function which depends on both pheromone and problem-
specific heuristic nij =
1
dij
41. Various Algorithms - 2
MAX-MIN Ant System (MMAS):
◦ Improves over AS.
◦ Only best ant updates pheromone.
◦ Value of pheromone is bound.
◦ Lbest is length of tour of best ant.
◦ Bounds on pheromone are problem specific.
42. Various Algorithms - 3
Ant Colony System (ACS):
◦ Local pheromone update in addition to offline pheromone update.
◦ By all ants after each construction step only to last edge traversed.
◦ Diversify search by subsequent ants and produce different solutions in an iteration.
◦ Local update:
◦ Offline update:
43. Theoretical Details
Convergence to optimal solutions has been proved.
Can’t predict how quickly optimal results will be found.
Suffer from stagnation and selection bias.
45. Ant like agents for routing
Intuitive to think of ants for routing problem
Aim is to get shortest path
Start as usual
◦ Release a number of ants from source, let the age of ant increases with increase in hops
◦ decide on pheromone trails i.e biasing the entries in routing table in favor of youngest ant
Problem – Ants at an node do not know the path to destiation, can't cahnge table
entry
46. Routing continued ...
Possible Solutions
◦ first get to dest. and then retrace
◦ Needs memory to store the path
◦ And intelligence to revert the path
◦ Leave unique entries on nodes
◦ a lot of entries at every node
Observation – At any intermediate node, ant knows the path to source from that
node.
◦ now leave influence on routing table having entry “route to source via that link”
47. Routing contd ...
Now at any node it has information about shortest path to dest., left by ants from dest.
The ant following shortest path should have maximum influence
A convenient form of pheromone can be inverse of age + constant
The table may get frozen, with one entry almost 1, add some noise f i.e probabilty that
an ant choses purely random path
48. Dealing with congestion
Add a function of degree of congestion of each node to age of an ant
Delay an ant at congested node, this prevents ants from influencing route
table
49. SI - Limitations
Theoretical analysis is difficult, due to sequences of probabilistic choices
Most of the research are experimental
Though convergence in guaranteed, time to convergence is uncertain
50. Scope
Startup !!
◦ Bluetronics, Smartintel
Analytic proof and models of swarm based algorithm remain topics of
ongoing research
List of applications using SI growing fast
◦ Controlling unmanned vehicles.
◦ Satellite Image Classification
◦ Movie effects
51. Conclusion
Provide heuristic to solve difficult problems
Has been applied to wide variety of applications
Can be used in dynamic applications
52. References
Reynolds, C. W. (1987) Flocks, Herds, and Schools: A Distributed Behavioral Model, in Computer Graphics, 21(4)
(SIGGRAPH '87 Conference Proceedings) pages 25-34.
James Kennedy, Russell Eberhart. Particle Swarm Optimization, IEEE Conf. on Neural networks – 1995
www.adaptiveview.com/articles/ ipsop1
M.Dorigo, M.Birattari, T.Stutzle, Ant colony optimization – Artificial Ants as a computational intelligence technique,
IEEE Computational Intelligence Magazine 2006
Ruud Schoonderwoerd, Owen Holland, Janet Bruten - 1996. Ant like agents for load balancing in telecommunication
networks, Adaptive behavior, 5(2).