The first ant colony optimization (ACO) called ant system was inspired through studying of the behaviour of ants in 1991 by Macro Dorigo and co-workers. An ant colony is highly organized, in which one interacting with others through pheromone in perfect harmony. Optimization problems can be solved through simulating ant’s behaviours. Since the first ant system algorithm was proposed, there is a lot of development in ACO. In ant colony system algorithm, local pheromone is used for ants to search optimum result. However, high magnitude of computing is its deficiency and sometimes it is inefficient. Thomas Stützle etal. Introduced MAX-MIN Ant System (MMAS) in 2000. It is one of the best algorithms of ACO. It limits total pheromone in every trip or sub-union to avoid local convergence. However, the limitation of pheromone slows down convergence rate in MMAS.
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.
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.
Abstract: This PDSG workship introduces basic concepts on using Hill Climbing for Local Search. Concepts covered are global and local maximum, shoulder/flat, value functions, local beam search, and stochastic variant.
Level: Fundamental
Requirements: Should have prior familiarity with Graph Search. No prior programming knowledge is required.
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.
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 :))
The cuckoo search algorithm is a recently developed meta-heuristic optimization algorithm, which is suitable for solving optimization problems. Cuckoo search is a nature-inspired metaheuristic algorithm, based on the brood parasitism of some cuckoo species, along with Levy flights random walks
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.
Abstract: This PDSG workship introduces basic concepts on using Hill Climbing for Local Search. Concepts covered are global and local maximum, shoulder/flat, value functions, local beam search, and stochastic variant.
Level: Fundamental
Requirements: Should have prior familiarity with Graph Search. No prior programming knowledge is required.
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.
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 :))
The cuckoo search algorithm is a recently developed meta-heuristic optimization algorithm, which is suitable for solving optimization problems. Cuckoo search is a nature-inspired metaheuristic algorithm, based on the brood parasitism of some cuckoo species, along with Levy flights random walks
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.
The assembly process is one of the most time consuming and expensive manufacturing activities.
Determination of a correct and stable assembly sequence is necessary for automated assembly system. The objective of
the present work is to generate feasible, stable and optimal assembly sequence with minimum assembly time.
Automated assembly has the advantage of greater process capability and scalability. It is faster, more efficient and
precise than any conventional process. Ant Colony Optimization (ACO) method is used for generation of stable
assembly sequence. This method has been applied to a Planetary Gearbox.
Automating Machine Learning - Is it feasible?Manuel Martín
Facing a machine learning problem for the first time can be overwhelming. Hundreds of methods exist for tackling problems such as classification, regression or clustering. Selecting the appropriate method is challenging, specially if no much prior knowledge is known. In addition, most models require to optimise a number of hyperparameters to perform well. Preparing the data for the learning algorithm is also a labour-intensive process that includes cleaning outliers and imperfections, feature selection, data transformation like PCA and more. A workflow connecting preprocessing methods and predictive models is called a multicomponent predictive system (MCPS). This talk introduces the problem of automating the composition and optimisation of MCPSs and also how they can be adapted in changing environments.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Ant Colony System with Saving Heuristic for Capacitated Vehicle Routing Problemijtsrd
The ACO heuristics is a distributed and cooperative search method that imitates the behavior of real ants in its the search for food. The Capacitated Vehicle Routing Problem CVRP is a well known combinatorial optimization problem, which is concerned with the distribution of goods between the depot and customers. This paper will apply the Ant Colony System ACS with Savings heuristic algorithm to solve Capacitated Vehicle Routing Problem. This problem will be solve to determine an optimal distribution plan that meets all the demands at minimum total cost by applying the ACS algorithm. In this paper, we consider that there is a single depot or distribution center that caters to the customer demands at a set of sales points or demand centers using vehicles with known limited capacities. The demand at each of these demand centers is assumed to be constant and known. Due to its limited capacity, the vehicles may need to make several trips from the depot for replenishment. This system will implement the transportation cost of CVRP and can find the minimum cost routes between the depot and the customers by using the Benchmarks datasets. Aye Aye Chaw "Ant Colony System with Saving Heuristic for Capacitated Vehicle Routing Problem" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd27884.pdfPaper URL: https://www.ijtsrd.com/computer-science/artificial-intelligence/27884/ant-colony-system-with-saving-heuristic-for-capacitated-vehicle-routing-problem/aye-aye-chaw
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.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
TEACHING AND LEARNING BASED OPTIMISATIONUday Wankar
Teaching–Learning-Based Optimization (TLBO) seems to be a rising star from amongst a number of metaheuristics with relatively competitive performances. It is reported that it outperforms some of the well-known metaheuristics regarding constrained benchmark functions, constrained mechanical design, and continuous non-linear numerical optimization problems. Such a breakthrough has steered us towards investigating the secrets of TLBO’s dominance. This report’s findings on TLBO qualitatively and quantitatively through code-reviews and experiments, respectively.
It is a selection of best element (with regard to some criteria) from some set of available alternatives. In the simplest case, an optimization problem consist of maximizing or minimizing a real function by choosing input values from within an allowed set and computing the value of function. The classical optimization techniques are useful in finding the optimum solution or unconstrained maxima or minima of continuous and differentiable functions. These are analytical methods and make use of differential calculus in locating the optimum solution. The classical methods have limited scope in practical applications as some of them involve objective functions which are not continuous and un-differentiable. Yet, the study of these classical techniques of optimization form a basis for developing most of the numerical techniques that have evolved into advanced techniques more suitable to today’s practical problems.
The difficulties associated with using mathematical optimization on large-scale engineering problems have contributed to the development of alternative solutions. Linear programming and dynamic programming techniques, for example, often fail (or reach local optimum) in solving NP-hard problems with large number of variables and non-linear objective functions. To overcome these problems, researchers have proposed evolutionary-based algorithms for searching near-optimum solutions to problems.
Evolutionary algorithms (EAs) are stochastic search methods that mimic the metaphor of natural biological evolution and/or the social behaviour of species. Examples include how ants find the shortest route to a source of food and how birds find their destination during migration. The behaviour of such species is guided by learning, adaptation, and evolution. To mimic the efficient behaviour of these species, various researchers have developed computational systems that seek fast and robust solutions to complex optimization problems. The first evolutionary-based technique introduced in the literature was the genetic algorithms (Gas). GAs were developed based on the Darwinian principle of the ‘survival of the fittest’ and the natural process of evolution through reproduction. Based on its demonstrated ability to reach near-optimum solutions to large problems, the GAs technique has been used in many applicationsin science and engineering. Despite their benefits, GAs may require long processing time for a near optimum solution to evolve. Also, not all problems lend themselves well to a solution with GAs.
For three decades, many mathematical programming methods have been developed to solve optimization problems. However, until now, there has not been a single totally efficient and robust method to coverall optimization problems that arise in the different engineering fields.Most engineering application design problems involve the choice of design variable values that better describe the behaviour of a system.At the same time, those results should cover the requirements and specifications imposed by the norms for that system. This last condition leads to predicting what the entrance parameter values should be whose design results comply with the norms and also present good performance, which describes the inverse problem.Generally, in design problems the variables are discreet from the mathematical point of view. However, most mathematical optimization applications are focused and developed for continuous variables. Presently, there are many research articles about optimization methods; the typical ones are based on calculus,numerical methods, and random methods.
The calculus-based methods have been intensely studied and are subdivided in two main classes: 1) the direct search methods find a local maximum moving a function over the relative local gradient directions and 2) the indirect methods usually find the local ends solving a set of non-linear equations, resultant of equating the gradient from the object function to zero, i.e., by means of multidimensional generalization of the notion of the function’s extreme points from elementary calculus given smooth function without restrictions to find a possible maximum which is to be restricted to those points whose slope is zero in all directions. The real world has many discontinuities and noisy spaces, which is why it is not surprising that the methods depending upon the restrictive requirements of continuity and existence of a derivative, are unsuitable for all, but a very limited problem domain. A number of schemes have been applied in many forms and sizes. The idea is quite direct inside a finite search space or a discrete infinite search space, where the algorithms can locate the object function values in each space point one at a time. The simplicity of this kind of algorithm is very attractive when the numbers of possibilities are very small. Nevertheless, these outlines are often inefficient, since they do not complete the requirements of robustness in big or highly-dimensional spaces, making it quite a hard task to find the optimal values. Given the shortcomings of the calculus-based techniques and the numerical ones the random methods have increased their popularity.
Finding an alternative with the most cost effective or highest achievable performance under the given constraints, by maximizing desired factors and minimizing undesired ones. It also mean that it make best use of a situation or resource. In comparison, maximization means trying to attain the highest or maximum result or outcome without regard to cost or expense. Practice of optimization is restricted by the lack of full information, and the lack of time to evaluate what information is available (see bounded reality for details). In computer simulation (modeling) of business problems, optimization is achieved usually by using linear programming techniques of operations research.
The gas turbine is an internal combustion engine that uses air as the working fluid. The engine extracts chemical energy from fuel and converts it to mechanical energy using the gaseous energy of the working fluid (air) to drive the engine and propeller, which, in turn, propel the aeroplane.
The gas turbine is an internal combustion engine that uses air as the working fluid. The engine extracts chemical energy from fuel and converts it to mechanical energy using the gaseous energy of the working fluid (air) to drive the engine and propeller, which, in turn, propel the airplane.
This ppt show the steps to rewind the Brushless motor(BLDC)
If you fly brushless you've probably cooked a motor or two. You also probably know there are many different types of motors. Similar motors when wound differently performs very differently. Whether you've burned the motor up, or just want to alter performance, rewinding is a cheap solution for a patient modeller.
For this tutorial, I will be using Dynam E-Razor 450 Brushless Motor 60P-DYM-0011 (2750Kv). It is a Delta wound 8T (It means 8 turns ) quad wind.
The winding pattern described in this tutorial (called an ABC wind - ABCABCABC as you go around the stator) works for any brushless motor with 9 stator teeth and 6 magnets.
This ppt show the steps to rewind the Brushless motor(BLDC)
If you fly brushless you've probably cooked a motor or two. You also probably know there are many different types of motors. Similar motors when wound differently performs very differently. Whether you've burned the motor up, or just want to alter performance, rewinding is a cheap solution for a patient modeller.
For this tutorial, I will be using Dynam E-Razor 450 Brushless Motor 60P-DYM-0011 (2750Kv). It is a Delta wound 8T (It means 8 turns ) quad wind.
The winding pattern described in this tutorial (called an ABC wind - ABCABCABC as you go around the stator) works for any brushless motor with 9 stator teeth and 6 magnets.
Our project is a persistence of vision display (POV) that spins 360 degrees horizontally. The purpose of our POV display project is to create a small apparatus that will create a visual using only a small number of LEDs as it spins in a circle. When the LEDs rotate several times around a point in less than a second, the human eye reaches its limit of motion perception and creates an illusion of a continuous image. Therefore, our POV display demonstrates this phenomenon by creating a visual as the LEDs spin rapidly in a circle and the person watching will see one continuous image.
Arm cortex (lpc 2148) based motor speedUday Wankar
The project is designed to control the speed of a DC and AC motor using an
ARM7 LPC2148 processor. The speed of motor is directly proportional to the voltage
applied across its terminals. Hence, if voltage across motor terminal is varied, then
speed can also be varied. This project uses the above principle to control the speed of
the motor by varying the duty cycle of the pulses applied to it, popularly known as
PWM control. The project uses input button interfaced to the processor, which are
used to control the speed of motor. Pulse Width Modulation is generated at the output
by the microcontroller as per the program. The program is written in Embedded C.
The average voltage given or the average current flowing through the motor
will change depending on the duty cycle, ON and OFF time of the pulses, so the speed
of the motor will change. A motor driver IC is interfaced to the ARM7 LPC2148
processor board for receiving PWM signals and delivering desired output for speed
control. Further the project can be enhanced by using power electronic devices such
as IGBTs to achieve speed control higher capacity industrial motors.
Arm Processor Based Speed Control Of BLDC MotorUday Wankar
The project is designed to control the speed of a DC motor using an ARM series processor. The speed of DC motor is directly proportional to the voltage applied across its terminals. Hence, if voltage across motor terminal is varied, then speed can also be varied. This project uses the above principle to control the speed of the motor by varying the duty cycle of the pulse applied to it (popularly known as PWM control). The project uses input button interfaced to the processor, which are used to control the speed of motor. PWM (Pulse Width Modulation) is generated at the output by the microcontroller as per the program. The program is written in Embedded C. The average voltage given or the average current flowing through the motor will change depending on the duty cycle (ON and OFF time of the pulses), so the speed of the motor will change. A motor driver IC is interfaced to the STM32 board for receiving PWM signals and delivering desired output for speed control of a small DC motor. Further the project can be enhanced by using power electronic devices such as IGBTs to achieve speed control higher capacity industrial motors.
Arm cortex ( lpc 2148 ) based motor speed control Uday Wankar
The project is designed to control the speed of a DC and AC motor using an ARM7 LPC2148 processor. The speed of motor is directly proportional to the voltage applied across its terminals. Hence, if voltage across motor terminal is varied, then speed can also be varied. This project uses the above principle to control the speed of the motor by varying the duty cycle of the pulses applied to it, popularly known as PWM control. The project uses input button interfaced to the processor, which are used to control the speed of motor. Pulse Width Modulation is generated at the output by the microcontroller as per the program. The program is written in Embedded C.
The average voltage given or the average current flowing through the motor will change depending on the duty cycle, ON and OFF time of the pulses, so the speed of the motor will change. A motor driver IC is interfaced to the ARM7 LPC2148 processor board for receiving PWM signals and delivering desired output for speed control. Further the project can be enhanced by using power electronic devices such as IGBTs to achieve speed control higher capacity industrial motors.
Power Quality is a combination of Voltage profile, Frequency profile, Harmonics contain and reliability of power supply.
The Power Quality is defined as the degree to which the power supply approaches the ideal case of stable, uninterrupted, zero distortion and disturbance free supply.
MSEB was set up in 1960 to generate, transmit and distribute power to all consumers in
Maharashtra excluding Mumbai. MSEB was the largest SEB in the country. The generation
capacity of MSEB has grown from 760 MW in 1960-61 to 9771 MW in 2001-02. The customer
base has grown from 1,07,833 in 1960-61 to 1,40,09,089 in 2001-02.
C.S.T.P.S in contribution much in field of production of electricity. It is not only number
one thermal power station in Asia but also has occupied specific position on the international
map.
The first set was commission on August 1983 & was dedicated to nation by then PM
(late) Mrs. Indira Gandhi & second set commission on July 1984. The third & fourth units of
CSTPS under stage 2 were commissioned on the 3rd May 1985 & 8th March 1986 respectively.
The units 5 & 6 were commissioned on the 22nd March 1991 & 11th March 1992 respectively one
more units of 500MW was added to the CSTPS on making its generation to 2340 MW &
making “C.S.T.P.S.” as the giant in Power Generation of CSTPS.
With the development of industry and
agriculture, a great amount of energy such as coal, oil
and gas has been consumed in the world. Extensive
use of these fossil energies deteriorates a series of
problems like energy crisis, environmental pollution
and so on. Everybody knows that the fossil energy
reserves are finite, some day it will be exhausted.
It is possible that the world will face a
global energy crisis due to a decline in the
availability of cheap oil and recommendations to a
decreasing dependency on fossil fuel. This has led to
increasing interest in alternate power/fuel research
such as fuel cell technology, hydrogen fuel, biodiesel,
Karrick process, solar energy, geothermal energy,
tidal energy and wind. Today, solar energy and wind
energy have significantly alternated fossil fuel with
big ecological problems.
With the development of the science and
technology, power generation using solar energy and
wind power is gradually known by more and more
people. And it is widespread used in many developed
countries. The merits of the solar and wind power
generation are very obvious-infinite and nonpolluting.
The raw materials of the solar and wind
power generation derived from nature, and wind
power generation can work twenty-four hours a day,
solar power generation only works by daylight. In
addition, this kind of power generation has no
exhaust emission and there is no influence to the
nature. But it also has some shortcomings. Because
of the imperfect of the technology, equipment of the
solar and wind power generation is very expensive.
By far, it cannot be widely used.
In addition, solar and wind power
generation system affected by the changing of the
weather very much, so it has obvious defects in
reliability compared with fossil fuel, and it is difficult
to make it fit for practical use the lack of economical
efficiency .Because of these problems it needs to
increase the reliability of energy supply by
developing a system which interacts Solar and wind
energy. This kind of system is usually called windsolar
hybrid power generation system significantly
Hybrid power generation by and solar –windUday Wankar
With the development of industry and
agriculture, a great amount of energy such as coal, oil
and gas has been consumed in the world. Extensive
use of these fossil energies deteriorates a series of
problems like energy crisis, environmental pollution
and so on. Everybody knows that the fossil energy
reserves are finite, some day it will be exhausted.
It is possible that the world will face a
global energy crisis due to a decline in the
availability of cheap oil and recommendations to a
decreasing dependency on fossil fuel. This has led to
increasing interest in alternate power/fuel research
such as fuel cell technology, hydrogen fuel, biodiesel,
Karrick process, solar energy, geothermal energy,
tidal energy and wind. Today, solar energy and wind
energy have significantly alternated fossil fuel with
big ecological problems.
With the development of the science and
technology, power generation using solar energy and
wind power is gradually known by more and more
people. And it is widespread used in many developed
countries. The merits of the solar and wind power
generation are very obvious-infinite and nonpolluting.
The raw materials of the solar and wind
power generation derived from nature, and wind
power generation can work twenty-four hours a day,
solar power generation only works by daylight. In
addition, this kind of power generation has no
exhaust emission and there is no influence to the
nature. But it also has some shortcomings. Because
of the imperfect of the technology, equipment of the
solar and wind power generation is very expensive.
By far, it cannot be widely used.
In addition, solar and wind power
generation system affected by the changing of the
weather very much, so it has obvious defects in
reliability compared with fossil fuel, and it is difficult
to make it fit for practical use the lack of economical
efficiency .Because of these problems it needs to
increase the reliability of energy supply by
developing a system which interacts Solar and wind
energy. This kind of system is usually called windsolar
hybrid power generation system significantly
This paper presents Grid Solver Bot which is a self-driven vehicle capable of localizing itself in a grid and planning a path between two nodes. It can avoid particular nodes and plan path between two allowed nodes. Breadth-first search & Dijkstra's Algorithm have been used for finding the path between two allowed nodes. The searching of a block over grid is easier when the rows and columns i.e. m* n of a grid is fixed. But when the grid is dynamic or changes over time than in such situation we require a generalized algorithm for traversing over a grid. In these paper we develop an approach for searching an object and also able to avoid an obstacle which was placed in a junction (meeting point of row and column). Here, we use different algorithms like Dijkistra’s, Best first search and A star algorithms. We develop an approach to find the block with minimum shortest path with the help of priority based algorithm. The vehicle is also capable of transferring blocks from one node to another. In fact, this vehicle is a prototype of a self-driven vehicle capable of transporting passengers and it can also be used in industries to transfer different items from one place to another.
Ballarpur Industries Limited (BILT) is a flagship of the US$ 4 bnAvantha Group and India's
largest manufacturer of writing and printing (W&P) paper. The current chairman of the
company is GautamThapar, who succeeded his late uncle L.M. Thapar.
BILT's subsidiaries include Sabah Forest Industries (SFI), Malaysia's largest pulp and paper
company, and BILT Tree Tech Limited (BTTL), which runs BILT's farm forestry programme
in several states in India.
BILT has six manufacturing units across India, which give the company geographic coverage
over most of the domestic market. BILT has a dominant share of the high-end coated
paper segment in India. The company accounts for over 50% of the coated wood-free paper
market, an impressive 85% of the bond paper market and nearly 45% of the hi-bright
Maplitho market, besides being India's largest exporter of coated paper.
BILT’s acquisition of SFI in 2007 was a watershed event – it was the first overseas acquisition
by an Indian paper company. This acquisition transformed BILT into a major regional
player, and elevated the company's ranking among the global top 100.
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.
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
2. CONTENT
defination of optimization
ACO concept
ACO system
ACO system cont.
ANT foraging
Implementation
Applications
Advantages & Disadvantages
Sources
conclusions
References
3. What is Optimization?
Procedure to make a system or design as
effective, especially the mathematical
techniques involved. ( Meta-Heuristics)
Finding Best Solution
Minimal Cost (Design)
Minimal Error (Parameter Calibration)
Maximal Profit (Management)
Maximal Utility (Economics)
4. 4
ACO Concept
Ants (blind) navigate from nest to food source
Shortest path is discovered via pheromone trails
First ant moves at random
pheromone is deposited on path
ants detect lead ant’s path, inclined to follow
more pheromone on path increases probability of path
being followed
5. 5
ACO System
Virtual “trail” accumulated on path segments
Starting node selected at random
Path selected at random
based on amount of “trail” present on possible paths
from starting node
higher probability for paths with more “trail”
Ant reaches next node, selects next path
Continues until reaches starting node
Finished “tour” is a solution
6. 6
ACO System, cont.
A completed tour is analyzed for optimality
“Trail” amount adjusted to favor better solutions
better solutions receive more trail
worse solutions receive less trail
higher probability of ant selecting path that is part of a
better-performing tour
New cycle is performed
Repeated until most ants select the same tour on
every cycle (convergence to solution)
11. 11
Implementation
Can be used for both Static and Dynamic
Combinatorial optimization problems
Convergence is guaranteed, although the
speed is unknown
Value
Solution
14. 14
Applications
Other
Shortest Common Sequence
Constraint Satisfaction
2D-HP protein folding
Bin Packing
Machine Learning
Classification Rules
Bayesian networks
Fuzzy systems
Network Routing
Connection oriented network routing
Connection network routing
Optical network routing
15. 15
Advantages and Disadvantages,
cont.
Can be used in dynamic applications (adapts to
changes such as new distances, etc.)
Has been applied to a wide variety of applications
As with GAs, good choice for constrained discrete
problems (not a gradient-based algorithm)
16. 16
Sources
Dorigo, Marco and Stützle, Thomas. (2004) Ant Colony Optimization,
Cambridge, MA: The MIT Press.
Dorigo, Marco, Gambardella, Luca M., Middendorf, Martin. (2002)
“Guest Editorial,” IEEE Transactions on Evolutionary Computation,
6(4): 317-320.
Thompson, Jonathan, “Ant Colony Optimization.”
http://www.orsoc.org.uk/region/regional/swords/swords.ppt, accessed
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17. 17
Advantages and Disadvantages
For TSPs (Traveling Salesman Problem), relatively efficient
for a small number of nodes, TSPs can be solved by
exhaustive search
for a large number of nodes, TSPs are very computationally
difficult to solve (NP-hard) – exponential time to
convergence
Performs better against other global optimization techniques
for TSP (neural net, genetic algorithms, simulated annealing)
Compared to GAs (Genetic Algorithms):
retains memory of entire colony instead of previous
generation only
less affected by poor initial solutions (due to combination of
random path selection and colony memory)
18. Estimation and simulation, end
users; field work – tracer studies,
pressure tests, case studies;
contaminant and water security –
detection, source identification,
response; network vulnerability –
security assessments, network
reliability,
CONCLUSION