Multi Objective Optimization Using Ant Colony Algorithm

Multi Objective Optimization Of Environmental And Energy...
Research Proposal
Research Grant: Bi Nationally Supervised Doctoral Degree
PhD (Operations Research)
Multiobjective optimization in environmental, economical and energy planning problems
Mohammad Asim Nomani
PhD Student
Department of Statistics & Operations Research
Aligarh Muslim University, Aligarh, India
Mob: +91–9528072689
Email: nomani.aasim@gmail.com
Multi–objective optimization in environmental and energy planning
Energy policy, environmental planning and economic development play a key role in sustainable
development. Economic growth is closely linked to energy consumption since higher level of
energy consumption leads to higher economic growth. Energy consumption is also closely linked
with environmental pollutant. Environmental decisions are often complex and multifaceted and
involve many different stakeholders with different priorities on objectives. Energy consumption,
environmental planning and economic growth have been the subject of considerable academic
research over the past few decades.
During the past decades different mathematical models for energy resources allocation and
environmental planning have been developed and studied for both quantitative and qualitative
criteria. Policymakers deal with energy–related issues and how they interact or affect economic
growth and environmental quality. There are some factors in resources and environmental systems
that need to be considered by planners and decision–makers such as legislation,
... Get more on HelpWriting.net ...

Ant-Colony Analysis Paper
In this paper we will find solution using ant colony optimization. This process is based on
probability technique to find results of computational problem. It is a set of software agents called
artificial ants to find solution to a given problem. When ACO is applied then problem is changed
into the problem of finding the best way on the weighted graph. The artificial ants will increasingly
build the solution path via traversing through the whole graph. The solution is biased by the
pheromone model that includes the set of parameters linked with graph components whose values
are changed at the runtime by ants. Travelling Salesman problem was the first problem on which ant
colony optimization technique were applied. The behaviour of artificial ants is same as real ants. Ant
deposit a substance on the ground called pheromone while walking from the ant colony to the food
and at backtracking also. At the time of getting ... Show more content on Helpwriting.net ...
Path generation using ant colony optimization
5. Selection of edge and revise pheromone
Data Collection: This is the initial process to find out the optimal COCOMO coefficient using ant
colony optimization to assemble the dataset. Omitting some values from the dataset will probably
lose some data so our data set must be large enough to hold relevant values even after deletion.
Dataset of COCOMO 81 is chosen as the dataset. It includes 15 cost factors, 63data points, actual
effort and actual size.
Data Cleaning: The dataset consist of set project number and effort multipliers which further is
segmented into a set of 15 parameters, development effort and line of code. Relevant information
will be fetched from the initial dataset and dataset will be converted into a subset which will help us
to get relevant results. Data Analyzing: Analysis of data includes the removal of the outliers. These
are the experimental error which will leads to unsatisfactory results. It will deviate the actual result
from the expected one so they must be

Muscle Redundancy
Muscle redundancy is an issue of having more muscles than the mechanical degree of freedom of
the joint. Due to the muscle redundancy, a specific task can be performed by infinitely many
different relative contribution of individual muscles. Moreover, some muscles are bi–articular joint
muscle, which means they span more than one joint (e.g. gastrocnemius muscle). All together
muscle redundancy generates a very complicated dynamic system to solve. For such kind of system,
the resultant joint moment cannot be distributed directly to each muscle to find the individual
muscle forces.
Muscle redundancy has long been a central problem in computational biomechanics. Some
researchers have used the static optimization methods to solve this issue. In

Using Lp / Ip Hybrid Method For Time Cost Tradeoff
Using LP/IP Hybrid method for time–cost tradeoff
a. Suggested method
Obtaining a good and nearly optimal solution with a reasonable amount of computational effort is
the major motivating factor for this method. Integer programming can find the exact optimal
solution, but it is computationally intensive.
The LP/IP hybrid method is a hybrid approach uses:
(1) Linear programming to create a lower bound of the lowest direct cost curve efficiently; and (2)
Integer programming to find the exact solutions.
Following describe the formulation of linear and integer programming models. Examples are given
to demonstrate how to formulate the mathematical models. Linear programming algorithms, such as
simplex method, can then be used to find the optimal solutions. Much commercially available linear
programming software, such as Lindo, can perform the task very efficiently. The formulation of the
objective function and constraints for linear programming, however, is time–consuming and prone
to error. From the writers ' experience, formulating the objective function and constraints rarely
succeeds without several revisions. For large CPM networks, the effort to check and verify the
formulation could be phenomenal. The convex hull method in conjunction with linear programming
establishes the lower bounds for time–cost relationships of a project. These lower bounds give
construction planners a general idea of the project time–cost relationship. From these lower bounds,
construction

Facial Recognition Personal Statement
I am currently a senior majoring in Computer Science and Technology at the Communication
University of China. I have attained a firm foundation in computer science and have already taken
my first step towards research on artificial intelligence, which proves my commitment to this
subject. I am applying to the Master of Science Program in Computer Science at the University of
California, San Diego to continue my studies.
My passion lies in Artificial Intelligence, stemming from my belief that it will be extremely valuable
to the future of mankind. Primarily, my research interests are in Computer Vision, Robotics,
Machine Learning, Cognitive Science, and the interdisciplinary application of these technologies.
My background in Artificial Intelligence ... Show more content on Helpwriting.net ...
My grades significantly improve every year and my GPA during my junior year is nearly 4.0.
Serving as evidence of my academic aptitude, I also have a hybrid background in computer science
and art, with a rich experience in working with videos and images. I have worked at a television
station, held part–time jobs as a photography assistant, and even minored in Television Editing and
Directing. Because of these experiences, I am proficient in using various kinds of graphic and video
editing software. My unique background combining art and technology would undoubtedly help me
contribute creative and original views in problem–solving situations. With my rich research
background, problem solving ability, talent for implementing methods, and my genuine enthusiasm
and curiosity, I believe that I am the perfect candidate for your MS program in Computer Science.
The University of California, San Diego is the top–ranked engineering school in the US, and I am
convinced that it could provide me with a valuable graduate experience that would benefit the rest
of my life. I am self–confident and assured that I will meet all of the expectations, and I sincerely
hope UC San Diego would provide me with this honorable

Software 520 : Differential Evolution Essay
Intro:
Hi, my name is blank and the project I have been working on this year for computing 520 is
differential evolution, DE, on the cloud, under the supervision of blank.
Parallel programming, the utilisation of many small tasks to complete a larger one, has become far
more prevalent in recent times as problems call for systems with higher performance, faster turnover
times, easy access, and lower costs. While this has previously been cost–prohibitive, given that one
would have had to purchase a large number of physical machines to work on, the development of
cloud computing systems has largely answered this call, providing resources and computing power
as a service to users, rather than a product. The addition of hardware virtualisation has further
increased the availability of massively–parallel collections of computers as flexible networked
platforms for computing large–scale problems.
Differential Evolution, or DE, is a cost minimisation method that utilises various evolutionary
algorithm concepts, but can also handle non–differentiable, nonlinear, and multimodal objective
functions that standard evolutionary algorithms cannot. Experiments have shown that DE shows
good convergence properties and outperforms other EA's, converging faster and with more certainty
than many other popular global optimization methods.
DE provides a general optimization function that converges on an optimal set of parameter values
according to some objective function. This is a valuable

3.4Experiments & Results :-. A.Benchmark Functions. The
3.4 Experiments & results :– A. Benchmark Functions The most challenging issue in validation of
an Evolutionary Multi–objective Optimization (EMOO) algorithm is to identify the right benchmark
functions with diverse characteristics such as multi–modality, deception, isolation and particularly
location of true Pareto–optimal front in the surface to resemble complicated real life problems.
Traditional benchmark functions [1], [2] usually have the global optimum lying either in the centre
of the search range or on the bounds. Naturally, these benchmark functions are inadequate to
exhaustively test the performance of a MOO algorithm. In order to overcome the above problem, a
set of recommended benchmark functions [4] was proposed in the ... Show more content on
Helpwriting.net ...
Here two repositories are maintained in addition to the search population. One contains a single
local best for each member of the swarm and the second one is the external archive [7]. This archive
uses the method from [8] to separate the objective function space into a number of hypercubes (an
adaptive grid) to generate well–distributed Pareto fronts [9]. Those hypercubes containing more than
one particle are assigned a fitness score equal to the result of dividing 10 by the number of the
resident particles in that hypercube [6]. Thus a more densely populated hypercube is given a lower
score. Next the primary population uses its local best and global best particle positions (from the
external archive) to update their velocities. The global best is selected by first choosing a hypercube
(according to its score) using the roulette–wheel selection and then opting for a particle randomly
from such hypercube. After that mutation operators are used to enhance the exploratory capabilities
of the swarm. 2) Non–dominated Sorting Genetic Algorithm–II (NSGA–II) Non–dominated Sorting
Genetic Algorithm–II (NSGA–II) starts with a parent population set PG of randomly initialized
solutions of size. Then an iterative process begins, where genetic operations like tournament
selection, crossover and mutation are done on the parent set to obtain the child population QG also
of size

Solving Optimization Problems Involving Polynomial
Blendeman – 2C
Expectation:
2.4 solve optimization problems involving polynomial, simple rational, and exponential functions
drawn from a variety of applications, including those arising from real–world situations.
2.5 solve problems arising from real–world applications by applying a mathematical model and the
concepts and procedures associated with the derivative to determine mathematical results, and
interpret and communicate the results.
Concept:
For these expectations students need to take their prior knowledge of derivatives and apply that
knowledge to real world application problems. Students may be faced with a problem and then have
to decode what that problem is asking them to do. From the information that they are given they
would have to create and apply a mathematical model that will allow them to solve the problem.
Example: A farmer has 2400 ft of fencing and wants to fence off a rectangular field that borders a
straight river. He needs no fencing along the river. What are the dimensions of the field that has the
largest area?
This example gives students an idea of how the concepts that they are learning in the course can be
applied to real world situations. The problem does not provide the students with the needed
mathematical model, but gives them all the needed information to create a model that will help them
solve the problems. Students have to recognize that this is an optimization question dealing with
maximizing an area.
Struggling Learners:

The Multi Agent Optimization Systems Essay
Although the multi–agent optimization systems is not new, its application and the framework
development to deal with large scale process system engineering problems has not been dealt.
MAOP framework is an optimization algorithm formulated by a group of algorithmic agents in a
systematic way to solve large–scale process system engineering problems. In MAOP framework,
aAn agent is formulated in the MAOP framework is formed by combining the input and output
memory of the agent, the communication protocol between the agent and the global sharing
memory, and the agent algorithmic procedure. an algorithmic procedure, a communication protocol
between the algorithmic procedure and the global information sharing environment, the algorithmic
procedure specific initialization and output retrieving methods. Therefore, an agent In this context,
an agent can be defined asis a distinct, autonomous software entity that is capable of observing and
altering its environment neighborhood. An agent evaluates a given task that contributes directly or
indirectly to the advancement of it's surrounding Siirola et al (2003)5. Algorithmic agents are
combined into a cohesive system where the individual agents interact through the global
information sharing environment. The MAOP framework exhibits both the aggregate properties of
the individual agents, and superior properties resulting from the interactions among the individual
agents. In this nature inspired MAOP platform, the overall behavior is not

Advantages And Limitations Of Genetic Algorithm
1. Introduction
The most popular technique in evolutionary computation research has been the genetic algorithm. In
the traditional genetic algorithm, the representation used is a fixed–length bit string. Each position
in the string is assumed to represent a particular feature of an individual, and the value stored in that
position represents how that feature is expressed in the solution. Usually, the string is "evaluated as
a collection of structural features of a solution that have little or no interactions". The analogy may
be drawn directly to genes in biological organisms. Each gene represents an entity that is
structurally independent of other genes. The main reproduction operator used is bit–string crossover,
in which two strings are used as parents and new individuals are formed by swapping a ... Show
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Advantages and Limitations of Genetic Algorithms
The advantages of genetic algorithm includes:
1. Parallelism 2. Liability
3. Solution space is wider
4. The fitness landscape is complex
5. Easy to discover global optimum 6. The problem has multi objective function
7. Only uses function evaluations.
8. Easily modified for different problems.
9. Handles noisy functions well.
10. Handles large, poorly understood search spaces easily
11. Good for multi–modal problems Returns a suite of solutions.
12. Very robust to difficulties in the evaluation of the objective function.
The limitation of genetic algorithm includes:
1. The problem of identifying fitness function 2. Definition of representation for the problem 3.
Premature convergence occurs 4. The problem of choosing the various parameters like the size of
the population, mutation rate, cross over rate, the selection method and its strength.
5. Cannot use gradients.
6. Cannot easily incorporate problem specific information
7. Not good at identifying local optima
8. No effective terminator.
9. Not effective for smooth unimodal functions 10. Needs to be coupled with a local search

A Hydraulic Crane : The Present Work Is Carried Out On...
1.1 Telescopic crane: The present work is carried out on telescopic crane. A telescopic hydraulic
crane has a boom that consists of a number of tubes fitted one inside the other. A hydraulic or other
powered mechanism extends or retracts the tubes to increase or decrease the total length of the
boom. It uses one or more simple machines to create mechanical advantage and thus moves loads
beyond the normal capability of human. These types of booms are often used for short term
construction projects rescue jobs, lifting boats in and out of the water, etc. The relative compactness
of telescopic booms makes them adaptable for many mobile applications. Boom play objective role
in the load lifting operation and the maximum direct effect of the stress is initializing from it and
effects to another attached assemblies of crane. Sometimes this crane is truck mounted to travel on
highway and eliminating the need of the special transportation for crane. The telescopic boom is
composed of a series of rectangular shaped symmetrically cross–sectional segments which are fitted
with one inside the other.The boom base section is the largest segment while the boom tip section is
the smallest. In between there are six sections exist in the telescopic crane boom. There are different
types of Telescopic booms which may be pinned boom, full powered boom, or a combination of
both pinned boom and full powered boom. A pinned boom is one in which sections are pinned in the
extended or retracted

Project Proposal ( Option 3 )
Project Proposal (option 3)
Name: Yao Yang UNI: yy2641
The common theme my project will be focuses on is K–means clustering.
The k–means clustering problem can be described as following:
Given a set of n data points in d–dimensional space R^d and an integer k. Find a set of k
points/centers in R^d, such that the mean squared distance from each data point to its nearest centre
(one of the k centers) is minimized.
Paper Summaries:
A local search approximation algorithm for k–means clustering (2004)
The paper considered whether there exists a simple and practical approximation algorithm for k–
means clustering.
It brings up the classical tradeoff between run time and approximation factor.
A local improvement heuristic based on swapping centers in and out that yields a (9 + ϵ)–
approximation algorithm is presented. The paper also shows that any approach based on performing
a fixed number of swaps achieves an approximation factor of at least (9–ϵ) in all sufficiently high
dimensions by providing example.
In summary, the approximation factor is almost tight for algorithms based on methods that perform
fixed number swaps as shown by the paper.
The effectiveness of Lloyd–type methods for the k–means problem (2006)
This paper show that if the data satisfies a natural condition on separation/clusterability, then a near–
optimal clustering result can be expected as various Lloyd–style methods would have great
performance in such setting.
The major algorithmic contribution from

Why Do We Trust Google Maps Of Give Us The Best Route?
Why do we trust Google Maps to give us the best route?
Google Maps provides an interesting example of a complex optimization application that the user
understood easily and implemented without requiring optimization expertise.
The user simply provides an origin and destination before requesting an optimal route. Google Maps
identifies this optimal route based on the fastest travel times, up–to–date traffic and road closure
information, and user define constraints (e.g. avoid highways and tolls). Google displays the result
without revealing details of their sophisticated data collection, model representation, and
optimization routine. At this point and at various points during their route, the user makes a choice
either to follow the path that Google suggests or to use their favorite shortcut.
So, what make the user follow the suggested path instead of their established shortcut? Presumably,
the user is motivated by a desire to get to their destination as quickly as possible. The burden is on
Google to establish that their route is the fastest. Google Maps is able to accomplish this in a
number of ways. Below, I have listed several ways that we could translate to our problem
1) Displaying relevant route information. Google Maps puts a large amount of data at the user's
fingertips: road maps, accidents, road closures, travel distances, and alternative solutions. The
current traffic conditions are reported and historic conditions are available

The Biofuel And Biomass Industry
Abstract
The biofuel and biomass industry has become potentially more beneficial over the last few decades.
They have considerably reduced the usage of fossil fuels. As the non–renewable energy is being
replaced by the renewable energy, new initiatives are proposed for the continuous development of
supply chain network for biofuel energy. The main aim is to determine the optimal model of supply
chain for the biofuel industry, operations of biofuel supply chain, and also design a reliable supply
chain network for the biofuel and biomass industries. Multiple papers have been discussed in
considering various challenges present in the biofuel production market. The key objective of the
paper is to maximize the profit, study the changes in ... Show more content on Helpwriting.net ...
Keywords: Mixed integer programming, supply chain, biofuels, biomass, CyberGIS
Introduction Renewable resources play a vital role in supporting the environment by balancing the
ecosystem. The production of biomass and biofuel over the last few decades has increased due to its
sustainability over the fossil fuels. The biomass and the biofuel supply chain production have
rapidly increased all over the world to reduce the environmental impacts caused by the extinction of
non–renewable energy. This paper discusses about the production of biofuel and biomass supply
chain. The below table includes the methods, objective function and problems studied in each
article.
Article Method Objective Function Problem Studied
1 MILP Maximize the net present value [1] Optimization of forest residue
2 MILP
Monte Carlo [2] Maximization of net present value Optimization of waste cooking oils
3 MILP
Benders Decomposition Algorithm [3] Minimize the system costs Network design, operations,
environmental issues
4 MILP Maximize the total economic value [4] Development of bio–products from energy crops
5 MILP Minimize costs of production Expand production of biomass from agricultural & animal
waste
6 MINLP
Genetic algorithm [6] Minimize the total costs Construction of new facilities and transportation
network
7 MILP
Cyber–GIS [7] BioScope Linear combination of all the costs [7] Designing a biomass supply chain

decision making process
8 MILP
Stochastic [8] programming Maximize

Software Optimization Methods Of Changing A Software System
HW 4
Software Optimization Techniques
Software optimization is process of changing a software system to enable some aspect of the
process to work more efficiently using less memory storage and less power.
Profiling and timing code execution:
We need to identify portions of code that run frequently which are called hotspots and make these
identified hotspots run faster.
In Profiling the first step is to understand the code in terms of its computations and requests.
Next we need to identify any bottlenecks that may disrupt the performance.
Third we need to set objectives.
Finally we need to improve the performance cycles.
Using platform specific features:
We need to keep in mind the cache coherency, pipelining and branch guessing and side ... Show
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More the requirements implies slower network solution.
In order to do this we need to consolidate the CSS and the JS files, Embedding CSS images into
CSS which reduces extra space, load images only as they scroll into our view.
Next we need to reduce the bytes which also leads to slower network solutions.
For this we need to enable zip compressions, compress images fully and resizing the images as per
the screen size.
Slower javascript leads to weaker CPU:
For this we need to make sure that javaScript is async to the page load.
Asynch avoids delay.
We need to also remove the unused codes.
Numerical methods:
Linear programming:
The objective function is a real–valued function which is defined on a polyhedron. Main function is
finding a point in the polyhedron that has the smallest or the largest values if and when a point like
that exists.

Quadratic programming:
QP is minimizing/maximizing a quadratic function which consists of variables that are subject to LC
on these very variables.
Stochastic programming:
This is mainly used for modeling problems that have uncertainity. Almost all of the Real world
problems include unknown parameters(atlleast few).
Advanced optimization:
Hill climbing:
Hill climbing is used for reaching the end state from the first (start) node. Decision of the next nodes
from the starting nodes, can be done by various algorithms.
There are two variants of hill climbing which are simple hill climbing

Economic Dispatch : An Optimization Problem For Economic...
Economic dispatch(ED) is basically an optimization problem for economic scheduling of power
generating units to meet the forecasted load demand while satisfying all operational constraints [1].
As practical ED is a complex constrained optimization problem, its solution requires robust
optimization methods. An extensive study on has been carried out by researchers on small /medium/
large dimension problems related with single area till date [2]–[4]. The ED problem aims to
determine the optimum powers for the generating units so that the generating cost for the entire
system is minimum, when the power balance conditions and the generating units restrictions are
met. The interconnected power system which contains multiple areas ... Show more content on
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The solution of large scale MAED problem with the wind integration using backtracking search
(BSA) algorithm presented in [12]. Each algorithm has its own advantage. But the key point
associated with MFO algorithms which make them popular for solution of complex constrained
problem in comparison to conventional approach are there is 2 no restriction on the shape of the cost
curves and also heuristic methods do not always guarantee discovering the global optimal solution
in finite time, the often provide a fast and reasonable solution.
. Many researches in the past decade has presented the solutions of various complex constrained
MAED problems using nature–inspired algorithm, few of these are summarized as follows;
Streiffert D et al. [14] have proposed a new method for salving the Multi–Area Economic Dispatch
(MAED) problem with tie–line constraints. This formulation extends the traditional economic
dispatch methods used in study applications such as Unit Commitment to include area demand
constraints, area reserve constraints, and tie–line capacity constraints between the modeled areas.
The MAED is formulated as a capacitated nonlinear network flow problem which is solved using a
high–speed network–flow code. The MAED determines the amount of power that can be
economically generated in one area and transferred to another area to displace

Notes On English Word Arabic
documentclass[11pt,letterpaper]{article} usepackage[english]{babel} usepackage{amsmath}
usepackage{amsthm} usepackage{multirow} usepackage{graphicx} usepackage{fullpage}
usepackage{amsfonts} usepackage{hyperref} usepackage{url} usepackage[affil–it]{authblk}
usepackage{float} usepackage[para]{threeparttable} usepackage{natbib} usepackage{booktabs}
usepackage[onehalfspacing]{setspace} usepackage{enumerate} usepackage{mathtools}
usepackage{relsize} usepackage[table,xcdraw]{xcolor} hypersetup{colorlinks=true, linkcolor=blue,
citecolor=blue, filecolor=blue, urlcolor=blue} ewtheorem{Def}{Definition} ewtheorem{remark}
{Remark} ewtheorem{thm}{Theorem} ewtheorem{corollary}{Corollary} ewtheorem{pro}
{Proposition} ewtheorem{lemma}{Lemma} usepackage{pifont} usepackage{lscape}
usepackage{algorithm,algpseudocode} algnewcommand{Initialize}[1]{% State
extbf{initialization:} Statex hspace*{algorithmicindent}parbox[t]{.8linewidth}{ aggedright #1} }
allowdisplaybreaks[4] % Commands for probability ewcommand{p}[1]{mathbb{P} left{ #1 ight}}
ewcommand{e}[1]{mathbb{E} left[ #1 ight]} ewcommand{ee}[2]{mathbb{E}_{#1} left[ #2 ight]}
ewcommand{var}[1]{mathrm{var} left( #1 ight)} ewcommand{cov}[1]{mathrm{cov} left( #1
ight)} %Commands for commonly used notation ewcommand{xh}{hat{x}} % Commands for
Assumptions with descriptionlabel makeatletter letorgdescriptionlabeldescriptionlabel
enewcommand*{descriptionlabel}[1]{% letorglabellabel letlabel@gobble

The System Approach And Its Effect On The Cost...
A system approach behaves like a tool to estimate market elements which affect the cost–
effectiveness of any business. It underscores the interactive nature and interdependence of external
and internal factors in an organization. If we take a flashback on the day we baffled somewhere in a
process, we found ourselves among one of the stages of the process. The process is divided into
fragments of beginning, middle, near–the–end and end. Fragments make the complex tasks much
easier and much more tractable, but we always pay a concealed gigantic cost for it. In the paper,
Goldratt talks about the system approach by dividing them into three subparts– Just–in–time,
Statistical process control, and the Theory of constraints. All three ways are ... Show more content
on Helpwriting.net ...
It is used for boosting overall performance. The theory of constraint helps in identifying the
important bottleneck in processes and systems, so that we can improve the performance. All the
systems are interdependent. Each system has its boundaries and finite capacity. Similarly, Just in
Time is also interdependent. These interdependencies may effect a weakness in the system and can
harm the whole linkage of the chain. Hence, the weakest link regulates the strength of the system.
Many times in our regular life we hear batching, and these batching increases the variations in the
system. It reflects dependency. Young man tells that batching can be done in any amount or in any
amount of time. Quantity and time are exchangeable in batching and both can be treated as variable
and invariable. For instance, we need to batch a load full of material , then material is invariable and
time becomes variable. On the flip side, if we say, we need to batch thrice a month, then time is
invariable and material is variable. So, batching always reflects the dependency. Furthermore,
Youngman discusses the details about detail and dynamic complexity. He describes the detail
complexity as a sort where there are many dissimilar variables to consider, whereas Dynamic
complexity is defined as the sort where cause and effect are subtle and the effect over the time is not
obvious. Detail complexity can be view in operating processes nonetheless of the

What Is The Benchmark Function?
good results regarding the solution quality and success rate in finding optimal solution.
Performances of algorithms are tested on mathematical benchmark functions with known global
optimum. In order evaluate the optimization power of BSA various benchmark functions are taken
into consideration. This dissertation presents the application of GSA on 10 benchmark functions and
GOA on 8 benchmark functions. These benchmark functions are the classical functions utilized by
many researchers. Despite the simplicity, we have chosen these test functions to be able to compare
our results to those of the current meta–heuristics. Benchmark functions used are minimization
functions and are subdivided into the two groups i.e., unimodal and multimodal. ... Show more
content on Helpwriting.net ...
Benchmark functions used are minimization functions and are subdivided into the two groups i.e.,
unimodal and multimodal. Multimodal functions are also categorized into fixed dimension and high
dimension multimodal functions. GSA is a heuristic optimization algorithm which has been gaining
interest among the scientific community recently. GSA is a nature inspired algorithm which is based
on the Newton's law of gravity and the law of motion. GSA is grouped under the population based
approach and is reported to be more intuitive. The algorithm is intended to improve the performance
in the exploration and exploitation capabilities of a population based algorithm, based on gravity
rules. However, recently GSA has been criticized for not B.K. Panigrahi [2], presents a novel
heuristic optimization method to solve complex economic load dispatch problem using a hybrid
method based on particle swarm optimization (PSO) and gravitational search algorithm (GSA). This
algorithm named as hybrid PSOGSA combines the social thinking feature in PSO with the local
search capability of GSA. To analyze the performance of the PSOGSA algorithm it has been tested
on four different standard test cases of different dimensions and complexity levels arising due to
practical operating constraints. The obtained results are compared with recently reported methods.
The comparison confirms the robustness and efficiency of the algorithm over other existing
techniques. PSOGSA is formulated by S.

Eco204
University of Toronto Department of Economics (STG) ECO 204 2011 – 2012
Sayed Ajaz Hussain Lecture 1
© Sayed Ajaz Hussain, Department of Economics, University of Toronto, STG
1
Today
About ECO 204 Motivational Example HBS Case: The Prestige Telephone Company Types of
Optimization Methods in ECO 204
Unconstrained Optimization
Evaluating change in optimal solution due to a small increase in a parameter
Feedback? economics204@gmail.com
© Sayed Ajaz Hussain, Department of Economics University of Toronto, STG
2
ECO 204
We won't use Black Board (Except for electronic submissions of Some Projects) ECO 204 Course
Website http://www.economics.utoronto.ca/ahussain/eco204_2011_2012/ajaz_eco204.htm
Username: ... Show more content on Helpwriting.net ...
Illness before the test and notes from "quasi health care professionals" or notes stating that you
"would've performed sub–optimally" will not be accepted
❷ If explanation satisfactory, you will take a ½ hour oral exam within 5 (calendar) days of the
missed test (test administered by both instructors) ❸ Conditional on your performance on oral exam
you'll be allowed take a single cumulative makeup test on Wednesday, April 4th, 3 – 5 pm in GE
213
Feedback? economics204@gmail.com
© Sayed Ajaz Hussain, Department of Economics, University of Toronto, STG
10
Lecture Slides and HWs

Lecture slides posted on 204 website by Sunday midnight
You are expected to

Using Computers For Optimize Design And New Systems
Using computers to optimize designs and new systems is common so that companies can produce
better products more efficiently. However, the algorithms that are run within the computers to
optimize the designs and systems are usually not optimized because the companies are interested in
the new product or system and less in the software that is used to optimize their new ideas. Is seems
reasonable that if an algorithm can be used to optimize other systems, then it should have the ability
to optimize another algorithm and maybe even itself. Since the computer programs that are used to
implement algorithms are a well–defined process with limited and well–defined constraints, it
would be logical, that optimization of an algorithm program should be as easy as optimization of a
new product or system. An optimized optimization–algorithm should minimize the resources needed
to converge on the solution it is attempting to optimize resulting in faster execution time and
utilization of fewer processing resources such as memory or more capable processor. Optimization
of an optimization–algorithm may at times be redundant since the optimization algorithm has
already produced a satisfactory solution for the individual system. However, if the optimized
algorithm is general enough that it can be reused on future systems, then its application becomes
beneficial in minimizing future execution time or resources.
Optimization algorithms are fundamental processes in data analysis, engineering,

Nonlinear Dynamics And Its Effect On The Performance Of...
Abstract– Nonlinear behavior is a common feature of all real word systems. However for the sake of
simplicity, a linear model is often used in the controller design procedure. However, neglected
nonlinear dynamics might decrease the performance of controller drastically. In this paper, a new
method for designing MPC controllers in state space is proposed for a class of nonlinear processes.
In the proposed method, first an MPC controller is designed in state space based on a linear model
and then, it is modified using Modal Series to compensate the effect of neglected nonlinear
dynamics. At the end, the proposed method is applied to control two real systems and the results are
discussed. Index Terms– Modal Series, Nonlinear, Predictive Control. B INTRODUCTION
ECAUSE of high performance and simplicity, MPC controllers have extended their application in
various industries [1]. These controllers share following essential ideas [2]. Predicting process
future behavior using a model. Optimizing this behavior by determining future inputs. Applying
determined input and repeating this procedure in next sampling period of time. First of all, an
appropriate model of under control system should be acquired in predictive control, which is called
predictive model. This model should be capable of predicting system's behavior to provide the
designer with required outputs in prediction horizon k using system's information till the moment t.
In mathematic words, it should be able to

Laona??on Modified Spider Monkey Algorithm
In 2015, K. Lenin et. al. [44] in their study "Modified Monkey Optimization Algorithm for Solving
Optimal Reactive Power Dispatch Problem" expressed that to reduce the real power loss,
modifications were required in local and global leader phase and a Modified Spider Monkey
Algorithm (MMO) was introduced. Paper also upheld that MMO is more favorable for dealing with
non–linear constraints. The algorithm was examined on the IEEE 30–bus system to minimize the
active power loss.
H. Sharma, et al. [45] in 2016, discussed in "Optimal placement and sizing of the capacitor using
Limaçon inspired spider monkey optimization algorithm" that to limit the losses in distribution and
transmission, capacitors of definite sizes are should have been ... Show more content on
Helpwriting.net ...
In 2016, A. Sharma et. al. [48] presented a paper "Optimal power flow analysis using Lévy flight
spider monkey optimization algorithm" in which a Lévy flight spider monkey optimization
(LFSMO) algorithm was proposed to solve the standard Optimal power flow (OPF) problem for
IEEE 30–bus system. The exploitation capacity of SMO was increased in the proposed algorithm.
LFSMO was tested over 25 benchmark functions and its performance was examined. It was found
that LFSMO gave desirable outcomes than the original SMO.
In 2017, S. Kayalvizhi et. al. [49] presented a paper "Frequency Control of Micro Grid with Wind
Perturbations using Levy Walks with Spider Monkey Optimization Algorithm." In this paper, a new
eagle strategy, which is a combination of levy flights and SMO, is utilized in the optimization of the
gains of PI controllers which helps in regulating the frequency of the micro grid. A typical micro
grid test system and a real time micro grid setup at British Columbia are the two case studies
considered, in which the frequency control is implemented. The implementation is done in two–step
search process; in the first place, levy flights do the random search and after that SMO does a
thorough local search. Results demonstrate that the proposed method outperforms the results of
other well–known algorithms and is

Optimal Transmission Expansion Planning Using Biogeography...
Optimal Transmission Expansion Planning Using Biogeography–Based Optimization (BBO)
Abstract
Transmission expansion planning (TEP) is now a significant power system optimization problem.
The TEP problem is a large–scale, complex and nonlinear combinatorial problem of mixed integer
nature where the number of candidate solutions to be evaluated increases exponentially with system
size. The objective of the TEP is to determine the installation plans of new facilities, lines and other
network equipments. The main goal of this paper centers on the application of Biogeography –
Based Optimization (BBO) for the transmission planning systems and it is one of mathematical
methods (algorithms) to get the optimal planning.
An accurate cost function for the transmission system is formulated where both fixed and variable
costs for all planned facilities are includes, in addition to the cost energy losses. The cost function is
then minimized, using (BBO) algorithms. We can be used to derive algorithms for optimization. We
apply the BBO on the model of IEEE of 6–bus test system.
Keywords: Biogeography –Based Optimization; Transmission planning
1. Introduction
Transmission system is the bulk transfer of electrical energy, from generating power plants to
electrical substations located near demand centers. The main objective of the transmission
expansion planning (TEP) problem is to determine the optimal expansion plan of the electrical
system. According to the treatment of the study

The Optimization Problem Of Matlab Routines
Relevant numerical techniques, which have been done with the help of MATLAB routines, are
applied to solve the arising optimization problem and to find the optimum parameters of the TMD.
For a given mass ratio, µ, one can assume different values of the frequency ratio, f, and for each
frequency ratio assuming a range of damping factor ζ2 of the TMD and estimate the optimum
parameters that minimize a certain desired output. Fig. 8 is an example of the numerical
optimization conducted to estimate the optimal frequency ratio and damping factor of the TMD for
two different mass ratios under wind loads modeled as white–noise. The optimization is based on
the minimization of the displacement of the primary structure. In this numerical optimization, the
responses of the primary structure are normalized, which means that the response obtained with the
TMD when attached to the structure is divided by the corresponding response obtained without the
TMD. The optimal values of the frequency ratio and the damping factor of the TMD are written on
the subfigures. It is shown that a TMD with 1% mass ratio can provide a significant reduction in the
displacement response of the primary structure. The reduction in the displacement depends very
much on the tuning frequency and the damping ratio of the TMD. By increasing the mass ratio from
1% to 5%, the displacement response of the primary structure is reduced. However, the TMD with
5% mass ratio is more robust to the changes in the frequency

Problems With Optimization Of Tcp Protocol
ABSTRACT This paper is concerned with optimization of TCP since 2.5G and 3G services are
available to public users and mobile clients accessing the Internet using TCP/IP is increasing. It
highlights the features of 2.5G and 3G networks and its deployment. It also offers recommendations
on appropriate TCP algorithms for nodes known to be starting or ending on such paths.However
TCP was originally designed for use in wired networks which differ a lot from the wireless
networks.The technical mechanisms recommended in this document are available in modern TCP
stacks, and considered safe for use by a growing community of users. 1.0 INTRODUCTION 2g
systems are commonly refered to as second generation networks and have initiated exponential
growth in the number of wireless network from 1990s when digital voice encoding replaced analog
systems (1G). Second Generation 2G circuit switched systems are based on various radio
technologies including frequency, code and time division multiple access. Examples are 2G systems
GSM (Europe), PDC (Japan), and IS–95 (USA). The data links provided by 2G systems are mostly
circuit–switched and have transmission speeds of 10–20 kbps uplink and downlink. An
overwhelming demand for higher data rates resulted in the introduction of 2.5g which incresed
availability and curtailed some challenges such as lack of radio spectrum allocated for 2G. 3G
systems provide both packet–switched and circuit–switched connectivity in order to address the

Essay On Load Shedding
To overcome problem of load shedding and making generating units available at the time of peak
load and when there is shortage for the supply of gas a lot of work has been done before. Different
approach has been used to make maximum gas available to the generators. For that separate
modeling of the gas network has been done [1]. Studies tell us that there are differences between
natural gas transmission system and electrical system. Natural gas is the primary form of energy that
we get straight form gas field whereas electrical energy is a secondary source of energy and is what
we get after we transform energy from primary source. Since natural gas is the primary source we
must transfer it forms its source to make it available to its ... Show more content on Helpwriting.net
...
The problem formulation in reference [4] was done as follows:
1) Power balance
2) Hourly generation bids
3) Must on area protection constraints
4) System spinning and operating reserve requirements
5) Minimum up and down time limits
6) Ramp rate limits
7) Startup and shutdown characteristics of units
8) Fuel and multiple emission constraints
9) Transmission flow and bus voltage limits
10) Load shedding and bilateral constraints
11) Limits on state and control variables
12) Scheduled outages
To obtain the optimal solution various method such as dynamic programming, Lagrangian
relaxation, mixed integer programing, and expert system were used. But mainly they worked on
dynamic programming and Lagrangian relaxation and to deal with mixed integers they used mixed
integer programing, Benders decomposition were also used to solve the two stage UC problem.
Where after solving the unit commitment problem for generators when we check for the feasibility
of transmission system and if the transmission system is not feasible then using benders cuts will be
generated and this bender's cut will be added to the master UC problem. To solve a unit commitment
problem with AC constraints it creates a very complex problem which needs lots of method to
linearize the system which lead to an approximate solution.
A simpler way is

Bridgeton Industries: Automotive Component&Fabrication Plant
University of Toronto Department of Economics ECO 204 2010 – 2011
Sayed Ajaz Hussain Lecture 1
Ajaz Hussain. Department of Economics. University of Toronto (St. George)
1
Today
About ECO 204
(Single–Variable) Functions (Single–Variable) Calculus (Single–Variable) Unconstrained
Optimization (Single–Variable) Concave and Convex Functions
Ajaz Hussain. Department of Economics. University of Toronto (St. George) 2
Instructor:
Office Room 212, Economics Department 150 St. George Street Office hours Thursdays 1 – 3 pm or
by appointment E–mail sayed.hussain@utoronto.ca
Teaching Assistants
Head TA: Asad Priyo E–mail: asad.priyo@utoronto.ca Please ... Show more content on
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Department of Economics. University of Toronto (St. George) 12
Ahead ..
Functions: notation
3 Classes of Optimization Problems Unconstrained Optimization Interior Solution Boundary
Solution How to solve unconstrained optimization problems Concave and convex functions
A Motivational Example
Source: HBS Case The Prestige Telephone Company
Prestige Telephone Company Average intercompany billing capped at $82,000/month Ri = Pi Qi
"Inter company" Pi = $400/hr. → Qi = 205 hours/month "Commercial" PC = $800/hr

Other Services
Prestige Data Services
Commercial Customers
14
A Motivational Example
Exhibit 1: The Prestige Telephone Company January 2003 February 2003 March 2003
Intercompany Hours Commercial Hours Total Revenue Hours Service Hours Available Hours Total
Hours 206 123 329 32 223 584 181 135 316 32 164 512 223 138 361 40 143 544
15
Commercial Demand Curve
See Excel Model 1.1 In March 2003 PC = $800, QC = 138 hours In March 2003, management feels:
$800 PC ↑ by $200 → 30% ↓ QC PC ↓ by $200 → 30% ↑ QC
138
Commercial Hours
16
Commercial Price
Commercial Demand Curve and

Dynamic Programming Methods For Electricity Generation...
Dynamic Programming Method Approach to Unit Commitment for Electricity Generation Schedule
in Yangon Division
Khine Khine Mon*, Than Zaw Htwe*, Soe Soe Ei Aung*
Department of Electrical Power Engineering,Yangon Technological Universtiy
Yangon Technological Universtiy, Insein Township,Yangon, Myanmar
Abstract
This paper presents a Dynamic Programming (DP) method based an algorithm to solve the Unit
Commitment (UC) scheduling of the thermal generation units in Yangon. Electricity demands are in
its peak in Yangon, it has become very difficult for operators to fulfill the demand in the present.
The main objective of Unit Commitment is to determine a minimum cost turn–on and turn–off
schedule of a set of electrical power generating units to meet a load demand while satisfying a set of
operational constraints. The total production costs include fuel, startup, shutdown, and no–load
costs. There are many conventional and evolutionary programming methods used for solving the
unit commitment problem. Dynamic programming method is one of the successful approaches to
unit commitment problem. Dynamic Programming has many advantages over the enumeration
scheme, the chief advantage being a reduction in the dimensionality of the problem. It is one of the
refined algorithm design standards and is powerful tool which yields definitive algorithm for
various types of optimization problems. To implement the unit commitment problem into an
optimization program, the MATLAB® software is used.

A New Optimization Technique And The Foraging Strategy Of...
In 2002, a new optimization technique was proposed by Passino which is inspired by the foraging
strategy of Escherichia Coli (E. Coli) bacteria present in human intestines called Bacteria Foraging
Optimization Algorithm (BFOA) [1]. It is a population–based stochastic search algorithm that has
been introduced to solve the problem related to optimization and control system. Since its inception,
BFOA successfully has drawn the attention of many researchers from diverse fields to exploit its
performance as a high–performance optimizer and has been successfully applied in real world
applications such as optimal power control [2], image processing [3], jobs scheduling[4], [5] and
etc. The advantages that motivate researchers to explore its ... Show more content on
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By sending the signal, it enables an individual bacterium to communicate with others. Healthy
bacteria will be reproduced and poor foraging bacteria will be eliminated. The bacteria will keep
repeating these processes in their lifetime.
In BFOA, each of the individual bacteria in the search space is representing an individual solution to
the optimization problem [6]. Each bacterium will undergo chemotactic steps to the direction of
minimum fitness function (rich in nutrients). During the taxis, each bacterium will communicate
with other to swarm in the group toward the global optimum. Bacteria will be evaluated again
according to their health and sorted in ascending order. Half of them with better health will be
reproduced by splitting into two and the other half of poor health bacteria will be eliminated from
the search space. In order to explore more space, some of the bacteria will be eliminated and
reinitialized randomly to explore unvisited space in order to find the global minimum or maximum
point. For better understanding, this algorithm mechanism will be explained in solving an
optimization problem.
In optimization problem that we need to find the minimum of J(θ), θ ∈ ℜp, where we do not have
measurements or an analytical description of the gradient ∇J(θ). This problem is considered as a
non–gradient optimization problem. BFOA does not rely on the gradient function to operate but use
concentration of location of search space as the fitness function. Let θ be

How Can ALO Have Been Implemented To Solve The Problem?
results obtained show that ALO have been successfully implemented to solve different ELD
problems; moreover, ALO is able to provide very spirited results in terms of minimizing total fuel
cost and lower transmission loss. Also, convergence of ALO is very fast as compared to lambda
iteration method, particle swarm optimization (PSO) algorithm, genetic algorithm (GA), APSO,
artificial bee colony (ABC), and Grey Wolf optimizer (GWO) for small–scale power systems. Also,
it has been observed that the ALO has the ability to converge to a better quality near–optimal
solution and possesses better convergence characteristics than other widespread techniques reported
in the recent literature. It is also clear from the results obtained by different ... Show more content
on Helpwriting.net ...
In order to evaluate the effectiveness of the proposed method, 3–unit, 30 Bus IEEE, 13–unit and 15–
units are used as case studies with incremental fuel cost functions. The constraints include ramp rate
limits, prohibited operating zones and the valve point effect. These constraints make the economic
dispatch (ED) problem a non–convex minimization problem with constraints. Simulation results
obtained by the proposed algorithm are compared with the results obtained using other methods
available in the literature. Based on the numerical results, the proposed RTO algorithm is able to
provide better solutions than other reported techniques in terms of fuel cost and robustness. In order
to verify the feasibility and efficiency of the proposed algorithm, RTO algorithm was applied on two
set of case studies. The first set includes a 23 standard benchmark functions. The second set
includes four test systems (i.e., 3, 6, 13 and 15–units systems) for solving ED problem considering
various constraints. James J.Q.Yu n [8], presented a social spider algorithm for solving the non–
convex economic load dispatch problem. In order to solve such non–convex ELD problems, in this
paper we propose a new approach based on the Social Spider Algorithm(SSA).The classical SSA is
modified and enhanced to adapt to the unique characteristics of ELD problems, e.g. ,valve–point
effects, multi–fuel operations,

Switched Reluctance Motor ( Srm ) Essay
Switched reluctance motor (SRM) has merits of rigid and simple structure, simple converter circuit
with fault tolerance, high starting torque, premium speed regulation performance, high speed
capability, high torque to inertia ratio. However, the SRM suffers from noticeable torque ripple due
to discrete nature of the torque production, acoustic noises, high nonlinear characteristics due to the
doubly saliency structure. The nonlinearity in the operation of the SRM complicates the analysis as
well as the control of the motor [1–5]. Based on the modelling of SRM magnetic circuit, three
models of SRM are found in literature: linear model, nonlinear model without mutual inductances,
and nonlinear model with mutual inductances. Linear models in [6,7] are designed and simulated
readily. On contrast nonlinear models are obtained after a large set of experimental tests to obtain
the magnetic characteristics [8–15], or from a finite element method (FEM) analysis [16–18], which
takes into consideration the saturation of rotor and stator materials. The nonlinear model is
preferable when accurate precision is wanted. The converter used with SRM requires at least one
switch per phase due to unidirectional phase current. This is a big advantage when compared to the
converters for AC motor drives. Some configurations of converters used in SRM drives are
presented in [19–22]. The half–bridge asymmetric converter is the most widely used for SRM drive
applications, because of its high

The Optimization Problems Of The Constraint Optimization...
1. Introduction
In this paper, the problem we consider is the constrained optimization problem, as follows:
(P) min f (x)
s.t. gi(x) ≤ 0, i = 1, 2, . . . , m, x ∈ X,
Where X ⊂ Rn is a subset, and f , gi: X → R, i ∈ I = {1, 2, . . . , m} are continuously differentiable
functions. Let X0= {x ∈ X|gi(x) ≤ 0, i = 1, 2, . . . , m} be the feasible solution set. Here we assume
that X0 is nonempty. The penalty function method provides an important approach to solving (P),
and it has attracted many researchers in both theoretical and practical aspects (see e.g.
[1,8,9,11,12,18,25]). In 1967, Zangwill [25] first proposed the classic l1 exact penalty function:
F(x, σ ) = f (x) + σm∑i=1max{0, gi(x)}, (1)
Where σ > 0 is a penalty parameter, it is known from the theory of ordinary constrained
optimization that the l1 exact penalty function is a better candidate for penalization. The obvious
difficulty with the exact penalty functions is that it is not a smooth function. Which prevents the use
of efficient minimization algorithms, and causes some numerical instability problems in its
implementation when the value of the penalty parameter becomes larger. Hence, in order to use
many efficient algorithms, such as Newton Method, it is very necessary and important to smooth
exact penalty function to solve constrained optimization problems. In fact, almost all penalty
function algorithms need to change the value of the penalty parameter in computational process. So
do the exact

Multiobjective Planning of Recloser-Based Protection...
In the last years, distribution automation has gathered a significant relevance in distribution systems
planning and operation. The network operator (NOp) looks for a suitable configuration of the feeder
topology as well as the system, pursuing the reliability enhancement and a full energy demand
supply. Nevertheless, an efficient protection system requires an adequate investment in such devices
as reclosers, fuses and sectionalizers. Thus, two conflictive objectives arise, namely, NOp
investment minimization and reliability maximization.
In this sense, the number and location of devices in the system are critical variables to accomplish
preceding objectives. Here, we focus on recloser–based protection systems.
Specifically, ... Show more content on Helpwriting.net ...
Hence, it is necessary to apply metaheuristic approaches.
The remainder of this paper is organized as follows. In section II efficient planning of NCRs by
applying a multiobjective optimization approach is described. The optimization problem
formulation is presented in Section III, along with objective functions details. Section IV presents
the MOEAs to solve the multiobjective optimization problem. Here, the revised non–dominated
sorting genetic algorithm (NSGA–II), and non–dominated sorting differential evolution (NSDE) are
detailed. The test system is given in section V, and simulation results are provided in section VI.
Finally, conclusions are drawn and future research is suggested.
II. MULTIOBJECTIVE OPTIMIZATION APPROACH
In most real world optimization problems the solution must to be found considering multiple
objectives instead of one.
Whereas these objectives are often in conflict, a trade–off relation among them arises. that is, it is
necessary to sacrifice the performance of one or more objectives to enhance the other ones. Let us
consider, in the context of this paper, the decisionmaking involved in the sizing of the protection
system and the placement of NCRs. The amount of protective devices in a DS can vary from any
recloser to a few. Let us take two extreme hypothetical cases: 1) non branch has the

The Optimization Problems Of Swarm Intelligence
The combinatorial optimization problems such as Travelling Salesman Problem, Minimum
Spanning Tree Problem, Vehicle Routing Problem etc. aims at finding an optimal object from a
finite set of objects. Brute force methods which include exhaustive search are not feasible for such
problems. In recent years many new and interesting methods are applied for the solution of such
problems. These methods such as genetic algorithms (GA), Simulated Annealing, Tabu Search, and
Neural Networks are inspired from physical and biological processes.
In this project, the aim is to study popular NP–hard multi–objective combinatorial optimization
problem, Vehicle Routing Problem (VRP). The VRP has many variants. In this project we will keep
focus on one such variant, Capacitated Vehicle Routing Problem (CVRP).
Swarm Intelligence is a new emerging field for solving such combinatorial optimization problems.
Swarm Intelligence, in particular, Ant Colony metaheuristic is inspired from the way ants search for
food by building shortest path between food and nest. Our objective in this project is to develop and
enhance Ant Colony metaheuristic algorithm for solving Capacitated Vehicle Routing Problem.
1.1 Problem Description
These days transportation system is one of the unavoidable systems for everybody. It appears in a
large number of practical situations, such as transportation of people and products, delivery services,
garbage collection etc. It can be applied everywhere, for vehicles,

Pso Algorithm Is Developed By The Social Behavior Patterns...
3.3 PSO PSO algorithm is developed by the social behavior patterns of the organisms that exist and
interact within large groups. As, it converges at a faster rate than the global optimization algorithms,
the PSO algorithm is applied for solving various optimization problems easily. In the PSO
technique, a population called as a swarm of candidate solutions are encoded as particles in the
search space. Initially, PSO begins with the random initialization of the population. These particles
move iteratively through the D–dimensional search space to search the optimal solutions, by
updating the position of each particle. During the movement of the swarm, a vector Xi=(Xi1,
Xi2,...., XiD) represents the current position of the particle 'i'. Vi=(Vi1, Vi2,...., ViD) represents the
velocity of the particle which is in the range of [−vmax, vmax]. The best previous position of a
particle is denoted as personal best Pbest. The global best position obtained by the population is
denoted as Gbest. The PSO searches for the optimal solution by updating the velocity and position
of each particle, based on the Pbest and Gbest. The next position of the particle in the search space
is calculated by using the new velocity value. This process is repeated for a fixed number of times or
until a minimum error is achieved. The rate of the change in the velocity and position of the particle
is given as v_id=v_id+c_1 r_1 (p_id–x_id )+c_2 r_2 (p_gd–x_id ) (3.1) x_id=x_id+v_id (3.2)
Where c_1

Project Scheduling By Simulation Modelling Technique
Project Scheduling by Simulation Modelling Technique
Ramkumar Harikrishnakumar
Wichita State University Abstract
In the present scenario of manufacturing, agile manufacturing calls for flexibility in the global
market which involves rapid changes. Flexible manufacturing technology such as agent
manufacturing plays a vital role to achieve agility in the system. (Yeung, W. 2012) in job shop floor
the project scheduling technique provides the dynamic and unique way of dispatching the jobs if
there is any variabilities in the manufacturing system, resources, production disturbances and
irregular arrival of products. The main emphasis is designing the network schedules which will be
applicable to be implemented in a competitive operating condition. These software scheduling tools
will be able to represent the manufacturing tasks, sub tasks, work system and people involved.
(Wang, 2015) using simulation tools, the performance of a (simulated) multi–agent manufacturing
system under particular operating conditions can be analyzed in terms of. Simulation models are
developed based on current system to eliminate bottlenecks, to prevent under or over–utilization of
resources, and to optimize system performance.
Project Scheduling by Simulation Modelling Technique
Multi–objective optimization model was applied to minimize the bottleneck machine makespan and
the total product tardiness for machine tool in a job shop and Particle swarm optimization (PSO) is
been used to

The Analogy Of Car Driving: Analogy Of DSS
4.7.4. Analogy beyond DSS
The analogy of car driving is used for establishing a context based DSS. While driving, the safety of
the car (and people sitting inside) depends on the driving decision efficiency. Moreover, it depends
on the execution of the car handling skills and its effectiveness. In general, during driving, the
external environment creates a demands on the driver's decisions. Imagine, if the driver faces an
obstacle suddenly in front of him, what happens from the moment the obstacle appears until the
driver has stopped his vehicle are shown in Figure 4.9. The eye observes the obstacle and sends a
message to the brain. The brain interprets the signal, then the driver is in a position to decide what
needs to be done, subsequently ... Show more content on Helpwriting.net ...
Therefore, before developing it, the personnel working on the development should have enough
awareness and knowledge about the overall functional and safety goals, and constraints associated
with it. When it comes to real–time applications, the developer should be clear about what resources
are available at on–board AHVs. Unless it is clear, it is difficult to establish an effective DSS.
Therefore, before developing the DSS, the knowledge is acquired on AHO, available resources on
on–board AHVs, and suitable control measures (based on the operational context). The above
mentioned knowledge is gained through reading available documents, simulator training, field
observation and discussions with experts in the domain (see Section 3.3.4). As described earlier,
each operation is different from other in terms of vessels, people, safety constraints, etc. Hence,
effective control measures depends on the operational context. As described in the Section 4.6, the
master maintains the vessel's position and stability by means of executing control measures to
continue the operation safely or brought back to a safe condition. However, in critical situations, if it
is not possible to continue the operation safely, then the operation can be abandoned by means of
releasing mooring line. While the vessel is operating above situations, an effective control

The Epidemic Of Ebola Hemorrhagic Fever
Introduction
Ebola hemorrhagic fever is a severe and often deadly illness named after a river in the Democratic
Republic of Congo (formerly Zaïre) where it was first identified in 1976 with a high case fatality
rate lying between 50 and 90%. Outbreaks between 1972 and 2007 are shown in Table 1. The
disease first came into the limelight in 1976 in Zaïre and Sudan in 1976 [17]. Its origin is still
unknown and it is widely believed that Ebola virus is transmitted to humans from discrete life cycles
in animals or insects, but regardless of the original source. Person–to–person transmission is the
means by which Ebola outbreaks and epidemics progress. Bioterrorism threats as well as emergence
of new pandemic and drug–resistant variants of known infections require development of the tools
that would adequately predict occurrence of epidemics, assess efficiency of countermeasures, and
optimize the efforts directed towards provision of biological safety.
Mathematical modeling has emerged as an important tool for gaining understanding of the dynamics
of the spread of infectious diseases. The need of accurate models describing the epidemic process
are vital, because infectious diseases outbreaks disturb the host population and has financial and
health consequences. There is also the need to use sound statistical analysis methods to test the fit of
such models to observed data to account for uncertainties by means of probabilistic models.
The optimization of the control of an

Multi Objective Optimization Using Ant Colony Algorithm

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