Optimal Transmission Expansion Planning Using Biogeography...

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
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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. 1.0INTRODUCTION 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

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

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

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

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
MATLABВ® software is used.

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

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 more content on Helpwriting.net ...
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

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

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:

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) UnconstrainedOptimization (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
Ajaz Hussain. Department of Economics. University of Toronto (St. George) 3
Teaching Assistants
Head TA: Asad Priyo E–mail: asad.priyo@utoronto.ca Please ... Show more content on Helpwriting.net ...
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
Ajaz Hussain. Department of Economics. University of Toronto (St. George) 13

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. в†’ 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 в†‘ by $200 в†’ 30% в†“ QC PC в†“ by
$200 в†’ 30% в†‘ QC
138
Commercial Hours
16
Commercial Price
Commercial Demand Curve and

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

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,

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,

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 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, Вµ, one can assume different values of the frequency ratio, f, and for each
frequency ratio assuming a range of damping factor О¶2 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

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 + Пµ)–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–Пµ) 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

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.

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

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.
ArticleMethodObjective FunctionProblem Studied
1MILPMaximize the net present value [1]Optimization of forest residue
2MILP
Monte Carlo [2]Maximization of net present valueOptimization of waste cooking oils
3MILP
Benders Decomposition Algorithm [3]Minimize the system costsNetwork design, operations, environmental issues
4MILPMaximize the total economic value [4]Development of bio–products from energy crops
5MILPMinimize costs of productionExpand production of biomass from agricultural & animal waste
6MINLP
Genetic algorithm [6]Minimize the total costsConstruction of new facilities and transportation network
7MILP
Cyber–GIS [7] BioScopeLinear combination of all the costs [7]Designing a biomass supply chain decision making process

8MILP
Stochastic [8] programmingMaximize

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 Helpwriting.net ...
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

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

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 more content on Helpwriting.net ...
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

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 ofOptimization 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
вќ· If explanation satisfactory, you will take a ВЅ hour oral exam within 5 (calendar) days of the missed test (test administered by both instructors) вќё
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

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

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

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

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) usingsimulation 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 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.
1.1Problem 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...
3.3PSO 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 [в€’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

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) в‰¤ 0, i = 1, 2, . . . , m, x в€€ X,
Where X вЉ‚ Rn is a subset, and f , gi: X в†’ R, i в€€ I = {1, 2, . . . , m} are continuously differentiable functions. Let X0= {x в€€ X|gi(x) в‰¤ 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, Пѓ ) = f (x) + Пѓmв€‘i=1max{0, gi(x)}, (1)
Where Пѓ > 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

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

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,

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

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

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 LimaГ§on 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 LГ©vy flight spider monkey optimization algorithm" in which a
LГ©vy 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

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 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 ZaГЇre) where it was
п¬Ѓrst identiп¬Ѓed 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 п¬Ѓrst came into the limelight in 1976 in ZaГЇre 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 п¬Ѓnancial and
health consequences. There is also the need to use sound statistical analysis methods to test the п¬Ѓt of such models to observed data to account for
uncertainties by means of probabilistic models.
The optimization of the control of an

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 Helpwriting.net ...
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(Оё), Оё в€€ в„
њp, where we do not have measurements or an analytical description of
the gradient в€‡J(Оё). 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 Оё be

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