ADVANCEMENT OF IMPERIALIST COMPETIT IVE ALGORITHM … · 2017-07-11 · Dr. Poorani S Professor, EEE Department, Karapagam University , India Dr. MV Jayan Professor, EEE Department,

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International Journal of Electrical Engineering & Technology (IJEET) Volume 8, Issue 1, January- February 2017, pp. 72–79, Article ID: IJEET_08_01_010 Available online at http://www.iaeme.com/IJEET/issues.asp?JType=IJEET&VType=8&IType=1 ISSN Print: 0976-6545 and ISSN Online: 0976-6553 Journal Impact Factor (2016): 8.1891 (Calculated by GISI) www.jifactor.com © IAEME Publication

ADVANCEMENT OF IMPERIALIST COMPETITIVE ALGORITHM FOR THE DESIGN OF LOW SPEED

SINGLE SIDED LINEAR INDUCTION MOTOR Sijitha Issac

Research Scholar, EEE Department, Karpagam University, India

Dr. Poorani S Professor, EEE Department, Karapagam University, India

Dr. MV Jayan Professor, EEE Department, Govt Engineering College, Thrissur, India

ABSTRACT In this paper a novel optimization algorithm based on imperialist competitive algorithm (ICA)

is used for the design of a low speed single sided linear induction motor (LIM). This type of motors is used increasingly in industrial process specially in transportation systems. In these applications having high efficiency with high power factor is very important. So in this paper mainly comparing the results of imperialist competitive algorithm and genetic algorithm by considering both efficiency and power factor. Finally the results of ICA are compared with the ones of genetic algorithm and conventional design parameters. Comparison shows the success of ICA for design of LIMs. Key words: imperialist competitive algorithm, Linear induction motors, genetic algorithm. Cite this Article: Sijitha Issac, Dr. Poorani S and Dr. MV Jayan. Advancement of Imperialist Competitive Algorithm for the Design of Low Speed Single Sided Linear Induction Motor. International Journal of Electrical Engineering & Technology, 8(1), 2017, pp. 72–79. http://www.iaeme.com/IJEET/issues.asp?JType=IJEET&VType=8&IType=1

1. INTRODUCTION Linear induction motors (LIMs) are widely used in rapid transportation systems and they obtain thrust directly without gear, link or axial mechanism. LIMs also have many other advantages such as simple structure and easy maintenance. There is much work on the such design of the linear induction motor has been performed based on different drawbacks of it. Two major disadvantages of LIMs are low power factor and low efficiency. But there are not enough works on them. In optimization is based on some characteristics such as end effect, transverse edge effect and normal force. In this reference sequential quadratic programming (SQP) method is used to optimize output volt–ampere, primary weight and cost of secondary. In optimization is done based on starting thrust and output power to input volt–ampere ratio.

Advancement of Imperialist Competitive Algorithm for the Design of Low Speed Single Sided Linear Induction Motor


2. GENETIC ALGORITHM In a genetic algorithm a population of candidate solutions to an optimization problem is evolved toward better solutions. Each candidate solution has a set of properties which can be mutated and altered; traditionally, solutions are represented in binary as strings of 0s and 1s, but other encodings are also possible. The evolution usually starts from a population of randomly generated individuals, and is an iterative process, with the population in each iteration called a generation. In each generation, the fitness of every individual in the population is evaluated; the fitness is usually the value of the objective function in the optimization problem being solved.

The more fit individuals are stochastically selected from the current population, and each individual's genome is modified (recombined and possibly randomly mutated) to form a new generation. The new generation of candidate solutions is then used in the next iteration of the algorithm. Commonly, the algorithm terminates when either a maximum number of generations has been produced, or a satisfactory fitness level has been reached for the population.

A typical genetic algorithm requires: A genetic representation of the solution domain.

A fitness function to evaluate the solution domain. GA has regenerative tools namely, crossover and mutation tools for generating a new chromosome

from parental chromosomes. Because of these tools, there is a chance of occurring old chromosomes in previous generations as new chromosome in future generations or stuck at local optimum. These noted drawbacks have led them to slow and local convergence

3. IMPERIALIST COMPETITIVE ALGORITHM Imperialist competitive algorithm (ICA) is a new evolutionary algorithm for optimization. This algorithm starts with an initial population. Each population in ICA is called country. Countries are divided in two groups: imperialists and colonies. In this algorithm the more powerful imperialist, have the more colonies. When the competition starts, imperialists attempt to achieve more colonies and the colonies start to move toward their imperialists. So during the competition the powerful imperialists will be improved and the weak ones will be collapsed. At the end just one imperialist will remain. In this stage the position of imperialist and its colonies will be the same. The flowchart of this algorithm is shown in Fig.1. As mentioned before in this optimization problem the objective function is the inverse of (9). Optimization variables are the Al sheet thickness (d), primary winding current density (Jc), slip (s) and primary width to pole pitch ratio (a/s). The number of contries is 200 and the number of imperialists is 8.

4. OPTIMIZATION PROBLEM A large number of algorithms proposed for solving non-convex problems including the majority of commercially available solvers – are not capable of making a distinction between local optimal solutions and rigorous optimal solutions, and will treat the former as actual solutions to the original problem. Because we want to

objective primaryThedesign

than power factor, we canwilloptimized On the other minimiz

optideteriorated

http://www.iaeme.com/

Compareobjective functionprimary widthThe constraintsdesign consider

Jτ(x1,. . .Where k1 and k2 are constant and x1

than power factor, we canwill be selected as k1 = 0, k2 = 1.optimized simultaneously. TheseOn the other minimized,

The minimumoptimized motor.deteriorated


ompare the abilityfunction. In

width to pole pitchconstraints are as listed

considering bothJτ(x1,. . . xn) = η(x1,…,xn)

k1 and k2 are constant and x1than power factor, we can

be selected as k1 = 0, k2 = 1.simultaneously. These

On the other hand, imperial the inverse

minimum valuemotor. Therefore,

deteriorated. One major criterion for optimizers is just the number of required function

http://www.iaeme.com/IJE

Figure 1 Flowchart of the imperialist

ability of ICA with [1] design pitch ratio(alisted in Table

both power factorη(x1,…,xn)k1.cosΦ(x1,…,xn)

k1 and k2 are constant and x1than power factor, we can choose k1 = 1, k2 = 0 and when power factor is more

be selected as k1 = 0, k2 = 1.simultaneously. These three problems are called optimal 1, optim

imperialist of (1) is regarded

value of efficiencyTherefore, we

One major criterion for optimizers is just the number of required function

Sijitha Issac, Dr. Poorani S and Dr. MV Jayan

IJEET/index.asp

Flowchart of the imperialist

with genetic variables area/s), the aluminum

Table 1 whichfactor and efficiency,

.cosΦ(x1,…,xn)k1 and k2 are constant and x1 and

choose k1 = 1, k2 = 0 and when power factor is more be selected as k1 = 0, k2 = 1.By considering

three problems are called optimal 1, optim competitive

regarded as objectiveefficiency and power

can be sureOne major criterion for optimizers is just the number of required function


asp 74

Flowchart of the imperialist

genetic algorithmare chosen

aluminum sheetwhich are the same

efficiency, the .cosΦ(x1,…,xn)k2

and xn are design choose k1 = 1, k2 = 0 and when power factor is more

By considering k1 = k2 = 1 both efficiency and power factor will be three problems are called optimal 1, optim

petitive algorithmobjective function.power factor

sure that neitherOne major criterion for optimizers is just the number of required function


Flowchart of the imperialist competitive algorithm (ICA)

algorithm in optimization as the primary

sheet thicknesssame as what

objective function

xn are design variables. Whenchoose k1 = 1, k2 = 0 and when power factor is more

k1 = k2 = 1 both efficiency and power factor will be three problems are called optimal 1, optim

algorithm the objectivefunction.

factor is chosen neither the efficiency



competitive algorithm (ICA)

optimization problemprimary winding

thickness (d), and thewhat considered

function is de

variables. When efficiency is more important choose k1 = 1, k2 = 0 and when power factor is more

k1 = k2 = 1 both efficiency and power factor will be three problems are called optimal 1, optimal 2 and

the objective function

close to theirefficiency nor


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competitive algorithm (ICA)

problems, we choosewinding current

the maximumconsidered in [1].To obtain

defined as follows

efficiency is more important choose k1 = 1, k2 = 0 and when power factor is more important, these

k1 = k2 = 1 both efficiency and power factor will be al 2 and optimal 3

function (cost function)

their initial valuesnor the power

One major criterion for optimizers is just the number of required function evaluations as

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we choose the density (Jc

maximum thrust slipobtain an optimal

follows [1]:

efficiency is more important important, these constants

k1 = k2 = 1 both efficiency and power factor will be optimal 3 respectively.

function) will

values of thepower factor has

evaluations as

same c), the

slip (s). optimal

efficiency is more important constants

k1 = k2 = 1 both efficiency and power factor will be respectively.

will be

the non has been

evaluations as this



often is already a large computational effort, usually much more effort than within the optimizer itself, which mainly has to operate over the N variables. The derivatives provide detailed information for such optimizers, but are even harder to calculate, e.g. approximating the gradient takes at least N+1 function evaluations

Table 1 Optimization results and assumed conventional parameters

Table 2 Optimization results and assumed conventional parameters

5. RESULTS Table 2 shows the motor dimensions and characteristics using conventional, genetic algorithm, and ICA design optimization methods. Conventional motor parameters and genetic algorithm search results are given from [1]. From Table 2 the maximum efficiency is obtained in the first optimization process (optimal 1) for both genetic algorithm and ICA. But ICA result is higher than GA one. Furthermore, the maximum of power factor is gained in optimal 2 and also, ICA result is better than GA. In the case of optimal 3, in comparison with conventional design genetic algorithm improved the power factor about 1.3 times. But, it collapses the efficiency about 0.9 times.

Parameter Conventional design [9]

GA Opt.1 Opt.2 Opt.3

Efficiency 0.36 0.393 0.325 0.327 Power factor 0.32 0.3 0.42 0.415 Maximum thrust slip

0.5 0.5 0.5 0.5

Aluminum thickness

2 1.4 1.7 1.7

Primary width/pole pitch

2 2.5 3.9 3.9

Primary current density

4 3 5 5

Parameter Conventional design[1]

ICA Opt. 1 Opt. 2 Opt. 3

Efficiency 0.36 0.4901 0.4345 0.4461 Power factor 0.32 0.5729 0.6463 0.6295 Maximum thrust slip

0.5 0.5 0.5 0.5

Aluminum thickness

2 1 1 1

Primary width/pole pitch

2 2 4 4

Primary current density

4 3 5 4.01

respectivelyproblemsdesign

factor10.3.


Figure

On the respectivelyproblems (optimals1design and of

Althoughfactor respectively1 the efficiency0.3. So ICA

Figure 3 Improvement of


ure 2 Mean and minimum cost of all imperialists versus iteration in these three optimization problems

other handrespectively which have

(optimals1–3)of course in

Although genetic algorithm respectively but

efficiency has beenICA is more reliable

Improvement of


Mean and minimum cost of all imperialists versus iteration in these three optimization problems

hand ICA improvedhave considerable

3) improved in each problemalgorithm in in each case

been increasedreliable than genetic

Improvement of objective function using genetic algorithm (from left to right


IJEET/index.asp


proved bothconsiderable increase.

proved both efficiencyproblem the main

in the case ofcase it has collapsed

increased from 36%genetic algorithm

objective function using genetic algorithm (from left to right


asp 76


both power factorincrease. It is importantefficiency and powermain objective

of optimal 1 collapsed the other36% to 39.5% algorithm in calculating




factor and efimportant to power factor function is and optimalother character but power

calculating optimized




efficiency aboutto mention that

factor in the comparison with much more

optimal 2 has improvedcharacteristic. For

factor has been optimized motor


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about 1.97 that ICA in

comparison with more optimized.

improved efficiencyFor example in

been decreasmotor paramet

objective function using genetic algorithm (from left to right optimal designs 1, 2 and 3)

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and 1.24 in all optimizat

comparison with conventoptimized.

efficiency and powerin optimal design

decreased from 0.32parameters.

optimal designs 1, 2 and 3)


times optimization conventional

power design 0.32 to

optimal designs 1, 2 and 3)

algorithmgeneration.in 6theimperialistsiteration

6Inof boundarysuch

competitive algorithmand

7Linearmajorisefef


Of objectivealgorithm. Asgeneration. in Fig. 6. As6 depicts thethe functionimperialists iteration 5 only

6. FINITE ELEMENT ANAIn this sectionof the cross boundary conditionssuch as flux,

Figure 4 Comparison of: (a) efficiency and (b) power factor between optimal design with genetic algorithm and ICA.

2-D FEMcompetitive algorithmand graphical

7. CONCLUSIONSLinear inductionmajor disadvantais used to optimizeefficiency isefficiency and

Advancement of Imperialist Competitive Algor


objective functionAs shown The enhancement

As mentionedthe mean and

function is found at are also in

only one of them

. FINITE ELEMENT ANAsection 2-D time

section of theconditions are

flux, flux density

Comparison of: (a) efficiency and (b) power factor between optimal design with genetic algorithm and ICA.

FEM is carriedcompetitive algorithm

graphical representa

CONCLUSIONSinduction motors

disadvantages, lowoptimize bothis optimizedand power factor



function during in this figure

enhancement of these objectivementioned before the

and minimum at the first iteratio

in good positionsthem is alive;

. FINITE ELEMENT ANAtime stepping FEM

the machineare defined. After

density distributio


carried out for and graphical

representation of flux

CONCLUSIONS motors have many

low power factor,both efficiency

optimized and in thefactor are regarded.


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different iterationsfigure in the these objective

before the objective func cost of all imperialists

first iteration. Butpositions and arealive; one that

. FINITE ELEMENT ANALYSIS FEM is employed

machine is defined.After making

distribution, etc. are


for the both thirdgraphical results are

flux lines in the

many advantagespower factor, and low

efficiency and power the second one

regarded.


asp 77

iterations in best situation,

these objective functionsfunction of imperialists

But in the worse are able to com has reached

employed to validate fined. Then the materials

ing meshes andetc. are observed.


third optimalare obtained.

the analyzed

advantages that causelow efficiency

power factor. Optimizone just power

ithm for the Design of Low Speed Single Sided Linear Induction Motor

these threesituation, GA receives

functions using imperialistof ICA is selected

imperialists versus iteration. worse case (optimal

compete. Then reached the global

validate the optimizationmaterials ofand solving


optimal design using obtained. Figs. 7 and

lyzed LIM, respectively.

cause to in- creaseefficiency. In this paper

Optimizationpower factor

ithm for the Design of Low Speed Single Sided Linear Induction Motor

three optimizationreceives to

imperialist competiselected as theiteration. It shows,(optimal designThen they start

global minimum sooner

optimizationof the different

the problem,


using geneticand 8 show

respectively.

crease their application. paper imperial

tion is done in is considered.

ithm for the Design of Low Speed Single Sided

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optimization problems optimal response

competitive algothe inverse of shows, the global

design 1) until start to collapse

sooner.

optimization results. Firstdifferent sections problem, the results


genetic algorithm the flux-density distribution

application. Butimperialist competitive

in three cases. sidered. In the

ithm for the Design of Low Speed Single Sided

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problems using genetresponse after

algorithm is shown GA one and

global minimum iteration 5,

collapse one by one,

First the geometry are assigned

results of the analysis


algorithm and imperialistdensity distribution

But they havepetitive algorithm

cases. At first just final study

genetic after 50

shown and Fig.

minimum of 5, other one, at

geometry assigned and

analysis


imperialist density distribution

have two algorithm

just the study both

comparinumber


The optimizationcomparison number of its


Figure

optimization results shows that

its iteration is


ure 5 Flux density distribution in the LIM f

results in all that in all cases

is less than

Figure 6 Flux lines in the LIM f


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Flux density distribution in the LIM f

all problemscases the results

genetic algorith

Flux lines in the LIM f


asp 78

Flux density distribution in the LIM f

problems are comparedresults of ICA are

algorithm

Flux lines in the LIM for the case of optimal design 3


Flux density distribution in the LIM for the case of optimal design 3

compared withare more optimized

or the case of optimal design 3



with the ones optimized than


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of genetic than genetic algo


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algorithm.algorithm and

algorithm. This and the



REFERENCES [1] Hassanpour Isfahani A, Ebrahimi BM, Lesani H. Design optimization of a low- speed single-sided linear

induction motor for improved efficiency and power factor. IEEE Trans Magn 2008;44(2):266–72.

[2] Mukherjee BK, Sengupta M, Das S, Sengupta A. Design, fabrication, testing and finite element analysis of a lab-scale LIM. In: Proc. IEEE Indian annu. conf., India; 2004. p. 586–9.

[3] Kikuchi S, Hashimoto Y, Ebizuka R. Some considerations on the reduction of a noise in a LIM. IEEE Trans Magn 1996;32(5):5031–3.

[4] Kitamura M, Hino N, Nihei H, Ito M. A direct search shape optimization based on complex expressions of 2-dimensional magnetic fields and forces. IEEE Trans Magn 1998;34(5):2845–8.

[5] Neto CM, Jacinto GM, Cabrita CB. Optimised design aided by computer of single sided, three-phase, linear induction actuators. In. 9th Mediterranean electrotechnical conf. proc. (MELECON’98),Tel Aviv, Israel, vol.2; 1998. p. 1157–6

[6] Khichada Bhavin A, K.J. Chudashma, Vyas Darshan M and Shiyal Jignesh D, 3-Phase Induction Motor Parameter Monitoring and Anal ysis Using Labview. International Journal of Electrical Engineering & Technology, 7(6), 2016, pp. 81–91.

[7] Raichel Mathew, Aswathy Mohandas P, A Bridgeless CUK Converter Based Induction Motor Drive For PFC Applications, International Journal of Electrical Engineering & Technology, 5(12), 2014, pp. 191–196

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ADVANCEMENT OF IMPERIALIST COMPETIT IVE ALGORITHM … · 2017-07-11 · Dr. Poorani S Professor, EEE Department, Karapagam University , India Dr. MV Jayan Professor, EEE Department,