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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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
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
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