# Analysis: Moth Flame Optimization

1603 Words 7 Pages
CHAPTER 4
PSO AND GA TECHNIQUE

4.1 Moth Flame Optimization
In the proposed MFO algorithm, I assumed that the candidate solutions are moths and the problem’s variables are the position of moths in the space. Therefore, the moths can fly in 1-D, 2-D, 3-D, or hyper dimensional space with changing their position vectors. Since the MFO algorithm is a population-based algorithm.
It should be noted here that moths and flames are both solutions. The difference between them is the way we treat and update them in each iteration. The moths are actual search agents that move around the search space, whereas flames are the best position of moths that obtains so far. In other words, flames can be considered as flags or pins that are dropped by moths
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D is calculated as follows: With the above equations, the spiral flying path of moths is simulated. As may be seen in this equation, the next position of a moth is defined with respect to a flame. The t parameter in the spiral equation defines how much the next position of the moth should be close to the flame (t = -1 is the closest position to the flame, while t = 1shows the farthest). Therefore, a hyper ellipse can be assumed around the flame in all directions and the next position of the moth would be within this space. Spiral movement is the main component of the proposed method because it dictates how the moths update their positions around flames. The spiral equation allows a moth to fly “around” a flame and not necessarily in the space between them. Therefore, the exploration and exploitation of the search space can be guaranteed. The logarithmic spiral, space around the flame, and the position considering different t on the curve are illustrated as …show more content…
Though GA is a tool can be used as random select, they have been theoretically and empirically established to deliver robust solution in complex search spaces. The GA can be applied as follows: i. Proper Selection of binary or floating string. ii. Estimate the number of definite variables to the optimization problem. And the specific variables can be related to the number of controlled switching angles. iii. Set the initial population size depend upon the rate of convergence. iv. The fitness of every chromosome is assessed by the cost function. Since, the objective of the cost function depend upon the minimization of harmonics order with relates the switching angles v. The cost function for a nine level inverter is, f(θ_1,θ_2 〖,θ〗_3 )=|v_7 |+v_9 |/|v_1 | (4.2)
Algorithm is started with random selection of a set of solutions (represented by chromosomes) called population. Solutions from one population are taken and used to form a new population. This is motivated by a hope, that the new population will be better than the old one. Solutions which are selected to form new solutions (offspring) are selected according to their fitness - the more suitable they are the more chances they have to reproduce this is repeated until some condition (for example number of populations or improvement of the best solution) is satisfied Basic

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• ## Skittle Hive Essay

The right tension had to be found so that there was just enough energy to get it into the bowl. With that The Skittle Hive was born and ready to be tested and hopefully send a catapult to its designated target a sliver bowl. So what needed to be tested on the Skittle Hive more than you would think. The first issue that needed to be addressed was finding the right angle to launch the…

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• ## SVM And Genetic Algorithm

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• ## Fly Perfume: An Analysis Of Cuticular Hydrocarbons

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• ## Convergent And Divergent Evolution

From different origins, species arrive at a common adaptation for fitness. Example is, “flight has evolved in both bats and insects, and they both have structures we refer to as wings, which are adaptations to flight. However, the wings of bats and insects have evolved from very different original structures,” (Avissar et al., 2013,…

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• ## Ball Equation Essay

This is a necessary step in finding the initial velocity and position of the ball when it is launched. Using the rules shown in step 2 of example work we take the sine and cosine of the angle and use the property of proportions to cross multiply and to show what x and y equal. Those rules previously mentioned are that the sine of an angle, in a triangle, equals the opposite side over the hypotenuse, and the cosine of an angle, in a triangle, equals the adjacent side over the hypotenuse. The variable x now equals the x-position of the circle ', which is now called px(t), and the variable y now equals the y-position of the circle, which is now called…

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• ## Analysis Of This Moment By Eavan Boland

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• ## Balloon Physics Essay

Once the train begins to move, more forces are applied to the balloon. These forces include both tension and friction. Since there is no structure opposing the gravitational force once it is pulled to the right, the balloon begins to accelerate downwards. The…

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• ## The Chi Square Test Analysis

Researcher has applied it to understand the association and relationship between the respective variables. The Chi Square Test is thought to be appropriate to test the hypothesis as the data was in discrete categorical form. The Chi-square test is an important test amongst the several tests of the significance developed by statistician. It is basically used when the sample size is large. Chi square test is used when sample size is large.…

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• ## Analysis Of Binary Particle Swarm Optimisation

r 1,i and r 2,i are random values uniformly distributed over [0, 1]. This description of PSO is applicable to real-valued search spaces. However feature selection, along with many other problems, occur in a discrete search space and require a modified algorithm. Binary Particle Swarm Optimisation (BPSO) [?] is just such an algorithm.…

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