Bat Algorithm
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The document summarizes the bat algorithm, a metaheuristic optimization algorithm inspired by bat echolocation behavior. Bats emit sounds and use echoes to locate prey and obstacles. The bat algorithm idealizes these characteristics, with bats represented as solution vectors flying with velocities, frequencies, and loudness adjusted based on proximity to prey. The algorithm is extended to multi-objective optimization problems by combining objective functions into a single objective using weights and finding non-dominated solutions.
![Bat Algorithm
If we idealize some of the echolocation characteristics of
microbats, we can develop various bat-inspired algorithms or
bat algorithms. In the basic bat algorithm developed by Xin-
She Yang(2010a), the following approximate or idealized
rules were used.
1. All bats use echolocation to sense distance, and they also
‘know’ the difference between food/prey and background
barriers in some magical way.
2. Bats fly randomly with velocity vi at position xi with a
frequency fmin , varying wavelength and loudness A0 to
search for prey. They can automatically adjust the wavelength
(or frequency) of their emitted pulses and adjust the rate of
pulse emission r ∈ [0, 1], depending on the proximity of their
target.
3. Although the loudness can vary in many ways, we assume
that the loudness varies from a large (positive) A0 to a
minimum constant value Amin
. 6](https://image.slidesharecdn.com/hemel1-140615132726-phpapp01/85/Bat-Algorithm-6-320.jpg)
Bat Algorithm
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- 2. 2 Prepared for Nazmus Sakib Rupol Lecturer, Dept. Of CSE Ahsanullah University of Science & Technology Prepared By Md. Arman Ahmed 11.01.04.031
- 3. Introduction Bat-inspired algorithm is a meta heuristic optimization algorithm developed by Xin-She Yang in 2010.This bat algorithm is based on the echolocation behaviour of micro bats with varying pulse rates of emission and loudness. 3
- 4. Bat Behaviour Echolocation Some bats have evolved a highly sophisticated sense of hearing. They emit sounds that bounce off of objects in their path, sending echoes back to the bats. From these echoes, the bats can determine the size of objects, how far away they are, how fast they are travelling and even their texture, all in a split second. 4
- 6. Bat Algorithm If we idealize some of the echolocation characteristics of microbats, we can develop various bat-inspired algorithms or bat algorithms. In the basic bat algorithm developed by Xin- She Yang(2010a), the following approximate or idealized rules were used. 1. All bats use echolocation to sense distance, and they also ‘know’ the difference between food/prey and background barriers in some magical way. 2. Bats fly randomly with velocity vi at position xi with a frequency fmin , varying wavelength and loudness A0 to search for prey. They can automatically adjust the wavelength (or frequency) of their emitted pulses and adjust the rate of pulse emission r ∈ [0, 1], depending on the proximity of their target. 3. Although the loudness can vary in many ways, we assume that the loudness varies from a large (positive) A0 to a minimum constant value Amin . 6
- 7. Multi-Objective Bat Algorithm Objective functions f1(x), ..., fk(x), x = (x1, ..., xd)t Initialize the bat population xi (i = 1, 2, ..., n) and vi for j = 1 to N (points on Pareto fronts) Generate K weights wk ≥ 0 so that k=1 k wk = 1 Form a single objective f = k=1 k wkfk while (t <Max number of iterations) Generate new solutions and update by (1) to (3) if (rand > ri) Random walk around a selected best solution end if Generate a new solution by flying randomly if (rand < Ai & f(xi) < f(x*)) Accept the new solutions, and increase ri & reduce Ai end if Rank the bats and find the current best x* end while Record x* as a non-dominated solution end Postprocess results and visualization Figure 1: Multiobjective bat algorithm (MOBA). 7
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