Swarm optimization

Due to the advancement in technology many software’s are developed and this has also lead to development of various parts of software

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1. Data Analytics using R

This has also lead to development in various types of components like filters, programs and has lead to many other advantages.

The optimization techniques which were traditionally used didn’t perform efficiently for the design of digital filter.

When a system passes various frequencies according to the requirement it is called a filter. The main work of a filter is to remove unwanted signals and improve the quality of the component.

The filters are classified into analog and digital where these have many advantages and disadvantages.

The current project deals with the application of swarm technology by finite impulse response filter. A finite impulse response filter is a filter which idealizes the request for given design specifications.

The digital filters are again classified into finite impulse response filter (FIR) and Infinite impulse response filter (IIR). The FIR finds more applications than IIR. No feedback is required in FIR so it reduces the complexity of program

Particle swarm optimization

• It is an algorithm which is based on population
• Social behavior and bird flocking are the inspiration for these technique
• It is an objective function which is non-differential and is very easy to handle
• The objective function is optimized by bird flocking or fish schooling
• The optical solution is found out by moving around the particle vectors in space.

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Particle modification

Each particle tries to modify its position by the following ways

• The space between the personal best and current position is determined
• The space between the group best and current position is determined

Flowchart for PSO

• Initialization of random vector particles
• The particle vector is than found for fitness value
• The personal value is calculated for each particle vector based on its fitness value
• The group best data is then obtained from all the personal best data
• The velocity of each particle is now updated
• The position of each particle is now updated
• The process is continuously repeated until we reach the target

Conclusion

• From the above project it can be found that this technique was found to be best to design FIR filter
• By designing with this method it was found that best coefficients were discovered with desired response magnitude values
• It was also found that the best results w.r.t minimum stop band ripple and maximum stop band attenuation was obtained.

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Swarm optimization