Particle Swarm Optimization

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Particle Swarm Optimization

• Winter 2005

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Introduction

• Developed by Dr. Russ Eberhart (Electrical
  Engineer, Purdue School of Engineering and
  Technology) and Dr. James Kennedy (social
  psychologist, Bureau of Labor Stats) in 1995
• Easy to Implement
• Inspired by the social behavioural patterns of
  organisms along with some physics based
  interactions

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Heppners Birds

• Birds attracted to a roosting area
• Entire flock eventually lands on the roost
• Finding the roost is similar to finding a
  solution in a field of possible solutions

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PSO

• Learn from the successes of neighbours
• Try to achieve a balance between exploration
  (global search) and exploitation (local search)

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PSO Basic Algorithm

• 1. Initialize the population (assign velocities
  and locations)
• 2. Evaluate the fitness of individual particles
• 3. Keep track of location where the individual
  had its highest fitness
• 4. Modify velocities based on previous best and
  global best positions.
• 5. Update the particles position
• 6. Terminate if some condition is met
• 7. Go to Step 2

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Detailed Algorithm

• 1. Initialize the population. Each individual is
  defined by its locations and its velocity p
  locations vector
• v velocities vector

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Detailed Algorithm Cont.

• 2. Evaluate the fitness of individual particles
• 3. Keep track of location where the individual
  had its highest fitness

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Detailed Algorithm Cont.

• 4. Modify velocities based on pBest and gBest
  positions. and are constants
• 5. Update the particles position
• 6. Terminate if some condition is met
• 7. Go to step 2
• Note Random Number is also added to the
  particles velocity and its position

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Parameters

• Alpha is the inertia weight
• V_max

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Conclusion

• PSO tries to incorporate both evolutionary and
  swarm aspects
• "PSO belongs ideologically to the philosophical
  school that allows wisdom to emerge rather than
  trying to impose it, that emulates nature rather
  than trying to control it." - Eberhart and
  Kennedy