swarm intelligance (1)
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LOGO
Scientific Research Group in Egypt (SRGE)
Swarm Intelligence (I)Particle swarm optimization
Dr. Ahmed Fouad AliSuez Canal University,
Dept. of Computer Science, Faculty of Computers and informatics
Member of the Scientific Research Group in Egypt
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LOGO Meta-heuristics techniques
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LOGO Outline1. Swarm intelligence (Main idea)1. Swarm intelligence (Main idea)
2. History of Particle swarm optimization 2. History of Particle swarm optimization
3. Particle swarm optimization (PSO)3. Particle swarm optimization (PSO)
4. PSO Algorithm 4. PSO Algorithm
5. Advantage / disadvantage5. Advantage / disadvantage
6. Comparison with Genetic algorithm6. Comparison with Genetic algorithm
7. References 7. References
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LOGO Swarm intelligence (Main Idea)
• Suppose you and a group of friends are on a treasure finding mission. Each one in the group has a metal detector and can communicate the signal and current position to the n nearest neighbors.
• Each person therefore knows whether one of his neighbors is nearer to the treasure than him. If this is the case, you can move closer to that neighbor. In doing so, your chances are improved to find the treasure. Also, the treasure may be found more quickly than if you were on your own.
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LOGO Swarm intelligence (Main Idea)
• A swarm can be defined as a structured collection of interacting organisms (or agents).
• Within the computational study of swarm intelligence, individual organisms have included ants, bees, wasps, termites, fish (in schools) and birds (in flocks).
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LOGO Swarm intelligence (Main Idea)
• The global behavior of a swarm of social organisms therefore emerges in a nonlinear manner from the behavior of the individuals in that swarm
• The interaction among individuals plays a vital role in shaping the swarm's behavior.
• Interaction among individuals aids in refining experiential knowledge about the environment, and enhances the progress of the swarm toward optimality.
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LOGOHistory of particle swarm optimization
•Proposed by James Kennedy &Russell Eberhart in 1995
• Inspired by simulation social behavior Related to bird
flocking, fish schooling and swarming
theory- steer toward the center- match neighbors’ velocity- avoid collisions
• Combines self-experience with social experience
• Population-based optimization
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LOGO Particle swarm optimization (concepts)
• Set of agents (particles) that
constitute a swarm moving around
in the search space looking for the
best solution
• Each particle in search space adjusts
its “flying” according to its own
flying experience as well as the flying
experience of other particles
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LOGO Particle swarm optimization (concepts)
• Movement towards a promising
area
to get the global optimum
• Each particle keeps track:
• Its best solution, personal best,
pbest
• The best value of any particle,
global best, gbest
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LOGO Particle swarm optimization (concepts)
• Each particle modifies its
position according to:
• its current position
• its current velocity
• the distance between its
current position and pbest
• the distance between its
current position and gbest
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LOGO Particle swarm optimization (concepts)
• Swarm: a set of particles (S)• Particle: a potential solution
• Position, Velocity:
• Each particle maintains• Individual best position (PBest)
• Swarm maintains its global best (GBest)
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LOGO Particle swarm optimization Algorithm
P = Particle_Initialization();
For i=1 to it_max For each particle p in P do fp = f(p); If fp is better than f(pBest) pBest = p; end end gBest = best p in P;
For each particle p in P do v = v + c1*rand*(pBest – p) + c2*rand*(gBest – p); p = p + v; endend
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LOGO Particle swarm optimization Algorithm
vi(t+1) = vi (t)+ c1*rand*(pBest(t) – p(t)) + c2*rand*(gBest(t) – p(t));
Social influence
Personal influencesInertia
Particle’s velocity
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LOGO PSO Algorithm (parameter setting)
The right way
This way
Or this way
• Number of particles (10—50) are reported as usually sufficient.
• C1 (importance of personal best)
• C2 (importance of neighborhood best)
• Usually C1+C2 = 4.
• Vmax – too low: too slow too high: too unstable.
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LOGO Advantage / disadvantage
Advantage Disadvantage
Simple implementation
Slow convergence in refined search stage
Few parameters to adjust
Weak local search ability
Efficient in global search
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LOGOComparison with genetic algorithm (GA)
• Commonalities• PSO and GA are both population based
stochastic optimization
• Both algorithms start with a group of a randomly generated population,
• Both have fitness values to evaluate the population.
• Both update the population and search for the optimium with random techniques.
• Both systems do not guarantee success.
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LOGOComparison with genetic algorithm (GA)
• Differences
• PSO does not have genetic operators like crossover and mutation. Particles update themselves with the internal velocity.
• Particles do not die.
• The information sharing mechanism in PSO is significantly different
• There is no selection in PSO
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LOGO References
• Computational Intelligence An IntroductionAndries P. Engelbrecht, University of Pretoria South Africa
• Some slides adapted from a presentation“The Particle Swarm Optimization Algorithm” By Andry Pinto, Hugo Alves, Inês Domingues, Luís RochaSusana Cruz.
Particle Swarm Optimizationhttp://www.particleswarm.info/http://www.swarmintelligence.org
