rohit_ME697Y_PSO

rohit_ME697Y_PSO - ParticleSwarmOptimization...

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Particle Swarm Optimization By Rohit Hippalgaonkar ME 697Y – Intelligent Systems Overview Introduction / Concept Algorithm Parameter Selection and Tuning Applications and Extensions Example
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Introduction Inspired by bird flocking, fish schooling, swarming theory in AI Primarily used as a stochastic search technique for global optimization Swarm relies on individual and collective memory to find optimum Hybrid between evolutionary algorithm and artificial swarming Initialized with a population of random solutions Searches for optimum by updating generations Population evolution based on previous generations Each particle in swarm defined by : Position vector Velocity vector Algorithm
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Algorithm (contd….) Update equations: V ij (t+1) = w(t) V ij (t) + c 1 rand () (p ij (t)– x ij (t)) + c 2 Rand () (p gj (t)–x ij (t)) x ij (t+1) = x ij (t) + V ij (t+1) d is the number of decision variables (j = 1, 2, …, d ) x ij
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rohit_ME697Y_PSO - ParticleSwarmOptimization...

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