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PSO (Particle Swarm Optimization) Example Step-by-Step
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Particle swarm optimization (PSO)
PSO is a computational method that Optimizes a problem. It is a Population-based stochastic search algorithm. PSO is inspired by the Social Behavior of Birds flocking. n Particle Swarm Optimization the solution of the problem is represented using Particles. [Flocking birds are replaced with particles for algorithm simplicity]. Objective Function is used for the performance evaluation for each particle / agent in the current population. PSO solved problems by having a Population (called Swarms) of Candidate Solutions (Particles). Local and global optimal solutions are used to update particle position in each iteration.
How PSO will optimize?
By Improving a Candidate Solution.
What is Search Space in PSO?
It is the range in which the algorithm computes the optimal control variable. When any optimal control value of any particle exceeds the searching space, the value will be reinitialized.
PSO Disadvantage: PSO algorithm do not guarantee an optimal solution is ever found
What is the PSO fitness value?- Get link
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Comments
great
ReplyDeletecan you provide the code for finding the life time of each sensor node by using PSO and Grass hoper lgorithm
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