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Particle Swarm Optimization Algorithm and Its Application in Engineering Design Optim
Post: #1

Particle Swarm Optimization Algorithm and Its Application in Engineering Design Optimization
The Particle Swarm Optimization (PSO) method was originally proposed by J.Kennedy as a simulation of social behavior, and it was insitially introduced as an optimization method in 1995 (Eberhart and Kennedy, 1995). The PSO is a robust stochastic evolutionary computation technique based on the movement and intelligence of swarms looking for the most fertile feeding location. PSO's foundation is based on the principle that each solution can be represented as a particle (agent) in a swarm. Each agent has a position and velocity vector and each position coordinate represents a parameter value. The underlying rules of cooperation and competition within social swarms give it good capability to make global optimization with the help of memory rather than to simply random search in a certain area. So it has a better chance to fly into a better solution quickly than some previous optimizers have in addition to its better performance. Like GA or any optimization techniques, PSO also requires a fitness evaluation function that takes the agent's position and assigns to it a fitness value. For multi-parameter optimization problems, global optimization algorithms such as a genetic algorithm have been attempted but the genetic operators, such as selection, crossover, and mutation are relatively complex. By comparison, simulated annealing is much simpler but it needs too many iterations, and the result strongly depends on the cooling schedule pre-established. However the particle swarm optimization (PSO) technique as proposed by Kennedy and Eberhart in 1995 as an alternative method to solve global optimization problems is simpler and easy to handle. The modified particle swarm optimization (PSO) algorithm is applied for engineering optimization problems with constraints. PSO is started with a group of feasible solutions and a feasibility function is used to check if the newly explored solutions satisfy all the constraints. All the particles keep only those feasible solutions in their memory
Post: #2
i want to know more about particle swarm optimization technique and its applications where it can be easily implemented related to my field i.e. Electronics n Communication as i m doing project using this technique.
Post: #3
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