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HOW TO USE PARTICLE DESIGNER 2.5 CODE
The code does nothing more than what was stated in the above algorithm. This is the kind of algorithm that catches my fascination. It's quite a simple algorithm, but very powerful. Repeat steps 2-4 until maximum iteration or minimum error criteria is not attained.Calculate, for each particle, the new velocity and position according to the above equations.Choose the particle with the lowest cost of all particles.
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If the current cost is lower than the best value so far, remember this position (pBest). Initialize each particle with a random velocity and random position.Where c1 is the factor that influences the cognitive behaviour, i.e., how much the particle will follow its own best solution, and c2 is the factor for social behaviour, i.e., how much the particle will follow the swarm's best solution. V is the current velocity, v' the new velocity, x the current position, x' the new position, pBest and gBest as stated above, r1 and r2 are even distributed random numbers in the interval, and c1 and c2 are acceleration coefficients. Then, each particle adjusts its velocity and position with the following equations:Ĭopy Code v' = v + c1.r1.(pBest - x) + c2.r2.(gBest - x) This value is called gBest (global best). The other best value is the current best solution of the swarm, i.e., the best solution by any particle in the swarm. This value is called pBest (particle best). This is the solution that produces the lowest cost (has the highest fitness). The first is the cognitive part, where the particle follows its own best solution found so far. In every iteration, each particle adjusts its velocity to follow two best solutions. The algorithm is initialized with particles at random positions, and then it explores the search space to find better solutions. The particles fly through the search space by following the optimum particles. Each particle has a fitness/cost value that is evaluated by the function to be minimized, and each particle has a velocity that directs the "flying" of the particles. PSO adapts this behaviour and searches for the best solution-vector in the search space. Which strategy will the birds follow? Well, each bird will follow the one that is nearest to the food. Initially, the birds don't know where the food is, but they know at each time how far the food is. To understand the algorithm, it is best to imagine a swarm of birds that are searching for food in a defined area - there is only one piece of food in this area. Videos of the exploration by "virtual birds" can be watched at YouTube: In the example shown, a function R² -> R is minimized. Thus, PSO can be used as a training method for artificial neural networks or to minimize/maximize other high dimensional functions. Here, I'm going to show how PSO can be used to minimize functions. The algorithm is very simple but powerful. Kennedy in 1995, inspired by the social behavior of birds. Particle swarm optimization (PSO) is a population based stochastic optimization technique developed by Dr.