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Particle swarm optimization: PSO algorithm is a stochastic optimization technique. Its basic idea is to find the optimal solution through cooperation and information sharing among individuals in the swarm.
Step 1: Initialize the swarm. We give the initial velocity vector and initial position vector of the particle. We also give the swarm size and set various parameters.
Step 2: Calculate the fitness value. We calculate the fitness value of the particle and save it. We compare it with the historical optimum fitness value of the particle, save the smaller value as the optimum fitness value.
Step 3: Find the global optimum fitness value of the swarm. The smallest optimum fitness value of the swarm is used as the global optimum fitness value and every particle preserves it.
Step 4: Update the particle velocity and position. Each particle updates its velocity and position in the iteration.
Step 5: Check whether the algorithm is complete. When the number of iterations is less than the maximum number of iterations, repeat step 2, step 3 and step 4. Otherwise, the iteration terminates.
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IEEE/ICACT20230136 Slide.11
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