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Hello everyone,I have a short question about the PSO since I am a new comer to this field. how can we "add" position with velocity in the simple PSO algorithm, when they are of different units?
Particle Swarm Optimisation (PSO) is a computational method inspired by the social behavior of bird flocking and fish schooling. It is a population-based stochastic optimization algorithm that aims to find the optimal solution by simulating the collective movement and cooperation of particles in a multidimensional search space.
In PSO, a group of particles, each representing a potential solution, move around the search space and adjust their positions based on their own experience and the experience of their neighbors. The movement of particles is guided by two main components: personal best (pbest) and global best (gbest). The pbest is the best solution found by each particle, while the gbest is the best solution found by any particle in the entire swarm. These two components help the particles to converge towards the optimal solution.
PSO has several advantages, such as its simplicity, efficiency, and ability to handle large-scale optimization problems. It also does not require any gradient information, making it suitable for non-differentiable and noisy objective functions. PSO is also relatively easy to implement and can find near-optimal solutions even in complex search spaces.
PSO has been widely used in various fields, including engineering, economics, finance, and machine learning. It has been successfully applied to solve problems such as parameter estimation, pattern recognition, and data clustering. PSO has also been used to train neural networks and optimize the parameters of deep learning models.
Despite its advantages, PSO also has some limitations. One of the main limitations is that it can get stuck in local optima and may fail to find the global optimal solution. In addition, PSO may require a large number of iterations to converge, and its performance can be affected by the choice of parameters. It may also struggle with high-dimensional and multimodal optimization problems.