What is particle swarm optimization technique?
PSO is a stochastic optimization technique based on the movement and intelligence of swarms. In PSO, the concept of social interaction is used for solving a problem. It uses a number of particles (agents) that constitute a swarm moving around in the search space, looking for the best solution.
Why PSO is better than other optimization techniques?
Particle Swarm Optimization (PSO) was developed by Kennedy and Eberhart in the mid 1990s [2]. PSO has been used increasingly due to its several advantages like robustness, efficiency and simplicity. When compared with other stochastic algorithms it has been found that PSO requires less computational effort [3] [4].
Why we use both local best and global best in PSO?
Based on these empirical studies, and also based on intuitive understanding of these neighborhood topologies, there is a faction within the PSO research community that advocates the use of the local best (lbest) PSO due to its better exploration abilities, diminished susceptibility to being trapped in local minima, and …
How the exploration and exploitation criteria are maintained in PSO algorithm?
In particle swarm optimization (PSO) algorithms there is a delicate balance to maintain between exploitation (local search) and exploration (global search). When facing multimodal functions, the standard PSO algorithm often converges to a local minimum quickly, missing better opportunities.
Is PSO machine learning algorithm?
Particle swarm optimization (PSO) is one of the bio-inspired algorithms and it is a simple one to search for an optimal solution in the solution space. In this tutorial, you will learn the rationale of PSO and its algorithm with an example.
Is PSO genetic algorithm?
The genetic algorithm (GA) is the most popular of the so-called evolutionary methods in the electromagnetics community. Recently, a new stochastic algorithm called particle swarm optimization (PSO) has been shown to be a valuable addition to the electromagnetic design engineer’s toolbox.
What is swarm particle optimization PSO How does it work What’s the main difference between PSO and GA?
Particle Swarm optimization shares many similarities with Evolutionary Computation (EC) techniques in general and GAs in particular. The main difference between the PSO approach compared to EC and GA is that PSO does not have genetic operators such as crossover and mutation.
Why particle swarm optimization is used?
In computational science, particle swarm optimization (PSO) is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality.
Why do you call Lbest PSO Local Best?
What is velocity in particle swarm optimization?
Velocity in the Particle Swarm Optimization algorithm (PSO) is one of its major features, as it is the mechanism used to move (evolve) the position of a particle to search for optimal solutions. The velocity is commonly regulated, by multiplying a factor to the particle’s velocity.