The ability of a PSO algorithm to explore and exploit can be affected by its topological structure that is, with a different structure, the speed of convergence of the algorithm and its ability to avoid premature convergence on the same optimization problem will be different because a topological structure determines the speed or direction of sharing research information for each particle. In PSO convergence, regardless of how the swarm operates, convergence to a local optimum occurs when all personal bests P or, alternatively, the best-known position of the swarm g approaches a local optimum to the problem. Since the creation of PSO, according to the researchers, the PSO algorithm and its parameters must be designed to find an appropriate balance between exploration and exploitation in order to avoid early convergence towards a local optimum while ensuring a good rate. Researchers believe that swarm behavior differs between exploratory behavior (searching for a larger part of the search space) and exploitative behavior looking for a smaller region of the search space to come closer to an optimum.
Although we can mimic the movement of a flock of birds, we can also assume that each bird helps us find the optimal solution in a large solution space, with the best solution found by the flock being the best solution in space. In other words, if a bird flies at random in search of food, all the birds in the flock can share their findings and help the whole flock get the best hunt. The algorithm was inspired by the concept of swarm intelligence, which is commonly presented in groups of animals such as herds and schools.Īs noted in the original study, fish or a flock of birds moving in groups “can benefit from the experience of all the other members”. Inspired by this research, Kennedy and Eberhart introduced the PSO algorithm in 1995, a metaheuristic algorithm suitable for the optimization of nonlinear continuous functions. that this ability gives these animals a significant survival advantage. These investigations revealed that certain creatures of a specific group, namely birds and fish, are able to transmit knowledge between themselves and themselves. Several studies on the social behavior of groups of animals were developed in the early 1990s.
Register for this Free Session> Particle Swarm Optimization (PSO) Let’s start the discussion by understanding the Particle Swarm Optimization (PSO) algorithm.
We will cover the following main points in this article. We will also learn the practical implementation of PSO using the PySwarms python package. In this article, we will discuss in detail the optimization of the particle swarm as well as how it works and its different variations. It is the method that optimizes a problem by iteratively trying to improve a candidate solution against a given quality measure. It is an algorithm which searches for the best solution in space in a simple way. Particle Swarm Optimization (PSO) is also an optimization technique belonging to the field of nature-inspired computing. Therefore, each of the optimization approaches has its own advantages and limitations. Despite the fact that there are several optimization approaches that can be used, none is considered ideal for each given case. There are several approaches that can be taken to maximize or minimize a function in order to find the optimal value.