In this Post, the Particle Swarm Optimization (PSO) algorithm is fully explored. It should be noted, that in order to explain the mathematical calculations behind the particle swarm optimization algorithm or PSO algorithm, the classic version of this algorithm will be used. In the following, we will describe the types of particle swarm optimization algorithms. Also, we will introduce the existing hybrid methods using particle swarm optimization algorithms, which are a combination of heuristic and deterministic optimization methods.
Optimization and its algorithms
“Maximizing” and “minimizing” are very important issues in various fields, including technical and engineering fields. In a simple and short definition, it can be said that the problems in which the goal is to maximize or minimize a function are called “optimization problems”. Moreover, with the development of technology, the number and complexity of optimization problems in various scientific fields have also increased. The most common problems in the field of engineering that need to be used to solve optimization methods are the conversion and distribution of energy, logistics (readiness), and reloading of nuclear reactors. Optimization problems also apply to other fields, including geometry and economics. Other major areas of application of optimization include “Artificial Intelligence” (AI) and “Machine Learning”.
There are various approaches to maximizing or minimizing a function in order to find the optimal point or points. Despite the wide range of optimization algorithms that exist, there is no one-size-fits-all algorithm. In fact, one optimization method that works for one problem may not work for another. The suitability of an algorithm for a problem depends on various properties, including the derivability of the function and its concavity (convex or concave). One of the most important issues in choosing the right method for an optimization problem is the expert’s familiarity with the types of algorithms so that he/she can choose the most suitable algorithm for an optimization problem. In this post, one of the popular optimization algorithms, namely particle swarm optimization algorithm, is examined.
What are Optimization and its algorithms?
In the early 1990s, various studies were conducted on the social behavior of groups of animals. These studies have shown that some animals that belong to a particular group, such as birds, fish, and others, are able to share information in their own groups (flocks | herds) and have such a capability in these animals. It provided significant benefits for survival. Inspired by these studies, Kennedy and Eberhart introduced the Particle Swarm Optimization (PSO) algorithm (PSO) in 1995 in a paper. A particle swarm optimization algorithm or PSO algorithm is a “metaheuristic” algorithm that is suitable for optimizing nonlinear continuous functions. The authors of this paper have developed the Particle Swarm Optimization (PSO) algorithm from the concept of Swarm Intelligence, which is commonly found in groups of animals such as herds and groups of animals.
To make the general mechanism of the particle swarm optimization algorithm and other algorithms that are inspired by the group behavior of animals as clear as possible, explanations about the group behavior (herd) of animals are provided. This explanation can help to understand how to build a particle swarm optimization algorithm (and other algorithms with a similar approach) to solve complex mathematical problems.
PSO algorithm and animal group behavior