Particle Swarm Optimization: Fundamentals and Applications
Apie šią el. knygą
Particle swarm optimization, often known as PSO, is a computer method that was developed in the field of computational science. This method optimizes a problem by iteratively trying to improve a candidate solution with relation to a specific measure of quality. It solves a problem by having a population of potential solutions, which are referred to as particles here, and moving these particles around in the search space in accordance with a basic mathematical formula over the particle's position and velocity. This method is called particle-based search. The movement of each particle is led toward the best known positions in the search space, which are updated when better places are identified by other particles. However, the movement of each particle is also impacted by its best known position in its local region. It is anticipated that this will direct the hive toward the optimal options.
How You Will Benefit
(I) Insights, and validations about the following topics:
Chapter 1: Particle swarm optimization
Chapter 2: Particle filter
Chapter 3: Swarm intelligence
Chapter 4: Bees algorithm
Chapter 5: Fish School Search
Chapter 6: Artificial bee colony algorithm
Chapter 7: Derivative-free optimization
Chapter 8: Multi-swarm optimization
Chapter 9: Dispersive flies optimisation
Chapter 10: Metaheuristic
(II) Answering the public top questions about particle swarm optimization.
(III) Real world examples for the usage of particle swarm optimization in many fields.
(IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of particle swarm optimization' technologies.
Who This Book Is For
Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of particle swarm optimization.
