Swarm Intelligence and Evolutionary Computation: Theory, Advances and Applications in Machine Learning and Deep Learning

The aim of this book is to present and analyse theoretical advances and also emerging practical applications of swarm and evolutionary intelligence. It comprises ten chapters. Chapter 1 provides a theoretical introduction of the computational optimization techniques regarding the gradient based methods such as steepest descent, conjugate gradient, newton and quasi-Newton methods and also the non-gradient methods such as genetic algorithm and swarm intelligence algorithms. Chapter 2 discusses evolutionary computation techniques and genetic algorithm. Swarm intelligence theory and particle swarm optimization algorithm are reviewed in Chapter 3. Also, several variations of particle swarm optimization algorithm are analysed and explained such as Geometric PSO and Quantum mechanics - based PSO Algorithm. Chapter 4 deals with two essential colony bio-inspired algorithms: Ant colony optimization (ACO) and Artificial bee colony (ABC). Chapter 5 presents and analyses Cuckoo search and Bat swarm algorithms. In chapter 6, several other metaheuristic algorithms are discussed such as: Firefly algorithm (FA), Harmony search (HS), Cat swarm optimization (CSO). The latest Bio-Inspired Swarm Algorithms are discussed in chapter 7, such as: Grey Wolf Optimization (GWO) Algorithm, Whale Optimization Algorithm (WOA), Grasshopper Optimization Algorithm (GOA). Chapter 8 presents Machine learning optimization applications such as artificial neural network optimization. In chapter 9 an application of swarm intelligence in deep long short-term memory (LSTM) networks is discussed. Chapter 10 presents an illustrative application of swarm intelligence on Deep CNN satellite image classification regarding the remote sensing of environment. The aim of the book is to provide knowledge in the application of improved optimization techniques in several computational and artificial intelligence problems.