"The aim of this book is to present and analyze theoretical advances and also emerging practical applications of swarm and evolutionary intelligence. It includes ten relevant chapters. In chapter 1, a theoretical introduction of the computational optimization techniques is provided 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. In chapter 2, evolutionary computation techniques and genetic algorithm are discussed. In chapter 3, swarm intelligence theory and particle swarm optimization algorithm are discussed. Also, several variations of particle swarm optimization algorithm are analyzed and explained such as Geometric PSO and Quantum mechanics-based PSO Algorithm. In chapter 4, two essential colony bio-inspired algorithms are examined: Ant colony optimization (ACO) and Artificial Bee Colony (ABC). In chapter 5, Cuckoo search and Bat swarm algorithms are presented and analyzed. 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). Machine learning optimization applications are presented in chapter 8, such as artificial neural network optimization. In chapter 9 an application of swarm intelligence in deep long short-term memory (LSTM) networks is discussed. In chapter 10, an illustrative application of swarm intelligence on Deep CNN satellite image classification regarding the remote sensing of environment is presented. The final scope of the book is to provide knowledge towards the application of improved optimization techniques in several computational and artificial intelligence problems"-- Provided by publisher.

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