Swarm Intelligence and Evolutionary Computation

Provides theoretical analyses and practical applications of several important swarm and evolutionary intelligence algorithms, including: genetic algorithm, particle swarm optimization, ant colony optimization, artificial bee colony, bat algorithm, cuckoo search, firefly algorithm, differential evolution, cat swarm optimization and others.

Discusses a theoretical foundation and analyses of the computational optimization techniques including 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 particle swarm optimization.

Covers emerging practical applications of swarm intelligence in machine learning and deep learning including artificial neural networks (ANNs), support vector machines (SVMs), deep convolutional neural networks (CNN) and deep long short-term memory (LSTM) networks.

Covers the latest developed Bio-Inspired Metaheuristic Algorithms, including: Grey Wolf Optimization (GWO) Algorithm, Whale Optimization Algorithm (WOA), Grasshopper Optimization Algorithm (GOA).