The aim of this book is to present and analyse theoretical advances and also emerging practical applications of swarm and evolutionary intelligence. It comprises nine 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, PSO with mutation, Chaotic PSO with mutation, multi-objective 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 and their latest variations. In chapter 6, several other metaheuristic algorithms are discussed such as: Firefly algorithm (FA), Harmony search (HS), Cat swarm optimization (CSO) and their improved algorithm modifications. 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) and other algorithm variations such as binary and chaotic versions. Chapter 8 presents machine learning applications of swarm and evolutionary algorithms. Illustrative real-world examples are presented with real datasets regarding neural network optimization and feature selection, using: genetic algorithm, Geometric PSO, Chaotic Harmony Search, Chaotic Cuckoo Search, and Evolutionary Algorithm and also crime forecasting using swarm optimized SVM. In chapter 9, applications of swarm intelligence on deep long short-term memory (LSTM) networks and Deep Convolutional Neural Networks (CNNs) are discussed, including LSTM hyperparameter tuning and Covid19 diagnosis from chest X-Ray images. The aim of the book is to present and discuss several state-of-theart swarm intelligence and evolutionary algorithms together with their variances and also several illustrative applications on machine learning and deep learning.
Georgios N. Kouziokas is a Lecturer at the University of Thessaly, Greece. He holds a Ph.D. in artificial intelligence in decision systems from the University of Thessaly. He holds four Masters of Science (MSc) in: computer science, applied mathematics, education, geographic information systems and environmental spatial analysis and a BSc in computer science.
He serves as an editor in two international journals about the application of artificial intelligence, editorial board member and associate editor in several international journals. He has reviewed for more than 60 international journals. He was awarded with the Emerging Scholar Award 2018 by the University of Illinois, USA for his Ph.D. achievements. Also, he was awarded with the Top Peer Reviewer Award 2018, 2019 by Publons organization, part of Web of Science.
He has more than 45 publications in peer-reviewed international scientific journals, book chapters and conference proceedings from major publishers, like Elsevier and Springer. He has served as a member of the organizing committee, program chair in several international conferences. His major research areas include work related to Artificial Intelligence, Computational Intelligence and Optimization, Swarm Intelligence, Machine Learning, Deep Learning, Neuro-Fuzzy Logic, Applied Mathematics, Information Systems, Educational Informatics, Environmental Informatics, Data Analysis, AI in Education, AI in Public Management, AI in justice, AI in Image Processing/Remote Sensing - Geographic Information Systems, Robotics, Quantum Artificial Intelligence and Cyber-Security.
