Swarm Intelligence Inspired Engineering Optimization - Qi, Xuewei
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Swarm intelligence is usually observed in the collective behavior of decentralized, self-organized swarm systems, especially biological swarm systems (e.g. fish school, bird flock). Swarm intelligence algorithms realize the computational systems inspired by the swarm intelligence which always involves cooperation of large numbers of homogeneous agents in certain environment. In this book, several new swarm intelligence inspired algorithms are proposed to solve a real world engineering optimization problem. Machine Learning is also utilized to further enhance these algorithms. All of the…mehr

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Produktbeschreibung
Swarm intelligence is usually observed in the collective behavior of decentralized, self-organized swarm systems, especially biological swarm systems (e.g. fish school, bird flock). Swarm intelligence algorithms realize the computational systems inspired by the swarm intelligence which always involves cooperation of large numbers of homogeneous agents in certain environment. In this book, several new swarm intelligence inspired algorithms are proposed to solve a real world engineering optimization problem. Machine Learning is also utilized to further enhance these algorithms. All of the proposed algorithms are tested, evaluated and compared extensively. This book may satisfy many groups of people in different ways: research scientists may borrow algorithmic inspirations to model and investigate a new swarm system; software developers may follow the technical guidelines by comparing the performance of different algorithms and obtain a big picture of how to design, implement, applyand evaluate these state-of-art algorithms to address real world needs; other interested individuals can simply enjoy these beautiful nature-inspired algorithm models presented in the book.
Autorenporträt
The author received his second M.S. engineering degree from University of Georgia, Athens, USA in 2013. He is now pursing his Ph.D. degree in Electrical Engineering at University of California, Riverside. His research interests are Evolutionary Computation, Artificial Intelligence, Machine Learning and Intelligent Transportation Systems.