TY - BOOK ID - 32075700 TI - Nature-Inspired Algorithms and Applied Optimization PY - 2018 SN - 3319676695 3319676687 PB - Cham : Springer International Publishing : Imprint: Springer, DB - UniCat KW - Engineering. KW - Artificial intelligence. KW - Algorithms. KW - Mathematical optimization. KW - Computational intelligence. KW - Computational Intelligence. KW - Artificial Intelligence (incl. Robotics). KW - Optimization. KW - Intelligence, Computational KW - Artificial intelligence KW - Soft computing KW - Optimization (Mathematics) KW - Optimization techniques KW - Optimization theory KW - Systems optimization KW - Mathematical analysis KW - Maxima and minima KW - Operations research KW - Simulation methods KW - System analysis KW - Algorism KW - Algebra KW - Arithmetic KW - AI (Artificial intelligence) KW - Artificial thinking KW - Electronic brains KW - Intellectronics KW - Intelligence, Artificial KW - Intelligent machines KW - Machine intelligence KW - Thinking, Artificial KW - Bionics KW - Cognitive science KW - Digital computer simulation KW - Electronic data processing KW - Logic machines KW - Machine theory KW - Self-organizing systems KW - Fifth generation computers KW - Neural computers KW - Construction KW - Industrial arts KW - Technology KW - Foundations KW - Artificial Intelligence. KW - Natural computation. KW - Swarm intelligence. KW - Collective intelligence KW - Cellular automata KW - Distributed artificial intelligence KW - Biologically-inspired computing KW - Bio-inspired computing KW - Natural computing UR - https://www.unicat.be/uniCat?func=search&query=sysid:32075700 AB - This book reviews the state-of-the-art developments in nature-inspired algorithms and their applications in various disciplines, ranging from feature selection and engineering design optimization to scheduling and vehicle routing. It introduces each algorithm and its implementation with case studies as well as extensive literature reviews, and also includes self-contained chapters featuring theoretical analyses, such as convergence analysis and no-free-lunch theorems so as to provide insights into the current nature-inspired optimization algorithms. Topics include ant colony optimization, the bat algorithm, B-spline curve fitting, cuckoo search, feature selection, economic load dispatch, the firefly algorithm, the flower pollination algorithm, knapsack problem, octonian and quaternion representations, particle swarm optimization, scheduling, wireless networks, vehicle routing with time windows, and maximally different alternatives. This timely book serves as a practical guide and reference resource for students, researchers and professionals. ER -