TY - BOOK ID - 14222896 TI - Fractional Order Darwinian Particle Swarm Optimization : Applications and Evaluation of an Evolutionary Algorithm AU - Couceiro, Micael. AU - Ghamisi, Pedram. PY - 2016 SN - 3319196340 3319196359 PB - Cham : Springer International Publishing : Imprint: Springer, DB - UniCat KW - Artificial intelligence. KW - Engineering. KW - Systems theory. KW - Engineering & Applied Sciences KW - Computer Science KW - Swarm intelligence. KW - Mathematical optimization. KW - Evolution equations. KW - Evolutionary equations KW - Equations, Evolution KW - Equations of evolution KW - Optimization (Mathematics) KW - Optimization techniques KW - Optimization theory KW - Systems optimization KW - Collective intelligence KW - System theory. KW - Computational intelligence. KW - Computational Intelligence. KW - Artificial Intelligence (incl. Robotics). KW - Systems Theory, Control. KW - Intelligence, Computational KW - Artificial intelligence KW - Soft computing KW - Systems, Theory of KW - Systems science KW - Science 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 - Simulation methods KW - Fifth generation computers KW - Neural computers KW - Construction KW - Industrial arts KW - Technology KW - Philosophy KW - Differential equations KW - Mathematical analysis KW - Maxima and minima KW - Operations research KW - System analysis KW - Cellular automata KW - Distributed artificial intelligence KW - Artificial Intelligence. UR - https://www.unicat.be/uniCat?func=search&query=sysid:14222896 AB - This book examines the bottom-up applicability of swarm intelligence to solving multiple problems, such as curve fitting, image segmentation, and swarm robotics. It compares the capabilities of some of the better-known bio-inspired optimization approaches, especially Particle Swarm Optimization (PSO), Darwinian Particle Swarm Optimization (DPSO) and the recently proposed Fractional Order Darwinian Particle Swarm Optimization (FODPSO), and comprehensively discusses their advantages and disadvantages. Further, it demonstrates the superiority and key advantages of using the FODPSO algorithm, such as its ability to provide an improved convergence towards a solution, while avoiding sub-optimality. This book offers a valuable resource for researchers in the fields of robotics, sports science, pattern recognition and machine learning, as well as for students of electrical engineering and computer science. ER -