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This book provides an emerging computational intelligence tool in the framework of collective intelligence for modeling and controlling distributed multi-agent systems referred to as Probability Collectives. In the modified Probability Collectives methodology a number of constraint handling techniques are incorporated, which also reduces the computational complexity and improved the convergence and efficiency. Numerous examples and real world problems are used for illustration, which may also allow the reader to gain further insight into the associated concepts.

Engineering. --- Computational Intelligence. --- Artificial Intelligence (incl. Robotics). --- Statistical Physics, Dynamical Systems and Complexity. --- Artificial intelligence. --- Ingénierie --- Intelligence artificielle --- Computational intelligence. --- Multiagent systems. --- Engineering & Applied Sciences --- Computer Science --- Intelligence, Computational --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Agent-based model (Computer software) --- MASs (Multiagent systems) --- Multi-agent systems --- Systems, Multiagent --- Statistical physics. --- Dynamical systems. --- Artificial intelligence --- Soft computing --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Dynamical systems --- Kinetics --- Mathematics --- Mechanics, Analytic --- Force and energy --- Mechanics --- Physics --- Statics --- Mathematical statistics --- Construction --- Industrial arts --- Technology --- Statistical methods --- Intelligent agents (Computer software) --- Artificial Intelligence. --- Complex Systems. --- Statistical Physics and Dynamical Systems.

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This book presents the latest insights and developments in the field of socio-cultural inspired algorithms. Akin to evolutionary and swarm-based optimization algorithms, socio-cultural algorithms belong to the category of metaheuristics (problem-independent computational methods) and are inspired by natural and social tendencies observed in humans by which they learn from one another through social interactions. This book is an interesting read for engineers, scientists, and students studying/working in the optimization, evolutionary computation, artificial intelligence (AI) and computational intelligence fields.

Computational intelligence. --- Mathematical optimization. --- Intelligence, Computational --- Artificial intelligence --- Soft computing --- Optimization (Mathematics) --- Optimization techniques --- Optimization theory --- Systems optimization --- Mathematical analysis --- Maxima and minima --- Operations research --- Simulation methods --- System analysis --- Artificial intelligence. --- Computational Intelligence. --- Artificial Intelligence. --- Continuous Optimization. --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Fifth generation computers --- Neural computers