Applied Nature-Inspired Computing: Algorithms and Case Studies

Siddhartha Bhattacharyya (Hrsg.), Nilanjan Dey (Hrsg.), Amira S. Ashour (Hrsg.)

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Springer Singapore img Link Publisher

Naturwissenschaften, Medizin, Informatik, Technik / Allgemeines, Lexika

Beschreibung

This book presents a cutting-edge research procedure in the Nature-Inspired Computing (NIC) domain and its connections with computational intelligence areas in real-world engineering applications. It introduces readers to a broad range of algorithms, such as genetic algorithms, particle swarm optimization, the firefly algorithm, flower pollination algorithm, collision-based optimization algorithm, bat algorithm, ant colony optimization, and multi-agent systems. In turn, it provides an overview of meta-heuristic algorithms, comparing the advantages and disadvantages of each.

 

Moreover, the book provides a brief outline of the integration of nature-inspired computing techniques and various computational intelligence paradigms, and highlights nature-inspired computing techniques in a range of applications, including: evolutionary robotics, sports training planning, assessment of water distribution systems, flood simulation and forecasting, traffic control, gene expression analysis, antenna array design, and scheduling/dynamic resource management.


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Schlagwörter

algorithm analysis and problem complexity, Evolutionary Robotics, Hybrid Optimization Methods, Multi-Agent Systems, Genetic Algorithms, Particle Swarm Optimization, Nature Inspired Computing