TY  - GEN
AB  - 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.
AU  - Dey, Nilanjan,
AU  - Ashour, Amira,
AU  - Bhattacharyya, Siddhartha,
CN  - QA76.9.N37
CY  - Singapore :
DA  - 2020.
ID  - 922320
KW  - Natural computation.
LK  - https://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-981-13-9263-4
N2  - 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.
PB  - Springer,
PP  - Singapore :
PY  - 2020.
SN  - 9789811392634
SN  - 9811392633
T1  - Applied nature-inspired computing :algorithms and case studies /
TI  - Applied nature-inspired computing :algorithms and case studies /
UR  - https://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-981-13-9263-4
ER  -