TY - JOUR
T1 - A collective study on popular nature inspired optimization
AU - Sneh, Somya
AU - Kompella, Srikar
AU - Chethan, S.
PY - 2017/1/1
Y1 - 2017/1/1
N2 - The two subsets of nature inspired algorithms are swarm intelligence based and Bio-inspired algorithms. Swarm intelligence based algorithms behave as a sub set for Bio-inspired algorithms. Some other sources of inspiration are Physics-based Chemistry-based algorithms. Though not all of them are particularly efficient however, some of them have become popular tools for modelling and solving real world problems. The purpose of this review is to present a brief description about the four major types of swarm intelligent based algorithms along with their applications so as to provide a comprehensive view on their functioning and performance. The algorithms covered in this study are Particle Swarm Optimisation (PSO), Ant Colony System (ACS), the Artificial Bee Colony (ABC), Cuckoo Search (CS).
AB - The two subsets of nature inspired algorithms are swarm intelligence based and Bio-inspired algorithms. Swarm intelligence based algorithms behave as a sub set for Bio-inspired algorithms. Some other sources of inspiration are Physics-based Chemistry-based algorithms. Though not all of them are particularly efficient however, some of them have become popular tools for modelling and solving real world problems. The purpose of this review is to present a brief description about the four major types of swarm intelligent based algorithms along with their applications so as to provide a comprehensive view on their functioning and performance. The algorithms covered in this study are Particle Swarm Optimisation (PSO), Ant Colony System (ACS), the Artificial Bee Colony (ABC), Cuckoo Search (CS).
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U2 - 10.3923/jeasci.2017.6219.6222
DO - 10.3923/jeasci.2017.6219.6222
M3 - Article
AN - SCOPUS:85038968080
SN - 1816-949X
VL - 12
SP - 6219
EP - 6222
JO - Journal of Engineering and Applied Sciences
JF - Journal of Engineering and Applied Sciences
IS - Specialissue2
ER -