An Analytical Review of Nature Inspired Optimization Algorithms

Author(s):

Sherry Chalotra , Guru Nanak Dev Engineering College, Ludhiana; Sumeet Kaur Sehra, Guru Nanak Dev Engineering College, Ludhiana; Sukhjit Singh Sehra, Guru Nanak Dev Engineering College, Ludhiana

Keywords:

Swarm Intelligence (SI), Artificial Intelligence(AI), Bee Colony Optimization (BCO), Ant Colony Optimization (ACO), Particle Swarm optimization (PSO), Artificial Bee Colony (ABC), Biological Systems

Abstract:

Nature inspired optimization algorithm is a rapid growing research area in computer science. It gives an enormous inspiration for solving many complicated problems as it exposes a very divergent, robust, compelling and appealing behavior which is capable to give optimal results. The ability to adapt the ever changing environment makes it a hub for solving complex problems. Nature Inspired Optimization Algorithms are meta-heuristic which mimic the behavior of natural occurring species, like ants, bees, flies, bats, termites etc., and provide adequate ways for solving many complex combinatorial and optimization problems. This paper represents an analytical review of nature inspired optimization algorithms, like bee colony, ant colony, particle swarm optimization and artificial bee colony, which are developed by modeling the intelligence and natural behavior of different species, like ants, bees etc.

Other Details:

Manuscript Id	:	IJSTEV2I3024
Published in	:	Volume : 2, Issue : 3
Publication Date	:	01/10/2015
Page(s)	:	123-126

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