Comparative study of five bio-inspired evolutionary optimization techniques

Author

Krishnanand, K.R. ; Nayak, Santanu Kumar ; Panigrahi, B.K. ; Rout, P.K.

Author_Institution

Electr. & Electron. Eng., Silicon Inst. of Technol., Bhubaneswar, India

fYear

2009

fDate

9-11 Dec. 2009

Firstpage

1231

Lastpage

1236

Abstract

Bio-inspired evolutionary algorithms are probabilistic search methods that simulate the natural biological evolution or the behaviour of biological entities. Such algorithms can be used to obtain near optimal solutions in optimization problems, for which traditional mathematical techniques may fail. This paper does a comparative study of results of five evolutionary algorithms: Genetic Algorithm (GA), Particle Swarm Optimization (PSO) Algorithm, Artificial Bee Colony (ABC) Algorithm, Invasive Weed Optimization (IWO) Algorithm and Artificial Immune (AI) Algorithm when applied to some standard benchmark multivariable functions.

Keywords

artificial immune systems; genetic algorithms; search problems; statistical analysis; artificial bee colony; artificial immune algorithm; bio-inspired evolutionary optimization; genetic algorithm; invasive weed optimization; natural biological evolution; near optimal solutions; optimization problems; particle swarm optimization; probabilistic search methods; standard benchmark multivariable functions; Artificial intelligence; Birds; Evolution (biology); Evolutionary computation; Genetic algorithms; Genetic mutations; Immune system; Particle swarm optimization; Search methods; Silicon; Artificial Bee Colony Algorithm; Artificial Immune Algorithm; Genetic Algorithm; Invasive Weed Optimization Algorithm; Particle Swarm Optimization Algorithm;

fLanguage

English

Publisher

ieee

Conference_Titel

Nature & Biologically Inspired Computing, 2009. NaBIC 2009. World Congress on

Conference_Location

Coimbatore

Print_ISBN

978-1-4244-5053-4

Type

conf

DOI

10.1109/NABIC.2009.5393750

Filename

5393750