Performance analysis for the enhancement of ACO algorithms using Fourier transform

Author

Raghavendra, G.S. ; Borase, Amit Dharmaraj ; Prasanna, Kumar N.

Author_Institution

Comput. Sci. Dept., BITS, Pilani, India

fYear

2009

fDate

9-11 Dec. 2009

Firstpage

708

Lastpage

712

Abstract

Ant Colony Optimization (ACO) algorithms belong to class of Meta-heuristic algorithms, where a search is made for optimized solution rather than exact solution, based on the knowledge of the problem domain. ACO algorithms are iterative in nature. As the iteration proceeds, solution converges to the optimized solution. In this paper, we examine the pheromone trial, a knowledge repository for ants, which guides the ants in the search process and analyzed the nature of convergence of ACO algorithms using Fourier transforms.

Keywords

Fourier transforms; optimisation; search problems; Fourier transforms; ant colony optimization algorithm; knowledge repository; metaheuristic algorithm; performance analysis; pheromone trial; search process; Algorithm design and analysis; Ant colony optimization; Distributed computing; Feedback; Fourier transforms; Iterative algorithms; Pattern analysis; Performance analysis; Scheduling algorithm; Space exploration; Ant Colony Optimization; Convergence of ACO; Fourier transforms; Meta-heuristics; Pheromone Trial;

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.2009.5393476

Filename

5393476