A New Adaptive Real-Coded Memetic Algorithm

Author

Nobahari, Hadi ; Darabi, Davoud

Author_Institution

Dept. of Aerosp. Eng., Sharif Univ. of Technol., Tehran, Iran

Volume

1

fYear

2009

fDate

7-8 Nov. 2009

Firstpage

368

Lastpage

372

Abstract

A new adaptive real-coded memetic algorithm has been developed for continuous optimization problems. The proposed algorithm utilizes an adaptive variant of continuous ant colony system for local search. Here new adaptive strategies are utilized for online tuning of the number of local search steps and the width of the search interval over each dimension of the search space. A new crossover scheme is also developed and utilized. The new algorithm has been examined with CEC 2005 benchmarks and compared with three other state of the art works in the field. The comparisons have showed relatively better results.

Keywords

genetic algorithms; search problems; CEC 2005 benchmarks; adaptive local search; adaptive real-coded memetic algorithm; continuous ant colony system; continuous optimization problems; genetic algorithm; Ant colony optimization; Artificial intelligence; Biological cells; Computational intelligence; Evolutionary computation; Genetic algorithms; Performance analysis; Proposals; Steady-state; Testing; Adaptive Local Search; Continuous Ant Colony System; Memetic Algorithm; Real-coded; Steady State Genetic Algorithm;

fLanguage

English

Publisher

ieee

Conference_Titel

Artificial Intelligence and Computational Intelligence, 2009. AICI '09. International Conference on

Conference_Location

Shanghai

Print_ISBN

978-1-4244-3835-8

Electronic_ISBN

978-0-7695-3816-7

Type

conf

DOI

10.1109/AICI.2009.259

Filename

5376228