A Dynamic Multi-Agent Algorithm applied to challenging benchmark problems

Author

Lepagnot, Julien ; Nakib, Amir ; Oulhadj, Hamouche ; Siarry, Patrick

Author_Institution

Lab. Images, Univ. de Paris-Est Creteil, Creteil, France

fYear

2012

fDate

10-15 June 2012

Firstpage

1

Lastpage

8

Abstract

Many real-world optimization problems are dynamic (time dependent) and require an algorithm that is able to continuously track a changing optimum over time. In this paper, we investigate a recently proposed algorithm for dynamic continuous optimization, called MLSDO (Multiple Local Search algorithm for Dynamic Optimization). MLSDO is based on several coordinated local search agents and on the archiving of the optima found over time. This archive is used when a change occurs in the objective function. The performance of the algorithm is evaluated on the set of benchmark functions provided for the IEEE WCCI-2012 Competition on Evolutionary Computation for Dynamic Optimization Problems.

Keywords

multi-agent systems; optimisation; search problems; Evolutionary Computation for Dynamic Optimization Problems; IEEE WCCI-2012 Competition; MLSDO algorithm; archiving; benchmark function; coordinated local search agent; dynamic continuous optimization; dynamic multi-agent algorithm; multiple local search algorithm for dynamic optimization; objective function; Equations; Heuristic algorithms; Optimization; Silicon; Space exploration; Synchronization; Vectors;

fLanguage

English

Publisher

ieee

Conference_Titel

Evolutionary Computation (CEC), 2012 IEEE Congress on

Conference_Location

Brisbane, QLD

Print_ISBN

978-1-4673-1510-4

Electronic_ISBN

978-1-4673-1508-1

Type

conf

DOI

10.1109/CEC.2012.6252867

Filename

6252867