Evolving agents in a market simulation platform - a test for distinct meta-heuristics

Author

Oo, Naing Win ; Miranda, Vladimiro

Author_Institution

INESC, Porto

fYear

2005

fDate

6-10 Nov. 2005

Abstract

This paper presents a comparison in performance of 3 variants of genetic algorithms (GA) vs. 2 variants of evolutionary particle swarm optimization (EPSO), made in the extremely complex context of a multi-energy market simulation where the behavior of energy retailers is observed. The simulations are on JADE, a FIPA compliant platform based on intelligent autonomous agents running in a cluster of PCs. Each agent formulates its strategy by an inner complex simulation process using a meta-heuristic that tries to define optimum decisions. The results suggest that an EPSO approach is more efficient than GA

Keywords

genetic algorithms; multi-agent systems; particle swarm optimisation; power markets; power system simulation; FIPA compliant platform; JADE; complex simulation process; distinct meta-heuristics; energy retailers; evolutionary particle swarm optimization; evolving agents; genetic algorithms; intelligent autonomous agents; multienergy market simulation; Autonomous agents; Business; Energy consumption; Energy conversion; Genetic algorithms; Heating; Intelligent agent; Particle swarm optimization; Power system simulation; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Systems Application to Power Systems, 2005. Proceedings of the 13th International Conference on

Conference_Location

Arlington, VA

Print_ISBN

1-59975-174-7

Type

conf

DOI

10.1109/ISAP.2005.1599311

Filename

1599311