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

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.