A self-adaptive multiagent evolutionary algorithm for electrical machine design
Jean-Laurent Hippolyte, Christelle Bloch, Pascal Chatonnay, Christophe Espanet, Didier Chamagne · 2007
This paper presents a self-adaptive algorithm that hybridises evolutionary and multiagent concepts. Each evolutionary individual is implemented as a simple agent capable of re-production and predation. The transitions between these two states depend on the agent's local environment. Thus, no explicit global process is defined to select neither the mates nor the preys. The convergence of the algorithm emerges from the behaviour of the agents. This brings interesting properties, such as population size self-regulation. Two sets of experimental results are provided: a comparison with Saw-Tooth Algorithm and micro-GA using four classical functions and an optimisation of the efficiency and the weight of an electrical motor. Some possible evolutions and prospects are finally proposed.