Genetic algorithm aided optimization of hierarchical multiagent system organization

Abstract

In this paper, we propose a genetic algorithm aided optimization scheme for designing the organization of hierarchical multiagent systems. We introduce the hierarchical genetic algorithm, in which hierarchical crossover with a repair strategy and mutation of small perturbation are used. The phenotypic hierarchical structure space is translated to the genome-like array representation space, which makes the algorithm genetic-operator-literate. Our experiments show that competitive structures can be found by the proposed algorithm. Compared with traditional operators, the new operators produced better organizations of higher utility more consistently. The proposed algorithm extends the search processes of the state-of-the-art multiagent organization design methodologies, and is more computationally efficient in a large search space.