Abstract
A new algorithm, Multi-Agent Genetic Algorithm (MAGA), is proposed for solving global numerical optimization problems. All agents live in a latticelike environment, with each agent fixed on a lattice-point. In order to increase energies, they compete or cooperate with their neighbors, and they can also use knowledge. Theoretical analyses show that MAGA converges to the global optimum. In the experiments, 6 benchmark functions are used to test the performance of MAGA, and the scalability of MAGA along the problem dimension is studied with great care. The results show that MAGA achieves a good performance when the dimensions are increased from 20 to 10,000. Moreover, even when the dimensions are increased to as high as 10,000, MAGA still can find high quality solutions at a low computational cost.
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