Multi-agent cooperative algorithms of global optimization

In this paper we present multi-agent cooperative algorithms of global optimization based on a genetic algorithm, an evolution strategy and particle swarm optimization. Island and co-evolution approaches have been selected as a main scheme of cooperation. The proposed techniques have been implemented and evaluated on a set of 22 multivariate functions. We assert that the proposed techniques could achieve much higher results in terms of reliability and speed criteria than the performance of corresponding conventional algorithms (without cooperative schemes) with average parameters on 18 functions from the 22 selected for the evaluation procedure. Such advantages are much more observable with increasing dimensionality of functions. Furthermore, the performance of the suggested algorithms was even higher than the performance of conventional algorithms with the best parameters for 5 functions.