Agent-based evolutionary multiobjective optimisation

This work presents a new evolutionary approach to searching for a global solution (in the Pareto sense) to a multiobjective optimisation problem. The novelty of the method proposed consists in the application of an evolutionary multi-agent system (EMAS) instead of classical evolutionary algorithms. Decentralisation of the evolution process in EMAS allows for intensive exploration of the search space, and the introduced mechanism of crowd allows for effective approximation of the whole Pareto frontier. In the paper the technique is described as well as preliminary experimental results are reported