MEA: a metapopulation evolutionary algorithm for multi-objective optimisation problems

This paper introduces a metapopulation evolutionary algorithm (MEA) for multi-objective optimisation problems. Insights from landscape ecology and population dynamics are used to develop a robust algorithm that combines the "diffusion" properties of cellular parallel genetic algorithms and "island" properties of distributed models. Two alternate selection mechanisms-a Pareto based technique and a novel environmental gradient aggregation technique-are analysed. Preliminary results suggest that the hypothesis of improved performance for spatially heterogenous populations is correct. The dynamic selection pressure, which emerges as a result of the changing environmental structure, helps to maintain population diversity and subsequently solution quality.