Metric-topological–evolutionary optimization

Abstract

This article shows a novel approach for optimization and inverse problems based on evolutionary computation with the aim to satisfy two opposite requirements: exploration and convergence. The proposed approach is particularly suitable for parallel computing and it gives its best both for multimodal problems and for problems in which bad initializations can occur. The proposed algorithm has been called MeTEO to point out its metric-topological and evolutionary inspiration. In fact, it is based on a hybridization of two heuristics coming from swarm intelligence: the flock-of-starlings optimization (FSO; which shows high exploration capability but a lack of convergence), the standard particle swarm optimization (which is less explorative than FSO but with a good convergence capability) and a third evolutionary heuristic: the bacterial chemotaxis algorithm (that has no collective behaviour, no exploration skill but high convergence capability). Finally, with the aim of speeding up the algorithm, a technique that we call fitness modification is proposed and implemented. Suitable tests regarding optimization benchmarks and inverse problems will be presented with the aim of pointing out the MeTEO performances compared with those of each single heuristic used for hybridization.