Abstract
The island model paradigm allows to efficiently distribute genetic algorithms overmultiple processors while introducing a new genetic operator, themigration operator, able to improve the overall algortihmic performance. In this chapter we introduce the generalized island model that can be applied to a broad class of optimization algorithms. First, we study the effect of such a generalized distribution model on several well-known global optimizationmetaheuristics.We consider some variants of Differential Evolution, Genetic Algorithms, Harmony Search, Artificial Bee Colony, Particle Swarm Optimization and Simulated Annealing. Based on an set of 12 benchmark problems we show that in the majority of cases introduction of the migration operator leads to obtaining better results than using an equivalent multi-start scheme.We then apply the generalized island model to construct heterogeneous “archipelagos”, which employ different optimization algorithms on different islands, and show cases where this leads to further improvements of performance with respect to the homogeneous case.
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Izzo, D., Ruciński, M., Biscani, F. (2012). The Generalized Island Model. In: Fernández de Vega, F., Hidalgo Pérez, J., Lanchares, J. (eds) Parallel Architectures and Bioinspired Algorithms. Studies in Computational Intelligence, vol 415. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28789-3_7
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DOI: https://doi.org/10.1007/978-3-642-28789-3_7
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