Izzo, D., Ruciński, M., & Biscani, F. (2012). The generalized Island Model. Parallel Architectures and Bioinspired Algorithms, Studies in Computational Intelligence, 2012, Volume 415/2012, 151-169, DOI: 10.1007/978-3-642-28789-3_7
The island model paradigm allows to efficiently distribute genetic algorithms over multiple processors while introducing a new genetic operator, the migration operator, able to improve the overall algorithmic 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 optimization metaheuristics. 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.
Ruciński, M., Izzo, D., & Biscani, F. (2010). On the impact of the migration topology on the Island Model. Parallel Computing, 36(10-11), 555-571.
Parallel Global Optimization Algorithms (PGOA) provide an efficient way of dealing with hard optimization problems. One method of parallelization of GOAs that is frequently applied and commonly found in the contemporary literature is the so-called Island Model (IM). In this paper, we analyze the impact of the migration topology on the performance of a PGOA which uses the Island Model. In particular we consider parallel Differential Evolution and Simulated Annealing with Adaptive Neighborhood and draw first conclusions that emerge from the conducted experiments.
Biscani, F., Izzo, D., & Yam, C. H. (2010). A Global Optimisation Toolbox for Massively Parallel Engineering Optimisation. 4th International Conference on Astrodynamics Tools and Techniques (ICATT 2010).
A software platform for global optimisation, called PaGMO, has been developed within the Advanced Concepts Team (ACT) at the European Space Agency, and was recently released as an open-source project. PaGMO is built to tackle high-dimensional global optimisation problems, and it has been successfully used to find solutions to real-life engineering problems among which the preliminary design of interplanetary spacecraft trajectories - both chemical (including multiple flybys and deep-space maneuvers) and low-thrust (limited, at the moment, to single phase trajectories), the inverse design of nano-structured radiators and the design of non-reactive controllers for planetary rovers. Featuring an arsenal of global and local optimisation algorithms (including genetic algorithms, differential evolution, simulated annealing, particle swarm optimisation, compass search, improved harmony search, and various interfaces to libraries for local optimisation such as SNOPT, IPOPT, GSL and NLopt), PaGMO is at its core a C++ library which employs an object-oriented architecture providing a clean and easily-extensible optimisation framework. Adoption of multi-threaded programming ensures the efficient exploitation of modern multi-core architectures and allows for a straightforward implementation of the island model paradigm, in which multiple populations of candidate solutions asynchronously exchange information in order to speed-up and improve the optimisation process. In addition to the C++ interface, PaGMO's capabilities are exposed to the high-level language Python, so that it is possible to easily use PaGMO in an interactive session and take advantage of the numerous scientific Python libraries available.
Izzo, D., Ruciński, M., & Ampatzis, C. (2009). Parallel global optimisation meta-heuristics using an asynchronous island-model. IEEE Congress on Evolutionary Computation (CEC 2009) (pp. 2301-2308). IEEE.
We propose an asynchronous island-model algorithm distribution framework and test the popular differential evolution algorithm performance when a few processors are available. We confirm that the island-model introduces the possibility of creating new algorithms consistently going beyond the performances of parallel differential evolution multi starts. Moreover, we suggest that using heterogeneous strategies along different islands consistently reaches the reliability and performance of the best of the strategies involved, thus alleviating the problem of algorithm selection. We base our conclusions on experiments performed on high dimensional standard test problems (Rosenbrock 100, Rastrigin 300, Lennard Jones 10 atoms), but also, remarkably, on complex spacecraft interplanetary trajectory optimisation test problems (Messenger, Cassini, GTOC1). Spacecraft trajectory global optimisation problems have been recently proposed as hard benchmark problems for continuous global optimisation. High computational resources needed to tackle these type of problems make them an ideal playground for the development and testing of high performance computing algorithms based on multiple processor availability.
Llorens, Jose M., et al. (2013) Analysis of the strong coupling regime of a quantum well in a photonic crystal microcavity and its polarization dependence studied by the finite-difference time-domain method. 
Märtens M., Izzo, D. (2013). The Asynchronous Island Model and NSGA-II: Study of a New Migration Operator and its Performance. Genetic and Evolutionary Computation Conference, to appear in GECCO 2013.
Izzo, D., Simoes, F. L., Märtens, M., de Croon, G., Heritier, A., Hong Yam, C. (2013). Search for a Grand Tour of the Jupiter Galilean Moons. Genetic and Evolutionary Computation Conference, to appear in GECCO 2013.
Izzo., D. 'PyGMO and PyKEP: Open Source Tools for Massively Parallel Optimization in Astrodynamics (the case of interplanetary trajectory optimization)', Proceedings of the International Conference on Astrodynamics Tools and Techniques - ICATT (2012), Noordwijk, ESA, ESTEC
Kaleem, M. K., & Leitner, J. (2011). CUDA Massively Parallel Trajectory Evolution. GPUs for Genetic and Evolutionary Computation competition at the 2011 Genetic and Evolutionary Computation Conference (GECCO 2011).
"[...] Over the last year the aim was set to implement capabilities to run the PaGMO optimization heuristics on GPU architectures. As a starting point the evolving docking problem was chosen, which aims to design a neuro-controller for the automatic rendezvous and docking task in spacecraft operations. A genetic algorithm is used to search for a neural network that controls the spacecraft [...]"
Ampatzis, C., Izzo, D., Ruciński, M., & Biscani, F. (2009). ALife in the Galapagos: migration effects on neuro-controller design. In G. Kampis, I. Karsai, & E. Szathmáry (Eds.), Proceedings of the European Conference on Artificial Life (ECAL 2009) (pp. 197-204). Springer.
The parallelization of evolutionary computation tasks using a coarse-grained approach can be efficiently achieved using the island migration model. Strongly influenced by the theory of punctuated equilibria, such a scheme guarantees an efficient exchange of genetic material between niches, not only accelerating but also improving the evolutionary process. We study the island model computational paradigm in relation to the evolutionary robotics methodology. We let populations of robots evolve in different islands of an archipelago and exchange individuals along allowed migration paths. We show, for the test-case selected, how the exchange of genetic material coming from different islands improves the overall design efficiency and speed, effectively taking advantage of a parallel computing environment to improve the methodology of evolutionary robotics, often criticized for its computational cost.