7,765 research outputs found
Differential evolution with an evolution path: a DEEP evolutionary algorithm
Utilizing cumulative correlation information already existing in an evolutionary process, this paper proposes a predictive approach to the reproduction mechanism of new individuals for differential evolution (DE) algorithms. DE uses a distributed model (DM) to generate new individuals, which is relatively explorative, whilst evolution strategy (ES) uses a centralized model (CM) to generate offspring, which through adaptation retains a convergence momentum. This paper adopts a key feature in the CM of a covariance matrix adaptation ES, the cumulatively learned evolution path (EP), to formulate a new evolutionary algorithm (EA) framework, termed DEEP, standing for DE with an EP. Without mechanistically combining two CM and DM based algorithms together, the DEEP framework offers advantages of both a DM and a CM and hence substantially enhances performance. Under this architecture, a self-adaptation mechanism can be built inherently in a DEEP algorithm, easing the task of predetermining algorithm control parameters. Two DEEP variants are developed and illustrated in the paper. Experiments on the CEC'13 test suites and two practical problems demonstrate that the DEEP algorithms offer promising results, compared with the original DEs and other relevant state-of-the-art EAs
A Global Optimisation Toolbox for Massively Parallel Engineering Optimisation
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.Comment: To be presented at 'ICATT 2010: International Conference on
Astrodynamics Tools and Techniques
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