Int J Neural Syst - Multi-strategy coevolving aging particle optimization.

Tópicos

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Resumo

We propose Multi-Strategy Coevolving Aging Particles (MS-CAP), a novel population-based algorithm for black-box optimization. In a memetic fashion, MS-CAP combines two components with complementary algorithm logics. In the first stage, each particle is perturbed independently along each dimension with a progressively shrinking (decaying) radius, and attracted towards the current best solution with an increasing force. In the second phase, the particles are mutated and recombined according to a multi-strategy approach in the fashion of the ensemble of mutation strategies in Differential Evolution. The proposed algorithm is tested, at different dimensionalities, on two complete black-box optimization benchmarks proposed at the Congress on Evolutionary Computation 2010 and 2013. To demonstrate the applicability of the approach, we also test MS-CAP to train a Feedforward Neural Network modeling the kinematics of an 8-link robot manipulator. The numerical results show that MS-CAP, for the setting considered in this study, tends to outperform the state-of-the-art optimization algorithms on a large set of problems, thus resulting in a robust and versatile optimizer.

Resumo Limpo

propos multistrategi coevolv age particl mscap novel populationbas algorithm blackbox optim memet fashion mscap combin two compon complementari algorithm logic first stage particl perturb independ along dimens progress shrink decay radius attract toward current best solut increas forc second phase particl mutat recombin accord multistrategi approach fashion ensembl mutat strategi differenti evolut propos algorithm test differ dimension two complet blackbox optim benchmark propos congress evolutionari comput demonstr applic approach also test mscap train feedforward neural network model kinemat link robot manipul numer result show mscap set consid studi tend outperform stateoftheart optim algorithm larg set problem thus result robust versatil optim

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