  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 modelling 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.
