Back to Search
Start Over
Multi-Strategy Coevolving Aging Particle Optimization
- Source :
- International Journal of Neural Systems, Volume 24, Issue 1, December 2013
- Publication Year :
- 2018
-
Abstract
- 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.
- Subjects :
- Computer Science - Neural and Evolutionary Computing
Subjects
Details
- Database :
- arXiv
- Journal :
- International Journal of Neural Systems, Volume 24, Issue 1, December 2013
- Publication Type :
- Report
- Accession number :
- edsarx.1810.05018
- Document Type :
- Working Paper
- Full Text :
- https://doi.org/10.1142/S0129065714500087