INCAS3 - Innovation Centre for Advanced Sensors and Sensor Systems, P.O. Box 797, 9400 AT Assen, The Netherlands.
Int J Neural Syst. 2014 Feb;24(1):1450008. doi: 10.1142/S0129065714500087. Epub 2013 Dec 10.
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.
我们提出了多策略协同进化老化粒子（MS-CAP），这是一种新颖的基于种群的黑盒优化算法。在遗传算法的方式中，MS-CAP 将两个具有互补算法逻辑的组件结合在一起。在第一阶段，每个粒子沿着每个维度独立地被扰动，并且随着力的增加被吸引到当前的最佳解决方案。在第二阶段，根据多策略方法，粒子根据差分进化中的突变策略集合的方式进行突变和重组。在所提出的算法在不同维度上，在 2010 年和 2013 年进化计算大会上提出的两个完整的黑盒优化基准上进行了测试。为了证明该方法的适用性，我们还使用 MS-CAP 来训练用于模拟 8 连杆机器人运动学的前馈神经网络。数值结果表明，对于本研究中考虑的设置，MS-CAP 在大量问题上往往优于最先进的优化算法，从而成为一种稳健且通用的优化器。
