多策略协同进化老化粒子优化算法
Multi-Strategy Coevolving Aging Particle Optimization
摘要：
提出了一种基于种群的多策略协同进化老化粒子算法（MS-CAP）。以模因的方式，MS-CAP将两个组件与互补的算法逻辑相结合。在第一阶段，每个粒子沿着每个维度独立地扰动，半径逐渐缩小（衰减），并以越来越大的力吸引到当前的最佳解。在第二阶段，粒子的变异和重组根据一个多策略的方法在差分进化的突变策略的集合的方式。该算法进行了测试，在不同的维度上，在演化计算2010年和2013年的大会上提出的两个完整的黑盒优化基准。为了证明该方法的适用性，我们还测试MS-CAP训练前馈神经网络建模的8连杆机器人机械手的运动学。数值结果表明，MS-CAP，在这项研究中考虑的设置，往往优于国家的最先进的优化算法上的一大组问题，从而导致在一个强大的和通用的优化。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
