Li Wei
School of Computer Science and Engineering, Xi'an University of Technology, Xi'an 710048, China.
Comput Intell Neurosci. 2017;2017:8469103. doi: 10.1155/2017/8469103. Epub 2017 Jul 6.
Computational scientists have designed many useful algorithms by exploring a biological process or imitating natural evolution. These algorithms can be used to solve engineering optimization problems. Inspired by the change of matter state, we proposed a novel optimization algorithm called differential cloud particles evolution algorithm based on data-driven mechanism (CPDD). In the proposed algorithm, the optimization process is divided into two stages, namely, fluid stage and solid stage. The algorithm carries out the strategy of integrating global exploration with local exploitation in fluid stage. Furthermore, local exploitation is carried out mainly in solid stage. The quality of the solution and the efficiency of the search are influenced greatly by the control parameters. Therefore, the data-driven mechanism is designed for obtaining better control parameters to ensure good performance on numerical benchmark problems. In order to verify the effectiveness of CPDD, numerical experiments are carried out on all the CEC2014 contest benchmark functions. Finally, two application problems of artificial neural network are examined. The experimental results show that CPDD is competitive with respect to other eight state-of-the-art intelligent optimization algorithms.
计算科学家们通过探索生物过程或模仿自然进化设计了许多有用的算法。这些算法可用于解决工程优化问题。受物质状态变化的启发，我们提出了一种基于数据驱动机制的新型优化算法——差分云粒子进化算法（CPDD）。在所提出的算法中，优化过程分为两个阶段，即流体阶段和固体阶段。该算法在流体阶段执行全局探索与局部开发相结合的策略。此外，局部开发主要在固体阶段进行。控制参数对解的质量和搜索效率有很大影响。因此，设计了数据驱动机制以获得更好的控制参数，以确保在数值基准问题上具有良好的性能。为了验证CPDD的有效性，对所有CEC2014竞赛基准函数进行了数值实验。最后，研究了人工神经网络的两个应用问题。实验结果表明，CPDD与其他八种先进的智能优化算法相比具有竞争力。
