基于能量机制的多头绒泡菌动力学优化算法

发布时间：2018-08-29 10:31

【摘要】：随着人工智能和大数据的迅猛发展,大数据的爆炸式增长和问题的复杂性分布导致对并行智能处理的要求日趋迫切.传统的理论模型和技术方法面临严峻挑战,受自然界启发的物理学法则和生物学方法逐渐成为研究热点.受多头绒泡菌的生长觅食等行为启发,提出了一种基于能量机制的多头绒泡菌动力学算法(physarum-energy dynamic optimization algorithm,PEO).该算法以多头绒泡菌算法为基础,根据其动力学特征,引入能量机制,以改进现有的多头绒泡菌算法全局信息交互能力差等缺点.此外,PEO引入了年龄因子的概念和扰动机制,以控制算法在不同阶段的寻优能力和收敛速度,并从理论角度对算法模型的收敛性进行证明.最后,通过在TSP数据集上实验证明算法在不同规模数据集的有效性和收敛性,并进行了参数分析.与其他的优化算法的对比实验数据表明,PEO在面对复杂问题的求解速度和收敛速度明显优于其他的优化算法,具有高精度和快收敛的特性.
[Abstract]:With the rapid development of artificial intelligence and big data, the explosive growth of big data and the complexity distribution of problems lead to the urgent need of parallel intelligent processing. Traditional theoretical models and technical methods are facing severe challenges, and the physical and biological methods inspired by nature have gradually become the focus of research. A dynamic algorithm based on energy mechanism (physarum-energy dynamic optimization algorithm,PEO) was proposed, which was inspired by the growth and foraging behavior of Phaeopsis multiceps. The algorithm is based on the multi-headed actinomycetes algorithm. According to its dynamic characteristics, the energy mechanism is introduced to improve the global information interaction ability of the existing multi-headed actinomycetes algorithm. In addition, PEO introduces the concept of age factor and perturbation mechanism to control the optimization ability and convergence rate of the algorithm in different stages, and proves the convergence of the algorithm model from a theoretical point of view. Finally, the validity and convergence of the algorithm in different scale data sets are proved by experiments on TSP datasets, and the parameters are analyzed. Compared with other optimization algorithms, the experimental data show that the solution speed and convergence speed of PEO in the face of complex problems are obviously superior to those of other optimization algorithms, and have the characteristics of high accuracy and fast convergence.
【作者单位】：华东理工大学信息科学与工程学院;上海交通大学智慧城市协同创新中心;
【基金】：国家自然科学基金项目(61472139,61462073) 上海市经济和信息委员会信息化发展专项资金项目(201602008) 上海市智慧城市协同创新中心开放基金项目~~
【分类号】：TP18

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