ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2017, Vol. 54 ›› Issue (8): 1772-1784.doi: 10.7544/issn1000-1239.2017.20170343

所属专题: 2017人工智能前沿进展专题

• 人工智能 • 上一篇    下一篇

基于能量机制的多头绒泡菌动力学优化算法

刘阳1,冯翔1,2,虞慧群1,罗飞1   

  1. 1(华东理工大学信息科学与工程学院 上海 200237);2(上海交通大学智慧城市协同创新中心 上海 200240) (18255387158@163.com)
  • 出版日期: 2017-08-01
  • 基金资助: 
    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

Physarum Dynamic Optimization Algorithm Based on Energy Mechanism

Liu Yang1, Feng Xiang1,2, Yu Huiqun1, Luo Fei1   

  1. 1(School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237);2(Smart City Collaborative Innovation Center, Shanghai Jiao Tong University, Shanghai 200240)
  • Online: 2017-08-01

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.

关键词: 多头绒泡菌动力学优化算法, 能量机制, 年龄因子, 旅行商问题

Abstract: With the rapid development of artificial intelligence and big data, the explosive growth of big data and problem has grown in complexity, which leads to parallel intelligent computing demand increasing. Traditional theoretical models and methods are faced with severe challenges. Physics law and biological method inspired from nature has gradually become a hot spot in the present new period. Inspired by the foraging behavior of physarum, an dynamic algorithm based on energy mechanism is presented. Physarum-energy dynamic optimization algorithm (PEO) is being raised for overcome the drawbacks of physarum algorithm. According to physarum’s dynamic characteristics, the energy mechanism is introduced in PEO which aims to overcome the shortcomings of the existing physarum algorithm, such as its poor information interaction ability in whole. In addition, PEO develops age factor concept and disturbance mechanism, in order to adjust PEOs optimization ability and convergence speed in different age stages, and the convergence of algorithm model is proved through theoretical point of view. Finally, the validity and convergence of PEO are proved by experiments in TSP data set, and the main parameters of PEO are analyzed through experiments. When faced with complex problems, the simulation result comparison analysis between PEO and other optimization algorithms show that PEO is significantly better than other algorithm and PEO has the capability of high accuracy and fast convergence.

Key words: physarum-energy dynamic optimization algorithm (PEO), energy mechanism, age factor, traveling salesman problem

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