ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2017, Vol. 54 ›› Issue (8): 1772-1784.doi: 10.7544/issn1000-1239.2017.20170343

Special Issue: 2017人工智能前沿进展专题

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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

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

CLC Number: