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One Improved Agent Genetic Algorithm — Ring-Like Agent Genetic Algorithm For Global Numerical Optimization

Author

Listed:
  • BIN LIU

    (School of Economics and Business Administration, Chongqing University, Chongqing, China 400030, China)

  • TEQI DUAN

    (School of Economics and Business Administration, Chongqing University, Chongqing, China 400030, China)

  • YONGMING LI

    (College of Communication Engineering, Chongqing University, Chongqing, China 400030, China)

Abstract

In this paper, a novel genetic algorithm — dynamic ring-like agent genetic algorithm (RAGA) is proposed for solving global numerical optimization problem. The RAGA combines the ring-like agent structure and dynamic neighboring genetic operators together to get better optimization capability. An agent in ring-like agent structure represents a candidate solution to the optimization problem. Any agent interacts with neighboring agents to evolve. With dynamic neighboring genetic operators, they compete and cooperate with their neighbors, and they can also use knowledge to increase energies. Global numerical optimization problems are the most important ones to verify the performance of evolutionary algorithm, especially of genetic algorithm and are mostly of interest to the corresponding researchers. In the corresponding experiments, several complex benchmark functions were used for optimization, several popular GAs were used for comparison. In order to better compare two agents GAs (MAGA: multi-agent genetic algorithm and RAGA), the several dimensional experiments (from low dimension to high dimension) were done. These experimental results show that RAGA not only is suitable for optimization problems, but also has more precise and more stable optimization results.

Suggested Citation

  • Bin Liu & Teqi Duan & Yongming Li, 2009. "One Improved Agent Genetic Algorithm — Ring-Like Agent Genetic Algorithm For Global Numerical Optimization," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 26(04), pages 479-502.
    • Handle: RePEc:wsi:apjorx:v:26:y:2009:i:04:n:s0217595909002316
    DOI: 10.1142/S0217595909002316
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    Cited by:

    1. Yu Zhao & Xi Zhang & Zhongshun Shi & Lei He, 2017. "Grain Price Forecasting Using a Hybrid Stochastic Method," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 34(05), pages 1-24, October.
    2. Pin Wang & Yongming Li & Bohan Chen & Xianling Hu & Jin Yan & Yu Xia & Jie Yang, 2017. "Proportional Hybrid Mechanism for Population Based Feature Selection Algorithm," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 16(05), pages 1309-1338, September.

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