智能人工蜂群改进算法及其在油田注采优化中的应用

Improved Intelligent Artificial Bee Colony Algorithm and Its Application to Optimization of Injection and Production in Oilfield

  • 摘要: 针对目前传统智能优化算法在解决复杂非线性多参数优化问题时所存在的收敛速度慢、容易陷入局部最优、时间复杂度高及易于早熟等问题,受人体神经-内分泌-免疫(neuro endocrine immune,NEI)系统的启发,提出了一种基于生物调节机制的人工蜂群智能算法(NEI-ABC)。在传统人工蜂群算法(artificial bee colony,ABC)结构基础上,该算法增设了蓝光引导单元、蜜源调控单元和触角定向单元,极大提高了引领蜂、侦查蜂和跟随蜂的搜寻和探索能力,从而使得其全局寻优性能得到提升。仿真实验表明NEI-ABC算法相对其他传统算法具有较高寻优性能,且在油田注采产量规划等复杂优化问题解决中发挥积极作用。

     

    Abstract: Traditional intelligent optimization algorithms used in solving complex nonlinear multi-parameter optimization problems have several issues, such as slow convergence, local optimization, high time complexity, and easy precocity. Thus, inspired by the human neuroendocrine immune (NEI) system, we propose an artificial bee colony (ABC) intelligence algorithm (NEI-ABC) based on biological regulation mechanisms. Based on the structure of the traditional ABC algorithm, the NEI-ABC algorithm adds a blue light guidance unit, honey source adjustment unit, and antenna orientation unit. These additions allow the NEI-ABC algorithm to enhance the search and exploration abilities of the leading bee, reconnaissance bee, and follower bee, thus improving its overall optimization performance. Our simulation results show that the NEI-ABC algorithm has better optimization performance compared to that of other traditional algorithms and plays a positive role in solving complex optimization problems such as oilfield production planning.

     

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