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.