Abstract: In order to solve a class of function optimization problems, a population intelligent optimization algorithm, microbial dynamics optimization (MDO) algorithm, is proposed by using the microbial culture dynamics with hybrid food chain and time delay. In this algorithm, it is assumed that many microbial populations are cultivated in a microbiological culture system, the growth of microbial populations is influenced not only by the flow of culture fluid and the concentration of nutrients and harmful substances in the culture system, but also by the interaction of populations; the regular injection of the culture medium will suddenly increase the concentration of nutrients and toxic substances, which will suddenly increase the impact on populations. Using these characteristics, the absorption operator, the snatching operator, the hybrid operator and the toxin operator are constructed; by use of these operators and the growth and changes of microbial populations, the global optimal solutions of an optimization problem can be quickly determined. The simulation results show that the MDO algorithm has certain advantages in solving opti-mization problems with higher dimensions.

Key words: swarm intelligence optimization algorithm, microbial culture kinetics, microbial population, microbial dynamics optimization (MDO) algorithm

摘要： 为了解决一类函数优化问题，利用带时滞影响的混杂食物链微生物培养动力学理论提出一种微生物动力学优化（MDO）算法。在该算法中，假设有多个微生物种群在一个培养系统中培养，微生物种群的生长不但受注入到培养系统中的培养液流量、营养物质和有害物质的浓度影响，而且受种群之间相互作用的影响；定期注入的培养液会突然增加营养物质和有毒物质的浓度，从而会突然加大对种群的影响。利用上述特点构造出了吸收算子、攫取算子、混杂算子和毒素算子；利用这些算子和种群的生长变化，能够快速求解优化问题的全局最优解。仿真实验结果表明，MDO算法对求解维数较高的优化问题具有一定的优势。

关键词: 群智能优化算法, 微生物培养动力学, 微生物种群, 微生物动力学优化（MDO）算法