基于混合离散粒子群优化的控制模式分配算法

Hybrid Discrete Particle Swarm Optimization Algorithm for Control Pattern Assignment

连续微流控生物芯片是生物化学实验自动化、微型化的革命性技术.多路复用器的控制模式分配作为连续微流控生物芯片自动化设计的关键环节之一,是难的NP(Non-deterministic Polynomial)优化问题.现有工作采用粒子群优化算法求解控制模式分配问题存在过早陷入局部最优解、收敛速度慢以及算法稳定性差的缺点.为此,本文提出一种连续微流控生物芯片下基于混合离散粒子群优化的控制模式分配算法.首先,为了加快算法收敛速度及避免过早陷入局部最优解,提出了离散的自适应区域搜索策略.其次,通过基于样例的社会学习机制提高了算法的稳定性.然后,采用等距抽值的方式筛选出自适应区域搜索策略中重要参数的最佳组合,以进一步提高分配方案的质量.最终实验结果表明,所提算法在多路复用器中阀门使用数量上平均优化了19.01%,在算法稳定性上提高了29.18%,且在现实的生化应用中有良好的性能表现.

Continuous-flow microfluidic biochip is a revolutionary technology for automation and miniaturization of biochemical experiments.As one of the key links in the automatic design of continuous microfluidic biochips,the control pattern assignment problem of multiplexers is an NP-hard combinatorial optimization problem.The existing particle swarm optimization algorithm for control pattern assignment problem has the disadvantages of falling into local optimal solution prematurely,slow convergence speed,and poor stability of the algorithm.In this paper,control pattern assignment algo⁃rithm based on hybrid discrete particle swarm optimization for continuous-flow microfluidic biochips is proposed.First,in order to accelerate the convergence speed of the proposed algorithm and avoid falling into a local optimum prematurely,a discrete adaptive region search strategy is proposed.Second,the stability of the proposed algorithm is improved by a sam⁃ple-based social learning mechanism.Third,the optimal combination of the important parameters in the adaptive region search strategy is selected by equidistant sampling to further improve the results.The final experimental results show that the proposed algorithm optimizes the number of valves by an average of 19.01%,and improves the stability of the algorithm by 29.18%,and then performs well in practical biochemical applications.