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.
维普
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