Invasive weed optimization based scheduling for digital microfluidic biochip operations

Rajesh, Kolluri; Pyne, Sumanta

doi:10.1016/j.vlsi.2020.10.003

Cited by 5 publications

(1 citation statement)

References 14 publications

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“…As with any meta‐heuristic algorithm, genetic algorithms also produce local optimal solutions and do not guarantee global optimal solutions. Recently a meta‐heuristic weed colonization behavior based algorithm is proposed in work 23 for scheduling dmfb operations. ILP based schedulers 4,13,14,24 run for exponential time, they take more time than genetic algorithms which are longer than all heuristic algorithms.…”

Section: Related Workmentioning

confidence: 99%

A hybrid artificial bee colony algorithm for scheduling of digital microfluidic biochip operations

Rajesh

Pyne

2021

Concurrency and Computation

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Summary Digital microfluidic biochips (DMFBs) are designed to efficiently carry out biochemical and biomedical analysis in a miniaturized way. DMFBs offer various advantages over traditional laboratory techniques and reduces cost, and increases automation and software programmability. Scheduling of microfluidic operations is the first and essential step in the fluidic‐level synthesis of DMFBs, while the other two are the module placement and droplet routing. Scheduling DMFB operations is a multiconstrained optimization problem, and the particular decision problem is NP‐complete. We propose a hybrid artificial bee colony (ABC) algorithm using generalized N‐point crossover (GNX) based scheduling of DMFB operations. Proposed ABC‐GNX perturbs through search space, evaluates various schedules possible, and returns the best schedule among the evaluated schedules. Simple list scheduling based heuristic algorithms can explore a single schedule based on the sequence generated by the priority function. Iterative improvement based search algorithms explore the search space and evaluate more schedules, but the proposed ABC‐GNX algorithm produces optimal solutions in shorter execution times. Simulation results show that the proposed ABC‐GNX produces a higher number of optimal completion times and faster execution times than existing algorithms.

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Section: Related Workmentioning

confidence: 99%