A restructured artificial bee colony optimizer combining life-cycle, local search and crossover operations for droplet property prediction in printable electronics fabrication

For printable electronics fabrication, a major challenge is the print resolution and accuracy delivered by a drop-on-demand piezoelectric inkjet printhead. In order to meet the challenging requirements of printable electronics fabrication, this paper proposes a novel restructured artificial bee colony optimizer called HABC for optimal prediction of the droplet volume and velocity. The main idea of HABC is to develop an adaptive and cooperative scheme by combining life-cycle, Powell’s search and crossover-based social learning strategies for complex optimizations. HABC is a more biologically-realistic model that the reproduce and die dynamically throughout the foraging process and the population size varies as the algorithm runs. With the crossover operator, the information exchange ability of the bees can be enhanced in the early exploration phase while the Powell’s search enables the bees deeply exploit around the promising area, which provides an appropriate balance between exploration and exploitation. The proposed algorithm is benchmarked against other four state-of-the-art bio-inspired algorithms using both classical and CEC2005 test function suites. Then HABC is applied to predict the printing quality using nano-silver ink. Statistical analysis of all these tests highlights the significant performance improvement due to the beneficial combination and shows that the proposed HABC outperforms the reference algorithms.