Skip to main content
SpringerLink
Log in

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

  • Published:
Journal of Intelligent Manufacturing Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

References

  • Akbari, R., Hedayatzadeh, R., Ziarati, K., & Hassanizadeh, B. (2012). A multi-objective artificial bee colony algorithm. Swarm and Evolutionary Computation, 2, 39–52.

    Article  Google Scholar 

  • Alatas, B. (2010). Chaotic bee colony algorithms for global numerical optimization. Expert Systems with Applications, 37, 5682–5687.

    Article  Google Scholar 

  • Banharnsakun, A., Achalakul, T., & Sirinaovakul, B. (2011). The best-so-far selection in Artificial Bee Colony algorithm. Applied Soft Computing, 11(2), 2888–2901.

    Article  Google Scholar 

  • Basturk, B., & Karaboga, D. (2012). A modified artificial bee colony algorithm for real-parameter optimization. Information Sciences, 192, 120–142.

    Article  Google Scholar 

  • Biswas, S., Kundu, S., Das, S., & Vasilakos, A. V. (2013). Information sharing in bee colony for detecting multiple niches in non-stationary environments. In Blum, C. (Ed.), Proceeding of the fifteenth annual conference companion on genetic and evolutionary computation conference companion (GECCO 13 Companion), Amsterdam, The Netherlands, July 6–10, ACM, NY, USA, 2013, pp. 1–2.

  • Blackstock, D. T. (2000). Blackstock, fundamentals of physical acoustics. New York, NY: Wiley.

    Google Scholar 

  • Byung, J. K., & Je, J. H. (2010). Geometrical characterization of inkjet-printed conductive lines of nanosilver suspensions on a polymer substrate. Thin Solid Films, 518, 2890–2896.

    Article  Google Scholar 

  • Chen, H., Niu, B., Ma, L., et al. (2014). Bacterial colony foraging optimization. Neurocomputing, 137, 268–284.

  • Chen, M. H., Chang, P. C., & Lin, C. H. (2014). A self-evolving artificial immune system II with T-cell and B-cell for permutation flow-shop problem. Journal of Intelligent Manufacturing, 25(6), 1257–1270.

    Article  Google Scholar 

  • Cheung, C. L., Looi, T., Lendvay, T. S., Drake, J. M., & Farhat W. A. (2014). Use of 3-dimensional printing technology and silicone modeling in surgical simulation: Development and face validation in pediatric laparoscopic pyeloplasty. Journal of Surgical Education, 71(5),762–767.

  • Clerc, M., & Kennedy, J. (2002). The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation, 6(1), 58–73.

    Article  Google Scholar 

  • Coelho, L. S., & Alotto, P. (2011). Gaussian artificial bee colony algorithm approach applied to Loneys solenoid benchmark problem. IEEE Transactions on Magnetics, 47(5), 1326–1329.

    Article  Google Scholar 

  • Derrac, J., García, S., Molina, D., et al. (2011). A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm and Evolutionary Computation, 1(1), 3–18.

    Article  Google Scholar 

  • Dorigo, M., & Gambardella, L. M. (1997). Ant colony system: A cooperating learning approach to the travelling salesman problem. IEEE Transactions on Evolutionary Computation, 1(1), 53–66.

    Article  Google Scholar 

  • Gao, W., Liu, S., & Huang, L. (2013). A novel artificial bee colony algorithm based on modified search equation and orthogonal learning. IEEE Transactions on Cybernetics, 43(3), 1011–1024.

    Article  Google Scholar 

  • Gao, W., Liu, S., & Huang, L. (2013). A novel artificial bee colony algorithm with Powell’s method. Applied Soft Computing, 13(9), 3763–3775.

    Article  Google Scholar 

  • Hansen, N., & Ostermeier, A. (2001). Completely derandomized self-adaptation in evolution strategies. Evolutionary Computation, 9(2), 159–195.

    Article  Google Scholar 

  • Jaehyung, H., Alan, W., & Antoine, K. (2009). Energetics of metal-organic interfaces: New experiments and assessment of the field. Materials Science and Engineering: R: Reports, 64, 1–31.

    Article  Google Scholar 

  • Kahourzade, S., Mahmoudi, A., & Mokhlis, H. B. (2015). A comparative study of multi-objective optimal power flow based on particle swarm, evolutionary programming, and genetic algorithm. Electrical Engineering, 97(1), 1–12.

    Article  Google Scholar 

  • Kang, F., Li, J. J., & Ma, Z. Y. (2011). Rosenbrock artificial bee colony algorithm for accurate global optimization of numerical functions. Information Sciences, 181, 3508–3531.

    Article  Google Scholar 

  • Karaboga, D. (2005). An idea based on honey bee swarm for numerical optimization, Technical Report-TR06. Erciyes University, Engineering Faculty, Computer Engineering Department.

  • Karaboga, D., Akay, B., & Ozturk, C. (2007). Artificial bee colony (ABC) optimization algorithm for training feed-forward neural networks, Modeling decisions for artificial intelligence. Berlin: Springer.

    Google Scholar 

  • Karaboga, D., & Akay, B. (2009). A comparative study of artificial bee colony algorithm. Applied Mathematics and Computation, 214, 108–132.

    Article  Google Scholar 

  • Karaboga, D., & Basturk, B. (2007). A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (abc) algorithm. Journal of Global Optimization, 39(3), 459–471.

    Article  Google Scholar 

  • Karaboga, D., & Basturk, B. (2007). Artificial bee colony (ABC) optimization algorithm for solving constrained optimization problems. Lecture Notes in Computer Science., 4529, 789–798.

    Article  Google Scholar 

  • Kennedy, J., & Eberhart, R. C. (1995). Particle swarm optimization, In: Proceedings of the 1995 IEEE international conference on neural networks (Vol. 4, pp. 1942–1948).

  • Krink, T., & Løvbjerg, M. (2002). The lifecycle model: Combining particle swarm optimisation, genetic algorithms and hillclimbers, Parallel Problem Solving from Nature–PPSN VII. Berlin Heidelberg: Springer.

    Google Scholar 

  • Liang, J. J., Qin, A. K., Suganthan, P. N., & Baskar, S. (2006). Comprehensive learning particle swarm optimizer for global optimization ofmultimodal functions. IEEE Transactions on Evolutionary Computation, 10(3), 281–295.

    Article  Google Scholar 

  • Ma, L., Hu, K., Zhu, Y., et al. (2014). Discrete and continuous optimization based on hierarchical artificial bee colony optimizer. Journal of Applied Mathematics, 2014, 1–20.

  • Macdonald, E., Salas, R., Espalin, D., Perez, M., Aguilera, E., Muse, D., et al. (2014). 3D printing for the rapid prototyping of structural electronics. IEEE Access, 2, 234–242.

    Article  Google Scholar 

  • Niu, B., Zhu, Y. L., He, X. X., et al. (2008). A lifecycle model for simulating bacterial evolution. Neurocomputing, 72(1), 142–148.

    Article  Google Scholar 

  • Olivera, A. C., García-Nieto, J. M., & Alba, E. (2015). Reducing vehicle emissions and fuel consumption in the city by using particle swarm optimization. Applied Intelligence, 42(3), 389–405.

    Article  Google Scholar 

  • Pan, Q. K., Tasgetiren, M. F., Suganthan, P. N., & Chua, T. J. (2011). A discrete artificial bee colony algorithm for the lot-streaming flow shop scheduling problem. Information Sciences, 181, 2455– 2468.

    Article  Google Scholar 

  • Passino, K. M. (2002). Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Systems Magazine, 22, 52–67.

    Article  Google Scholar 

  • Powell, M. J. D. (1977). Restart procedures for the conjugate gradient method. Mathematical Programming, 12, 241–254.

    Article  Google Scholar 

  • Prasad, S., Horowitz, S., Gallas, Q., Sankar, B., Cattafesta, L., & Sheplak, M. (2002). Two-port electroacoustic model of an axisymmetric piezoelectric composite plate. In Proceedings of the 43rd AIAA/ASME/ASCE/AHS structures, structural dynamics, and materials conference, Denver, CO, USA, AIAA, 2002–1365.

  • Rashedi, E., Nezamabadi-pour, H., & Saryazdi, S. (2009). GSA: A gravitational search algorithm. Information Sciences, 179(13), 2232–2248.

    Article  Google Scholar 

  • Salomon, R. (1996). Reevaluating genetic algorithm performance under coordinate rotation of benchmark functions. A survey of some theoretical and practical aspects of genetic algorithms. Biosystems, 39, 263–278.

    Article  Google Scholar 

  • Seitz, H., & Heinzl, J. (2004). Modeling of a microfluidic device with piezoelectric actuators. Journal of Micromechanics and Microengineering, 14, 1140–1147.

    Article  Google Scholar 

  • Sheikhalishahi, M., Ebrahimipour, V., & Hosseinabadi Farahani, M. (2014). An integrated GA-DEA algorithm for determining the most effective maintenance policy for a k -out-of- n problem. Journal of Intelligent Manufacturing, 25(6), 1455–1462.

    Article  Google Scholar 

  • Singh, M., Haverinen, H. M., Dhagat, P., & Jabbour, G. E. (2010). Inkjet printing: Process and its applications. Advanced Materials, 22, 673–685.

    Article  Google Scholar 

  • Sumathi, S., Hamsapriya, T., & Surekha, P. (2008). Evolutionary intelligence: An introduction to theory and applications with matlab. New York: Springer.

    Google Scholar 

  • White, F. M. (1979). Fluid mechanics. New York, NY: McGraw-Hill, Inc.

    Google Scholar 

  • Yan, X., Zhu, Y., Zhang, H. et al. (2012). An adaptive bacterial foraging optimization algorithm with lifecycle and social learning. Discrete Dynamics in Nature and Society Article ID 409478, 20pp.

  • Yusup, N., Sarkheyli, A., Zain, A. M., Hashim, S. Z. M., & Ithnin, N. (2014). Estimation of optimal machining control parameters using artificial bee colony. Journal of Intelligent Manufacturing, 25(6), 1463–1472.

    Article  Google Scholar 

  • Zhu, G. P., & Kwong, S. (2010). Gbest-guided artificial bee colony algorithm for numerical function optimization. Applied Mathematics and Computation, 217(7), 3166–3173.

    Article  Google Scholar 

Download references

Acknowledgments

This research is partially supported by National Natural Science Foundation of China (Nos. 61174164, 61203161, and 51205389) and Liaoning Provincial Natural Science Foundation of China (2014010094-301).

Author information

Authors and Affiliations

  1. School of Mechanical Engineering, Beijing Institute of Technology, Beijing, 110081, China

    Shikai Jing

  2. Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China

    Lianbo Ma, Kunyuan Hu & Yunlong Zhu

  3. School of Computer Science and Software, Tianjin Polytechnic University, Tianjin, 300387, China

    Hanning Chen

Authors
  1. Shikai Jing
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lianbo Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kunyuan Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yunlong Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hanning Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hanning Chen.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jing, S., Ma, L., Hu, K. et al. A restructured artificial bee colony optimizer combining life-cycle, local search and crossover operations for droplet property prediction in printable electronics fabrication. J Intell Manuf 29, 109–134 (2018). https://doi.org/10.1007/s10845-015-1092-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10845-015-1092-y

Keywords

Search

Navigation