The CutMAG as a New Hybrid Method for Multi-edge Grinder Design Optimisation

  • Jacek M. CzerniakEmail author
  • Marek Macko
  • Dawid Ewald
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 401)


This article is a part of the series dedicated to AI Methods Inspired by Nature and their implementation in the mechatronic systems. The CutMAG algorithm uses hybrid approach to optimisation, i.e. a combination of classic genetic algorithms (GA) with morphologic optimisation (M) thus creating innovative approach to optimisation of cutting disk design (Cut) for the multi-edge grinder. The input data include population of individuals. Each individual is represented by a set of cutting disks. Whereas the fitness function was assumed as a combination of several postulates of the mechanical design foundations. The method includes mechanical, design and energy aspects. Each individual constitutes a complete solution of the disk set whereas the population represents the entire class of solutions. The fitness function of an individual is calculated as the average fitness of each disk supplemented by information describing the relationship between both adjacent disks. The method for calculation of function values was selected so as to ensure its maximisation in the process of evolution. Although promising results of the genetic algorithms operation were achieved, one can consider further improvement of the method efficiency. The authors used morphological operations in order to better adopt the method to the task.


Genetic Algorithm Particle Swarm Optimisation Fitness Function Morphological Operation Adjacent Disk 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Angryk, R., Czerniak, J.: Heuristic algorithm for interpretation of multi-valued attributes in similarity-based fuzzy relational databases. Int. J. Approximate Reasoning 51(8), 895–911 (2010)CrossRefGoogle Scholar
  2. 2.
    Apiecionek, L., Czerniak, J., Dobrosielski, W.: Quality of Services Method as a DDoS Protection Tool. Advances in Intelligent Systems and Computing. Springer, Berlin (2015)CrossRefGoogle Scholar
  3. 3.
    Apiecionek, L., Czerniak, J., Zarzycki, H.: Protection tool for distributed denial of services attack. Commun. Comput. Inf. Sci. Springer 424, 405–414 (2014)CrossRefGoogle Scholar
  4. 4.
    Chan, F.T.S., Au, K.C., Chan, L.Y., Lau, T.L.: Using genetic algorithms to solve quality-related bin packing problem. Robot. Comput. Integr. Manuf. 23, 71–81 (2007)Google Scholar
  5. 5.
    Ciesielska, D.: Ocena wpywu techniki rozdrabniania odpadw z tworzyw termoplastycznych na struktur i waciwoci otrzymywanych recyklatw. Archiwum Technologii Maszyn i Automatyzacji. 25(1), 169–178 (2005)Google Scholar
  6. 6.
    Czerniak, J.: Evolutionary approach to data discretization for rough sets theory. Fundamenta Informaticae 92(1–2), 43–61 (2009)MathSciNetGoogle Scholar
  7. 7.
    Czerniak, J., Dobrosielski, W., Zarzycki, H., Apiecionek, L.: A Proposal of the New Owlant Method for Determining the Distance Between Terms in On-tology. Advances in Intelligent Systems and Computing. Springer, Berlin (2015)Google Scholar
  8. 8.
    Czerniak, J.M. Apiecionek, Ł., Zarzycki, H.: Application of ordered fuzzy numbers in a new ofnant algorithm based on ant colony optimization. Commun. Comput. Inf. Sci. Springer 424, 259–270 (2014)Google Scholar
  9. 9.
    De Jong, K., Spears, W.: Using genetic algorithms to solve np complete problems. In: Schaffer, J.D. (ed.) Proceeding of the 3th International Conference on Genetic Algorithms, pp. 124–132 (1989)Google Scholar
  10. 10.
    Ewald, D., Czerniak, J., Zarzycki, H.: Approach to Solve a Criteria Problem of the ABC algorithm used to the WBDP Multicriteria Optimization. Advances in Intelligent Systems and Computing. Springer, Berlin (2015)CrossRefGoogle Scholar
  11. 11.
    Farzanegan, A., Vahidipour, S.: Optimization of comminution circuit simulations based on genetic algorithms search method. Miner. Eng. 22, 719–726 (2009)CrossRefGoogle Scholar
  12. 12.
    Flizikowski, J., Bieniaszewski, W., Macko, M.: Integron and innovation algorithm of the cereal grain grinder construction. In: Proceedings of the TMCE 2006 Ljubljana, Slovenia (2006)Google Scholar
  13. 13.
    Flizikowski, J., Kamyk, W.: Algorytmy genetyczne w konstrukcji rozdrabniaczy wielotarczowych ziarna kukurydzy. Inynieria Maszyn 22 (2005)Google Scholar
  14. 14.
    Flizikowski, J., Macko, M.: Method for estimating the efficiency of quasi cutting of recycled optical telecommunications pipes. Polimery 46(1), 53 (2001)Google Scholar
  15. 15.
    Jekiel, J., Tam, E.K.L.: Plastics waste processing: comminution size distribution and prediction. Envir. Eng. 133(2), 245–254 (2007)CrossRefGoogle Scholar
  16. 16.
    Macko, M.: Aspekty poboru mocy w rozdrabniaczach wielokrawdziowych. Inynieria i Aparatura Chemiczna (4), 103–104 (2006)Google Scholar
  17. 17.
    Macko, M., Flizikowski, J., Zych, G.: Konstruowanie rozdrabniaczy w recyklingu z zastosowaniem systemw ekspertowych—re-cykling materiaw polimerowych. Nauka—Przemys (2003)Google Scholar
  18. 18.
    Marbac-Lourdelle, M.: Model-based clustering for categrocial and mixed data sets. Statitics, Universite de Lille 1 (2014)Google Scholar
  19. 19.
    Powell, M., Morrison, R.: The future of comminution modelling. Int. J. Miner. Process. 84, 228–239 (2007)CrossRefGoogle Scholar
  20. 20.
    Quagliarella, D.: Genetic Algorithms and Evolution Strategy in Engineering and Computer Science: Recent Advances and Industrial Applications. Wiley, New York (1998)Google Scholar
  21. 21.
    Rutkowska, D., Piliński, M., Rutkowski, L.: Sieci neuronowe, algorytmy genetyczne i systemy rozmyte. PWN, Warszawa (1999)Google Scholar
  22. 22.
    Sadrai, S., Meech, J., Ghomshei, M., Sassani, F., Tromans, D.: Influence of impact velocity on fragmentation and the energy efficiency of comminution. Int. J. Impact Eng. 33, 723–734 (2006)CrossRefGoogle Scholar
  23. 23.
    Sameon, D., Shamsuddin, S.M. and Sallehuddin, R., Zainal, A.: Compact classification of optimized boolean, reasoning with particle swarm optimization. Intelligent Data Analysis 16 IOS Press, pp. 915–931 (2012)Google Scholar
  24. 24.
    Shuiping, L., Hongzan, B., Zhichu, H., Jianzhong, W.: Nonlinear comminution process modeling based on ga-fnn in the computational comminution system. J. Mater. Process. Technol. 120, 84–89 (2002)CrossRefGoogle Scholar
  25. 25.
    Sikora, R.: Przetwrstwo tworzyw polimerowych—podstawy logiczne, formalne i terminologiczne. Wyd. Polit. Lubelskiej (2006)Google Scholar
  26. 26.
    Woldt, D., Schubert, G., Jäckel, H.G.: Size reduction by means of low-speed rotary shears. Int. J. Miner. Process. 74S, 405–415 (2004)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Casimir the Great University in Bydgoszcz, Institute of TechnologyBydgoszczPoland
  2. 2.Institute of Mechanics and Applied Computer ScienceCasimir the Great University in BydgoszczBydgoszczPoland

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