Seeding Programming

  • Vladimir MochalovEmail author
Conference paper


The work is aimed at formalizing the implementation of the steps of the new method “seeding programming” focused on solving some optimization problems. Michelangelo told that there is a statue in every stone and all that is needed is to be able to remove all unnecessary and to take the statue to light. Based on Michelangelo’s statement in the proposed method, we search for such a sequence of elements to remove from the original space (“stone”), which will lead to the formation of a set of remaining undeleted elements with the desired objective function. Initial elements of the search space either can be specified or they can be searched using special covering algorithms. To search for the sequence of elements to remove from the search space, we suggest to use search agents that form and use shared global memory.


Optimization method Knowledge-based multi-agent system Synthesis of solutions 


  1. 1.
    Mochalov VA (2015) Multi-agent bio-inspired algorithms for wireless sensor network design. In: Proceedings on IEEE 17th international conference on advanced communication technology, ICACT 2015, Phoenix Park, Korea, pp 34–42Google Scholar
  2. 2.
    Bonavear F, Dorigo M, Theraulaz G (1999) Swarm intelligence: from natural to artificial systems. Oxford University Press, New York, 320 pGoogle Scholar
  3. 3.
    Mochalov VA (2015) Synthesis of the wireless sensor network structure in the presence of physical attacks. Lect Notes Comput Sci 9247:11–22CrossRefGoogle Scholar
  4. 4.
    Mochalov VA, Pschenichnikov AP (2015) Functional scheme of the flying sensor networks architecture design. ICACT Trans Adv Commun Technol (TACT) 4(4):659–663. Scholar
  5. 5.
    Mochalov VA (2016) Certificate of registration of computer software “Program for synthesis of monitoring networks by bio-inspired algorithms” No. 2016612039Google Scholar
  6. 6.
    Mochalov VA, Mochalova AV (2017) Algorithms for changing the structure of geospace self-organizing question-answering sensor networks. In: VIII international conference “solar-terrestrial relations and physics of earthquake precursors”, p 11. Scholar
  7. 7.
    Mochalova AV, Mochalov VA (2017) Mathematical model of an ontological-semantic analyzer using basic ontological-semantic patterns. In: Lecture notes in artificial intelligence, proceedings of 15th Mexican international conference on artificial intelligence, pp 53–66CrossRefGoogle Scholar
  8. 8.
    Mochalova AV, Zacharov VP, Mochalov VA (2017) Ontology modification using ontological-semantic rules. In: 19th international conference on advanced communications technology (ICACT)—opening new era of smart society, pp 902–906Google Scholar
  9. 9.
    Kuznetsov VA, Mochalov VA, Mochalova AV (2016) International conference on advanced communication technology, ICACT, pp 651–658Google Scholar
  10. 10.
    Brabazon A, O’Neill M, McGarraghy S (2015) Natural computing algorithms. Springer, 554 pGoogle Scholar
  11. 11.
    Mandal JK, Mukhopadhyay S, Pal T (2016) Handbook of research on natural computing for optimization problems (Vols 2), Igi-Global, 1015 pGoogle Scholar
  12. 12.
    Fister I Jr, Xin-She Y, Fister I, Brest J, Fister D (2013) A brief review of nature-inspired algorithms for optimization. ELEKTROTEHNIˇSKI VESTNIK 80(3):1–7Google Scholar
  13. 13.
    Binitha S, Sathya S (2012) A survey of bio inspired optimization algorithms. Int J Soft Comput Eng (IJSCE) 2(2):137–151Google Scholar
  14. 14.
    Gounaris CE, Rajendran K, Kevrekidis IG, Floudas CA (2015) Designing networks: a mixed integer linear optimization approach, 56 p.
  15. 15.
    Taccari L (2015) Mixed-integer programming models and methods for bilevel fair network optimization and energy cogeneration planning. Ph.D. dissertation, 209 pGoogle Scholar
  16. 16.
    Fraccaroli E, Quaglia D Toolchain for optimal network synthesis.
  17. 17.
    Fowler RJ (1981) Optimal packing and covering in the plane are NP-complete. Inf Process Lett 12(3):133–137CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Institute of Cosmophysical Research and Radio Wave Propagation FEB RASKamchatka Region, Elizovskiy, ParatunkaRussia

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