Swarm intelligence in the problems of detecting anomalies and states of natural-technical systems and objects

A.V. Skatkov, A.A. Bryukhovetskiy, D.V. Moiseev, I. A. Skatkov

 Sevastopol State University, RF, Sevastopol, Universitetskaya St., 33

E-mail: dmitriymoiseev@mail.ru

DOI: 10.33075/2220-5861-2021-3-119-126

UDC 004.56                                                                


   The ideological basis of the proposed article is close to the position of Teilhard de Chardin, the founder of scientific creationism and the branch of philosophy – Teilardism, who calls for: “Express your uniqueness to promote global progress.”

   What follows from our proposed principle of building complex systems and objects: individual improvement is one of the few ways to collective prosperity and harmony. Uncontrolled human negative impact on the environment caused by human economic activities, especially those related to the emergence of new industrial productions, leads to a deterioration of the environmental situation and, as a result, to a decrease in the quality and standard of living of people. Under the increased influence of the negative human impact on the environment, a large number of local environmental disasters have occurred over the past decades. Currently, the problems related to the control of soil, air, sea and river water pollution are particularly urgent. In this regard, there is an objective need to develop methods and tools designed to implement a system of continuous monitoring of key environmental indicators and forecasting the occurrence of abnormal states of ecosystems. Therefore, the solution of the problem of detecting anomalies and states of natural and technical systems and objects is timely and relevant. The integrated use of operational monitoring, mathematical and simulation modeling tools using artificial intelligence methods will allow monitoring the state of the ecosystem and predicting the dynamics of its changes, warning about possible anomalies and thereby preventing the occurrence of critical situations.

   The article considers an approach to solving the problem of detecting and classifying anomalies and states of natural-technical systems and objects using swarm intelligence methods. The main directions of development of the proposed approach include ant algorithms, bee swarm algorithms, and the particle swarm method. The structure of the swarm intelligence system of decision support based on collective preference rules is proposed. The application of the proposed approach allows optimizing the processes of processing, analysis, integration of heterogeneous data, increasing the sensitivity, reliability and efficiency of decisions made.

Keywords: unmanned vehicle, adaptive model, vulnerability detection, classification of information states, assessment matrix.

To quote:

Full text in PDF(RUS)


  1. Gajskij V.A., and Gajskij P.V. Mnogomernyj garmonicheskij analiz pri izmereniyah polej morskoj sredy (Multidimensional harmonic analysis for measurements of marine environment). Monitoring systems of environment, 2019, No. 4 (38), pp. 33–42. DOI: 10.33075/2220-5861-2019-4-33-42
  2. Skatkov A.V., Bryuhoveckij A.A., and Moiseev D.V.,. and SHishkin Yu.E. Mera Kul’baka v zadachah dinamicheskoj klasterizacii nablyudenij sostoyaniya okruzhayushchej sredy (Measure of Kulbak in problems of dynamic clustering of observations of the environmental state). Monitoring systems of environment, 2019, No. 3 (37), pp. 35–38. DOI: 10.33075/2220-5861-2019-3-35-38
  3. Bryuhoveckij A.A., Skatkov A.V., and SHishkin Yu.E. Modelirovanie processov obnaruzheniya anomalij v slozhno-strukturirovannyh dannyh monitoringa (Modeling of anomaly detection processes in complex structured monitoring data). Monitoring systems of environment, 2017, No. 9 (29), pp. 45–49. DOI: 10.33075/2220-5861-2017-3-45-49
  4. Agarwal D. Detecting anomalies in cross-classified streams: a bayesian approach. Knowledge and Information Systems, 2006, Vol. 11, No. 1, pp. 29–44.
  5. Gevrey M., Dimopoulos I., and Lek S. Review and comparison of methods to study the contribution of variables in artificial neural network models. Ecological Modelling, 2003, Vol. 160, No. 3, pp. 249–264.
  6. Chan P.K., and Mahoney M.V. Modeling multiple time series for anomaly detection. In Proceedings of the Fifth IEEE International Conference on Data Mining. IEEE Computer Society, Washington, USA, 2005, pp. 90–97.
  7. Socha K., and Dorigo M. Ant Colony Optimization for Continuous Domains. Technical Report TR/IRIDIA/2005-037. Bruxelles: Universite Libre de Bruxelles, 2005, 34 p.
  8. Kong M., Tian P., and Jiao L. Application of ACO in Continuous Domain: ICNC 2006, LNCS 4222. Part II. Berlin: Springer-Verlag, 2006, pp. 126–135.
  9. Karaboga D., and Basturk B. On the performance of artificial bee colony (ABC) algorithm. Applied Soft Computing, 2008, Vol. 8, pp. 687–697.
  10. Nazin A.V. O povyshenii effektivnosti avtomatnyh algoritmov adaptivnogo vybora variantov. Adaptaciya i obuchenie v sistemah upravleniya i prinyatiya reshenij. Novosibirsk: Nauka. 1982. 208 p.
  11. Skatkov A.V. Komplementarnoe detektirovanie atak v telekommunikacionnyh sistemah kriticheskogo primeneniya. Information technologies in education, science and production, 2013, No. 4 (5), pp.136–146.
  12. Skatkov A.V., Bryuhoveckij A.A., Moiseev D.V. Adaptivnyj metod obnaruzheniya uyazvimostej interfejsov bespilotnyh transportnyh sredstv v infrastrukture umnogo goroda. Infokommunikacionnye tekhnologii, 2020, Vol. 18, No. 1, pp. 45–50.