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Mimicking the collective intelligence of human groups as an optimization tool for complex problems

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  • De Vincenzo, Ilario
  • Massari, Giovanni F.
  • Giannoccaro, Ilaria
  • Carbone, Giuseppe
  • Grigolini, Paolo
Abstract
A large number of optimization algorithms have been developed by researchers to solve a variety of complex problems in operations management area. We present a novel optimization algorithm belonging to the class of swarm intelligence optimization methods. The algorithm mimics the decision making process of human groups and exploits the dynamics of such a process as a tool for complex combinatorial problems. In order to achieve this aim, we employ a properly modified version of a recently published decision making model [64,65], to model how humans in a group modify their opinions driven by self-interest and consensus seeking. The dynamics of such a system is governed by three parameters: (i) the reduced temperature βJ, (ii) the self-confidence of each agent β′, (iii) the cognitive level 0 ≤ p ≤ 1 of each agent. Depending on the value of the aforementioned parameters a critical phase transition may occur, which triggers the emergence of a superior collective intelligence of the population. Our algorithm exploits such peculiar state of the system to propose a novel tool for discrete combinatorial optimization problems. The benchmark suite consists of the NK - Kauffman complex landscape, with various sizes and complexities, which is chosen as an exemplar case of classical NP-complete optimization problem.

Suggested Citation

  • De Vincenzo, Ilario & Massari, Giovanni F. & Giannoccaro, Ilaria & Carbone, Giuseppe & Grigolini, Paolo, 2018. "Mimicking the collective intelligence of human groups as an optimization tool for complex problems," Chaos, Solitons & Fractals, Elsevier, vol. 110(C), pages 259-266.
    • Handle: RePEc:eee:chsofr:v:110:y:2018:i:c:p:259-266
    DOI: 10.1016/j.chaos.2018.03.030
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    Cited by:

    1. Wibowo, Ferry Wahyu & Sediyono, Eko & Purnomo, Hindriyanto Dwi, 2022. "Chimpanzee leader election optimization," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 201(C), pages 68-95.
    2. Massari, Giovanni F. & Giannoccaro, Ilaria & Carbone, Giuseppe, 2019. "Are distrust relationships beneficial for group performance? The influence of the scope of distrust on the emergence of collective intelligence," International Journal of Production Economics, Elsevier, vol. 208(C), pages 343-355.
    3. Wasniewski, Krzysztof, 2020. "Energy efficiency as manifestation of collective intelligence in human societies," Energy, Elsevier, vol. 191(C).
    4. Namjun Cha & Junseok Hwang & Eungdo Kim, 2020. "The optimal knowledge creation strategy of organizations in groupthink situations," Computational and Mathematical Organization Theory, Springer, vol. 26(2), pages 207-235, June.
    5. Rafał Olszowski & Marcin Chmielowski, 2020. "Collective Intelligence in Polish-Ukrainian Internet Projects. Debate Models and Research Methods," Future Internet, MDPI, vol. 12(6), pages 1-20, June.

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