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

Ilario De Vincenzo, Giovanni F. Massari, Ilaria Giannoccaro, Giuseppe Carbone and Paolo Grigolini

Chaos, Solitons & Fractals, 2018, vol. 110, issue C, 259-266

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.

Keywords: Optimization algorithm; Artificial intelligence; Collaborative decisions; Decision making; Group decision; Social interactions; Complexity; Markov chains (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:110:y:2018:i:c:p:259-266

DOI: 10.1016/j.chaos.2018.03.030

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