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This article presents a novel optimization algorithm belonging to the class of swarm intelligence optimization methods.
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8 p.
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Abstract: A large number of optimization algorithms have been developed by researchers to solve a variety of com- plex 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 pro- cess 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 βJ , (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 ex- ploits 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. A comparison with genetic algorithms (GA), simulated annealing (SA) as well as with a multiagent version of SA is presented in terms of efficacy in finding optimal solutions. In all cases our method outperforms the others, particularly in presence of limited knowledge of the agent.
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De Vincenzo, Ilario; Massari, Giovanni F.; Giannoccaro, Ilaria; Carbone, Giuseppe & Grigolini, Paolo.Mimicking the collective intelligence of human groups as an optimization tool for complex problems,
article,
April 4, 2018;
Amsterdam, The Netherlands.
(https://digital.library.unt.edu/ark:/67531/metadc1152236/:
accessed June 8, 2024),
University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu;
crediting UNT College of Science.
