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A large number of optimization algorithms have been developed by researchersto solve a variety of complex problems in operations management area. Wepresent a novel optimization algorithm belonging to the class of swarmintelligence optimization methods. The algorithm mimics the decision makingprocess of human groups and exploits the dynamics of this process as anoptimization tool for combinatorial problems. In order to achieve this aim, acontinuous-time Markov process is proposed to describe the behavior of apopulation of socially interacting agents, modelling how humans in a groupmodify their opinions driven by self-interest and consensus seeking. As in thecase of a collection of spins, the dynamics of such a system is characterizedby a phase transition from low to high values of the overall consenus(magnetization). We recognize this phase transition as being associated withthe emergence of a collective superior intelligence of the population. Whilethis state being active, a cooling schedule is applied to make agents closerand closer to the optimal solution, while performing their random walk on thefitness landscape. A comparison with simulated annealing as well as with amulti-agent version of the simulated annealing is presented in terms ofefficacy in finding good solution on a NK - Kauffman landscape. In all casesour method outperforms the others, particularly in presence of limitedknowledge of the agent.

The Human Group Optimizer (HGO): Mimicking the collective intelligence of human groups as an optimization tool for combinatorial problems