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

TL;DR

The paper introduces the Human Group Optimizer (HGO), a novel swarm intelligence algorithm inspired by human group decision-making, which effectively solves complex combinatorial problems by modeling social interactions and collective intelligence dynamics.

Contribution

It presents a new optimization algorithm that mimics human group decision processes using a continuous-time Markov model, outperforming traditional methods like simulated annealing.

Findings

HGO outperforms simulated annealing in solution quality.

The algorithm effectively models social influence and consensus dynamics.

HGO is robust with limited agent knowledge.

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 this process as an optimization tool for combinatorial problems. In order to achieve this aim, a continuous-time Markov process is proposed to describe the behavior of a population of socially interacting agents, modelling how humans in a group modify their opinions driven by self-interest and consensus seeking. As in the case of a collection of spins, the dynamics of such a system is characterized by a phase transition from low to high values of the overall consenus (magnetization). We recognize this phase transition as being associated with the emergence of a collective superior intelligence of the population. While this state being active, a cooling schedule is applied to make agents closer and closer to the optimal solution, while performing their random walk on the fitness landscape. A comparison with simulated annealing as well as with a multi-agent version of the simulated annealing is presented in terms of efficacy in finding good solution on a NK - Kauffman landscape. In all cases our method outperforms the others, particularly in presence of limited knowledge of the agent.