From Collective Intelligence to Global Optimisation: An Agent-based Model Approach

Drawing inspiration from online question-and-answer (Q&A) communities often regarded as embodiments of Collective Intelligence (CI), this study investigates the dynamics of reputation-driven and distributed network interactions in multi- agent systems as a model for problem-solving in global optimisation. We explore the interplay among diverse participants, including Solvers motivated by reputation and Users seeking net benefits, recognising its critical role in fostering success within these communities. Our study translates the principles of CI inherent in these interactions into a novel agent-based search algorithm for unconstrained optimisation of continuous-valued cost functions. Empirical testing across a suite of established benchmark problems allows a comparative analysis of its perfor- mance against alternative agent-based methodologies. These findings underscore the algorithm’s advantages across diverse optimisation 2D landscapes, highlighting the potential of the CI framework as a promising avenue in metaheuristic research. They illustrate how the interaction between individual actors and the collective, favours the emergence of global solutions in unknown environments, mirroring similar emergent phenomena observed in social organisations.