On Emergent Function Optimization with Multi-agent Search

Abstract

This article presents a multi-agent search algorithm for function optimization, conducted by multiple agents. Those agents perform local search tasks repeatedly, updating their starting points according to the previous results, and attempt to identify optimal solutions collectively. During the course of the collective optimization, some agents merge into a single one to suppress redundant search processes.