GMO: geometric mean optimizer for solving engineering problems

This paper introduces a new meta-heuristic technique, named geometric mean optimizer (GMO) that emulates the unique properties of operator in mathematics. can simultaneously evaluate fitness and diversity search agents space. In GMO, scaled objective values certain agent’s opposites is assigned to agent as its weight representing overall eligibility guide other process when solving an optimization problem. Furthermore, GMO has no parameter tune, contributing results be highly reliable. The competence problems verified via implementation on 52 standard benchmark test including 23 classical functions, 29 CEC2017 functions well nine constrained engineering problems. presented by are then compared with those offered several newly proposed popular algorithms. demonstrate significantly outperforms competitors vast range Source codes publicly available at https://github.com/farshad-rezaei1/GMO .