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- W4310263555 abstract "Abstract This paper introduces a new meta-heuristic technique, named Geometric Mean Optimizer (GMO) that emulates the unique properties of the geometric mean operator in mathematics. This operator can simultaneously evaluate the fitness and diversity of the search agents in the search space. In GMO, the geometric mean of the scaled objective values of a certain agent’s opposites is assigned to that agent as its weight representing its overall eligibility to guide the other agents in the search process when solving an optimization problem. Furthermore, the GMO has no parameter to tune, contributing its results to be highly reliable. The competence of the GMO in solving optimization problems is verified via implementation on 52 standard benchmark test problems including 23 classical test functions, 29 CEC2017 test functions as well as nine constrained engineering problems. The results presented by the GMO are then compared with those offered by several newly-proposed and popular meta-heuristic algorithms. The results demonstrate that the GMO significantly outperforms its competitors on a vast range of the problems." @default.
- W4310263555 created "2022-11-30" @default.
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- W4310263555 date "2022-11-28" @default.
- W4310263555 modified "2023-10-18" @default.
- W4310263555 title "GMO: Geometric Mean Optimizer for Solving Engineering Problems" @default.
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- W4310263555 doi "https://doi.org/10.21203/rs.3.rs-2052464/v1" @default.
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