Author:
Gao Zheng-Ming,Zhao Juan,Yang Yu,Tian Xue-Jun
Abstract
Abstract
In this paper, we hybridize the grey wolf optimization (GWO) algorithm with the newly proposed slime mould algorithm (SMA). Comparisons had been made and three kinds of benchmark functions were introduced to verify the capability. 100 Monte Carlo simulation experiments had been carried on to reduce the influence of randomness as less as possible. Results showed that the performance of hybrid GWO-SMA would base on the given characteristics of problems themselves because of the random threshold parameter p and the multiple branches in the updating equation. The hybridization of the GWO and SMA might be not recommended for steady applications and engineering problems.
Subject
General Physics and Astronomy
Reference7 articles.
1. Ant colony optimization [J];Dorigo;Computational Intelligence Magazine, IEEE,2006
2. Particle Swarm Optimization for Design of Slotted MEMS Resonators With Low Thermoelastic Dissipation [J];Lake;Microelectromechanical Systems, Journal of,2014
3. Grey Wolf Optimizer [J];Mirjalili;Advances in Engineering Software,2014
4. Equilibrium optimizer: A novel optimization algorithm [J];Faramarzi;Knowledge-Based Systems,2019
5. Slime mould algorithm: A new method for stochastic optimization [J];Li;Future Generation Computer Systems,2020
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