EO是受控制体积质量平衡的启发来估计动态和平衡状态的。

EO is inspired by control volume mass balance to estimate both dynamic and equilibrium state.

在EO中，搜索智能体随机更新其浓度（位置），以寻找一些称为平衡候选者的天才粒子，以最终达到平衡状态作为最优结果。

In EO, search agents randomly update their concentration (Position) with respect to some talented particles called equilibrium candidates to finally reach to equilibrium state as optimal results.

用58个数学函数（包括单峰函数、多峰函数、混合函数和组合函数）以及3个工程基准问题验证了EO的性能，并将其性能与三类优化方法进行了比较；GA和PSO作为研究最为深入的元启发式方法，GWO、GSA和SSA是最近开发的算法，CMA-ES、SHADE和LSshade-SPACMA是高性能优化器。

EO’s performance was validated against 58 mathematical functions including unimodal, multimodal, hybrid and composition functions as well as 3 engineering benchmark problems and its performance was compared to three classes of optimization methods; GA and PSO as the most well-studied metaheuristics, GWO, GSA and SSA as recently developed algorithms and CMA-ES, SHADE and LSHADE-SPACMA as high performance optimizers.

综合统计分析表明，EO的性能明显优于PSO、GA、GWO、GSA、SSA和CMA-ES，其性能在统计学上与SHADE和LSHADE-SPACMA相似。

Comprehensive statistical analysis revealed that EO is able to significantly outperform PSO, GA, GWO, GSA, SSA and CMA-ES while its performance is statistically similar to SHADE and LSHADE-SPACMA.

参考文献：

Main paper: A. Faramarzi, M. Heidarinejad, B. Stephens, S. Mirjalili, Equilibrium optimizer: A novel optimization algorithm

published in Knowledge-Based Systems. DOI: https://doi.org/10.1016/j.knosys.2019.105190

If you do not have access to the paper, please send me an email and I will get back to you.

Email : afaramar@hawk.iit.edu or afshin.faramarzi@gmail.com

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