Equilibrium optimizer: A novel optimization algorithm
Equilibrium optimizer: A novel optimization algorithm
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DOI：
10.1016/j.knosys.2019.105190
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发表时间：
2020-03-05
影响因子：
8.8
通讯作者：
Mirjalili, Seyedali
中科院分区：
文献类型：
--
作者：
Faramarzi, Afshin;Heidarinejad, Mohammad;Mirjalili, Seyedali
本文提出了一种新的，优化算法称为平衡优化（EO），控制体积质量平衡模型的启发，用于估计动态和平衡状态。在EO中，每个粒子（溶液）都有其浓度（位置）。作为一个搜索代理。搜索代理随机地更新其关于迄今为止最好的解决方案（即均衡候选者）的浓度，以最终达到均衡状态（最优结果）。一个定义良好的“生成率”的长期被证明是搞活EO的能力，在勘探，开发和避免局部极小值。该算法是基准与58单峰，多峰，和组合功能和三个工程应用问题。将EO的结果与三类现有的优化方法进行比较，包括：（i）最著名的元算法，包括遗传算法（GA）、粒子群优化算法（PSO）;（ii）最近开发的算法，包括灰狼优化算法（GWO）、引力搜索算法（GSA）和Salp群算法（SSA）;以及（iii）高性能优化器，包括CMA-ES、SHADE和LSHADE-SPACMA。使用Friedman检验的平均秩，对于所有58个数学函数，EO能够分别优于PSO，GWO，GA，GSA，SSA和CMA-ES 60%，69%，94%，96%，77%和64%，而SHADE和LSHADE-SPACMA分别优于24%和27%。对所有函数的Bonferroni-Dunn和霍尔姆检验表明，EO算法的性能明显优于PSO、GWO、GA、GSA、SSA和CMA-ES算法，而其性能与SHADE和LSHADE-SPACMA算法在统计学上相似。EO的源代码可在https：//github.comiafshinfaramarzi/Equilibrium-Optimizer、http://builtenvi.com/portfolio/equilibrium-optimizer/和http://www.alimirjalili.com/SourceCodes/EOcode.zip上公开获得。(C)2019爱思唯尔B. V.保留所有权利。
This paper presents a novel, optimization algorithm called Equilibiium Optimizer (EO), inspired by control volume mass balance models used to estimate both dynamic and equilibrium states. In EO, each particle (solution) with its concentration (position) acts . as a search' agent. The search agents randomly update their concentration with respect to best-so-far solutions, namely equilibrium candidates, to finally reach to the equilibrium state (optimal result). A well-defined "generation rate" term is proved to invigorate EO's ability in exploration, exploitation, and local minima avoidance. The proposed algorithm is benchmarked with 58 unimodal, multimodal, and composition functions and three engineering application problems. Results of EO are compared to three categories of existing optimization methods, including: (i) the most well-known meta-heuristics, including Genetic Algorithm (GA), Particle Swarm Optimization (PSO); (ii) recently developed algorithms, including Grey Wolf Optimizer (GWO), Gravitational Search Algorithm (GSA), and Salp Swarm Algorithm (SSA); and (iii) high performance optimizers, including CMA-ES, SHADE, and LSHADE-SPACMA. Using average rank of Friedman test, for all 58 mathematical functions EO is able to outperform PSO, GWO, GA, GSA, SSA, and CMA-ES by 60%, 69%, 94%, 96%, 77%, and 64%, respectively, while it is outperformed by SHADE and LSHADE-SPACMA by 24% and 27%, respectively. The Bonferroni-Dunn and Holm's tests for all functions showed that EO is significantly a better algorithm than PSO, GWO, GA, GSA, SSA and CMA-ES while its performance is statistically similar to SHADE and LSHADE-SPACMA. The source code of EO is publicly availabe at https://github.comiafshinfaramarzi/Equilibrium-Optimizer, http://builtenvi.com/portfolio/equilibrium-optimizer/ and http://www.alimirjalili.com/SourceCodes/EOcode.zip. (C) 2019 Elsevier B.V. All rights reserved.