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| 高维优化问题的改进平衡优化器算法 |
| An improved equilibrium optimizer for high-dimensional optimization problems |
| 投稿时间：2024-01-31 修订日期：2024-07-30 |
| DOI： |
| 中文关键词: 元启发式 平衡优化器算法；高维问题；结构优化；外点惩罚函数法 |
| 英文关键词:Metaheuristic Equilibrium optimizer High-dimensional problems Structural optimization Exterior penalty function method |
| 基金项目:国家自然科学基金 |
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| 摘要点击次数: 60 |
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| 中文摘要: |
| 平衡优化器算法在求解高维优化问题时存在过早收敛和求解精度低等问题。提出了一种改进的平衡优化器算法，首先利用自适应权重策略调节搜索步长，提高算法的全局搜索能力；其次使用反向学习策略重新构造均衡池，维持候选解的多样性，防止算法过早收敛；最后引入随机挑选机制和高斯刷新算子，避免算法因单一的更新策略而陷入局部停滞，从而提高算法的求解精度。计算了多个高维标准函数及约束工程结构优化问题，利用外点惩罚函数法处理约束，并将改进的平衡优化器算法与其他智能优化算法结果进行对比。结果表明改进的平衡优化器算法具有更快的收敛速度和更高的精度。 |
| 英文摘要: |
| The equilibrium optimizer (EO) has the shortcomings of premature convergence and low accuracy in solving high-dimensional optimization problems. An improved equilibrium optimizer (IEO) is proposed. Firstly, an adaptive weight strategy is used to adjust the search step and improve the global search ability of the algorithm. Secondly, the opposition-based learning strategy is utilized to reconstruct the equilibrium pool and to maintain the diversity of candidate solutions, which prevents the algorithm from premature convergence. Finally, the random selection mechanism and gaussian refresh operator are applied to avoid the local stagnation caused by a single update strategy and to improve the accuracy of the algorithm. Several high-dimensional benchmark functions and constrained engineering structure optimization problems are solved. The exterior penalty function method is em-ployed to handle the constraints and the results of the improved equilibrium optimizer and other intelligent optimization algorithms are compared with each other. The results indicate that the IEO exhibits faster convergence and high accuracy. |
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