DHRDE:基于双群体混合更新和 RPR 机制的工程应用差分进化算法

IF 6.9 1区 工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY
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引用次数: 0

摘要

本文提出了一种基于双种群混合更新和随机种群替换策略(即RPR机制)的增强型差分进化算法,称为DHRDE。DHRDE算法主要有三方面的改进:第一,在更新阶段前根据原始种群构建精英反向种群,以挖掘更多潜在的搜索区域。第二,在差分进化算法的 DE/rand/2 方法中加入扰动机制以提高搜索效率,建立随机个体和局部最优个体共同领导的两种更新模型,然后采用双种群混合更新策略实现全方位、多角度搜索。第三,利用 RPR 机制对部分种群进行多类型突变,进一步提高了收敛精度。为了验证所提算法的有效性,DHRDE 与多种不同类型的算法在 CEC2017、CEC2020 和 CEC2022 测试集的多维度上进行了比较,并通过 Wilcoxon 秩和检验和 Friedman 检验进行了统计分析。结果表明,DHRDE 算法具有更好的性能。本文还利用 DHRDE 算法解决了 7 个工程设计问题和 3 个光伏模型参数估计问题,优化结果表明 DHRDE 算法适用于不同的复杂问题,并具有有效性。此外,本文建立了多尺寸机器人的平滑路径规划模型,并使用 DHRDE 求解该模型,五组仿真实验结果表明,DHRDE 算法能提供平滑度更高且路径更短的机器人移动轨迹。通过热图分析和比较适配度指标,对比研究表明 DHRDE 算法在求解多尺寸机器人平滑路径规划模型时比其他算法更有优势,更强。以上结果表明,DHRDE 算法在解决工程应用优化问题方面具有更好的性能,具有很大的优势和竞争力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
DHRDE: Dual-population hybrid update and RPR mechanism based differential evolutionary algorithm for engineering applications

In this paper, an enhanced differential evolution algorithm based on dual population hybrid update and random population replacement strategy (namely RPR mechanism) is proposed, which is called DHRDE. DHRDE algorithm involves three key improvements, first, the elite reverse population is constructed according to the original population before the update phase to uncover more potential areas to be searched. Second, a perturbation mechanism is integrated into the DE/rand/2 approach of the differential evolution algorithm to bolster its search efficiency, two updating models are established using co-leadership of random and locally optimal individuals, and then dual-population hybrid update strategy is adopted to achieve all-round and multi-angle search. Thirdly, using RPR mechanism to operate multiple types of mutations on some populations further improves the convergence accuracy. In order to verify the effectiveness of the proposed algorithm, DHRDE is compared with a variety of different types of algorithms in multi-dimension of the CEC2017, CEC2020 and CEC2022 test set, and statistical analysis is performed by Wilcoxon rank sum test and Friedman test. The results show that DHRDE algorithm has better performance. DHRDE algorithm is also used to solve seven engineering design problems and three PV model parameter estimation problems, the optimization results show that DHRDE algorithm is suitable for different complex problems and has effectiveness. In addition, this paper establishes a smooth path planning model for multi-size robots, and uses DHRDE to solve the model, the results of five groups of simulation experiments show that DHRDE algorithm can provide robot moving trajectories with higher smoothness and shorter paths. Analyzing and comparing the fitness metrics through heat maps, the comparative study demonstrates that the DHRDE algorithm is more advantageous and stronger than other algorithms in solving the smooth path planning model for multi-size robots. The above results show that DHRDE algorithm has better performance and has great advantages and competitiveness in solving engineering application optimization problems.

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来源期刊
CiteScore
12.70
自引率
15.30%
发文量
719
审稿时长
44 days
期刊介绍: Computer Methods in Applied Mechanics and Engineering stands as a cornerstone in the realm of computational science and engineering. With a history spanning over five decades, the journal has been a key platform for disseminating papers on advanced mathematical modeling and numerical solutions. Interdisciplinary in nature, these contributions encompass mechanics, mathematics, computer science, and various scientific disciplines. The journal welcomes a broad range of computational methods addressing the simulation, analysis, and design of complex physical problems, making it a vital resource for researchers in the field.
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