Thetraditional first-order reliability method(FORM)often encounters challengeswith non-convergence of results or excessive calculation when analyzing complex engineering problems.To improve the global convergence spee...Thetraditional first-order reliability method(FORM)often encounters challengeswith non-convergence of results or excessive calculation when analyzing complex engineering problems.To improve the global convergence speed of structural reliability analysis,an improved coati optimization algorithm(COA)is proposed in this paper.In this study,the social learning strategy is used to improve the coati optimization algorithm(SL-COA),which improves the convergence speed and robustness of the newheuristic optimization algorithm.Then,the SL-COAis comparedwith the latest heuristic optimization algorithms such as the original COA,whale optimization algorithm(WOA),and osprey optimization algorithm(OOA)in the CEC2005 and CEC2017 test function sets and two engineering optimization design examples.The optimization results show that the proposed SL-COA algorithm has a high competitiveness.Secondly,this study introduces the SL-COA algorithm into the MPP(Most Probable Point)search process based on FORM and constructs a new reliability analysis method.Finally,the proposed reliability analysis method is verified by four mathematical examples and two engineering examples.The results show that the proposed SL-COA-assisted FORM exhibits fast convergence and avoids premature convergence to local optima as demonstrated by its successful application to problems such as composite cylinder design and support bracket analysis.展开更多
To improve the wear resistance of aluminum alloy frictional parts, Ti B2particles reinforced Ni-base alloy composite coatings were prepared on aluminum alloy 7005 by laser cladding. The microstructure and tribological...To improve the wear resistance of aluminum alloy frictional parts, Ti B2particles reinforced Ni-base alloy composite coatings were prepared on aluminum alloy 7005 by laser cladding. The microstructure and tribological properties of the composite coatings were investigated. The results show that the composite coating contains the phases of Ni Al, Ni3Al, Al3Ni2, TiB2, TiB, TiC, CrB, and Cr23C6.Its microhardness is HV0.5855.8, which is 15.4 % higher than that of the Ni-base alloy coating and is 6.7 times as high as that of the aluminum alloy. The friction coefficients of the composite coatings are reduced by 6.8 %–21.6 % and 13.2 %–32.4 % compared with those of the Ni-base alloy coatings and the aluminum alloys, while the wear losses are 27.4 %–43.2 % less than those of the Ni-base alloy coatings and are only 16.5 %–32.7 % of those of the aluminum alloys at different loads. At the light loads ranging from 3 to 6 N, the calculated maximum contact stress is smaller than the elastic limit contact stress. The wear mechanism of the composite coatings is micro-cutting wear, but changes into multi-plastic deformation wear at 9 N due to the higher calculated maximum contact stress than the elastic limit contact stress. As the loads increase to 12 N, the calculated flash temperature rises to 332.1 °C.The composite coating experiences multi-plastic deformation wear, micro-brittle fracture wear, and oxidative wear.展开更多
In this paper,a multi-strategy improved coati optimization algorithm(MICOA)for engineering applications is proposed to improve the performance of the coati optimization algorithm(COA)in terms of convergence speed and ...In this paper,a multi-strategy improved coati optimization algorithm(MICOA)for engineering applications is proposed to improve the performance of the coati optimization algorithm(COA)in terms of convergence speed and convergence accuracy.First,a chaotic mapping is applied to initial-ize the population in order to improve the quality of the population and thus the convergence speed of the algorithm.Second,the prey’s position is improved during the prey-hunting phase.Then,the COA is combined with the particle swarm optimization(PSO)and the golden sine algorithm(Gold-SA),and the position is updated with probabilities to avoid local extremes.Finally,a population decreasing strategy is applied as a way to improve the performance of the algorithm in a comprehen-sive approach.The paper compares the proposed algorithm MICOA with 7 well-known meta-heuristic optimization algorithms and evaluates the algorithm in 23 test functions as well as engineering appli-cation.Experimental results show that the MICOA proposed in this paper has good effectiveness and superiority,and has a strong competitiveness compared with the comparison algorithms.展开更多
The power sector is an important factor in ensuring the development of the national economy.Scientific simulation and prediction of power consumption help achieve the balance between power generation and power consump...The power sector is an important factor in ensuring the development of the national economy.Scientific simulation and prediction of power consumption help achieve the balance between power generation and power consumption.In this paper,a Multi-strategy Hybrid Coati Optimizer(MCOA)is used to optimize the parameters of the three-parameter combinatorial optimization model TDGM(1,1,r,ξ,Csz)to realize the simulation and prediction of China's daily electricity consumption.Firstly,a novel MCOA is proposed in this paper,by making the following improvements to the Coati Optimization Algorithm(COA):(ⅰ)Introduce improved circle chaotic mapping strategy.(ⅱ)Fusing Aquila Optimizer,to enhance MCOA's exploration capabilities.(ⅲ)Adopt an adaptive optimal neighborhood jitter learning strategy.Effectively improve MCOA escape from local optimal solutions.(ⅳ)Incorporating Differential Evolution to enhance the diversity of the population.Secondly,the superiority of the MCOA algorithm is verified by comparing it with the newly proposed algorithm,the improved optimiza-tion algorithm,and the hybrid algorithm on the CEC2019 and CEC2020 test sets.Finally,in this paper,MCOA is used to optimize the parameters of TDGM(1,1,r,ξ,Csz),and this model is applied to forecast the daily electricity consumption in China and compared with the predictions of 14 models,including seven intelligent algorithm-optimized TDGM(1,1,r,ξ,Csz),and seven forecasting models.The experimental results show that the error of the proposed method is minimized,which verifies the validity of the proposed method.展开更多
Optimization problems are crucial for a wide range of engineering applications,as efficient solutions lead to better performance.This study introduces an improved coati optimization algorithm(ICOA)that overcomes the p...Optimization problems are crucial for a wide range of engineering applications,as efficient solutions lead to better performance.This study introduces an improved coati optimization algorithm(ICOA)that overcomes the primary limitations of the original coati optimization algorithm(COA),notably its insufficient population diversity and propensity to become trapped in local optima.To address these issues,the ICOA integrates three innovative strategies:Latin hypercube sampling(LHS),Lévyflight,and an adaptive local search.LHS is employed to ensure a diverse initial population,thereby laying a foundation for the optimization.Lévy-flight is utilized to facilitate an efficient global search,enhancing the algorithm’s ability to explore the solution space.The adaptive local search is designed to refine solutions,enabling more precise local exploration.Together,these strategies significantly improve the population’s quality and diversity,thereby improving the algorithm’s convergence accuracy and optimization capabilities.The performance of the ICOA is tested against several established algorithms,using 12 benchmark functions.Additionally,the ICOA’s practicality and effectiveness are demonstrated through application to a real-world engineering problem,specifically the design optimization of tension/compression springs.Simulation results show that the ICOA consistently outperforms the other algorithms,providing robust solutions for a wide range of optimization problems.展开更多
针对浣熊优化算法(coati optimization algorithm,COA)全局搜索能力不足、易陷入局部最优和收敛速度慢的问题,提出一种基于非线性自适应的改进浣熊优化算法(improved coati optimization algorithm based on nonlinear adaptation,NACOA...针对浣熊优化算法(coati optimization algorithm,COA)全局搜索能力不足、易陷入局部最优和收敛速度慢的问题,提出一种基于非线性自适应的改进浣熊优化算法(improved coati optimization algorithm based on nonlinear adaptation,NACOA)。采用Logistic-Tent映射初始化浣熊种群,提升算法初始搜索空间覆盖度,生成更加分散且高质量的初始解;引入莱维飞行策略,利用其长跳跃特性,增强算法的全局搜索能力,有效避免算法陷入局部最优;利用非线性递减惯性权重提高种群的适应性与搜索效率,平衡全局搜索和局部搜索能力,并通过黄金正弦策略提高种群收敛精度。在基准测试函数上进行对比仿真试验,结果表明NACOA具有更好的收敛速度和寻优精度。将NACOA应用到工程问题设计中,证明了该算法的有效性和实用性。展开更多
光伏发电功率受气象因素的影响呈现出不稳定性和间歇性,准确预测光伏功率有助于实现大规模并网并保障电网的稳定运行。以澳大利亚DKASC Solar Centre光伏电站数据为研究对象,提出一种基于气象相似日的变分模态分解算法、长鼻浣熊算法和...光伏发电功率受气象因素的影响呈现出不稳定性和间歇性,准确预测光伏功率有助于实现大规模并网并保障电网的稳定运行。以澳大利亚DKASC Solar Centre光伏电站数据为研究对象,提出一种基于气象相似日的变分模态分解算法、长鼻浣熊算法和双向长短期记忆神经网络(VMD-COA-BiLSTM)的光伏功率短期预测模型。针对光伏数据的复杂非线性特征、噪声干扰以及高维特征等问题,通过K均值聚类将数据划分为3种天气类型,增强模型映射能力;利用VMD将聚类之后的原始信号分解,采用中心频率法确定最佳模态数,充分提取集合中的输入因素信息,提高数据质量;将分解后的各分量分别输入BiLSTM网络进行预测,采用COA优化BiLSTM的超参数配置,实现不同天气类型下的光伏功率的准确预测。结果表明:K均值聚类和VMD算法有效提升了数据质量,增强了输入、输出数据的耦合强度;COA优化BiLSTM模型在优化能力和收敛速度上均优于粒子群算法(PSO);所提出的VMD-COA-BiLSTM模型在晴天、多云和阴雨天的RMSE分别降低了35.24%,45.54%和42.88%,显著提高了预测精度,且能适应不同环境下的可靠预测。展开更多
基金funded by the National Key Research and Development Program(Grant No.2022YFB3706904).
文摘Thetraditional first-order reliability method(FORM)often encounters challengeswith non-convergence of results or excessive calculation when analyzing complex engineering problems.To improve the global convergence speed of structural reliability analysis,an improved coati optimization algorithm(COA)is proposed in this paper.In this study,the social learning strategy is used to improve the coati optimization algorithm(SL-COA),which improves the convergence speed and robustness of the newheuristic optimization algorithm.Then,the SL-COAis comparedwith the latest heuristic optimization algorithms such as the original COA,whale optimization algorithm(WOA),and osprey optimization algorithm(OOA)in the CEC2005 and CEC2017 test function sets and two engineering optimization design examples.The optimization results show that the proposed SL-COA algorithm has a high competitiveness.Secondly,this study introduces the SL-COA algorithm into the MPP(Most Probable Point)search process based on FORM and constructs a new reliability analysis method.Finally,the proposed reliability analysis method is verified by four mathematical examples and two engineering examples.The results show that the proposed SL-COA-assisted FORM exhibits fast convergence and avoids premature convergence to local optima as demonstrated by its successful application to problems such as composite cylinder design and support bracket analysis.
基金financially supported by the Research Program of General Armament Department of China (No. 2012500)
文摘To improve the wear resistance of aluminum alloy frictional parts, Ti B2particles reinforced Ni-base alloy composite coatings were prepared on aluminum alloy 7005 by laser cladding. The microstructure and tribological properties of the composite coatings were investigated. The results show that the composite coating contains the phases of Ni Al, Ni3Al, Al3Ni2, TiB2, TiB, TiC, CrB, and Cr23C6.Its microhardness is HV0.5855.8, which is 15.4 % higher than that of the Ni-base alloy coating and is 6.7 times as high as that of the aluminum alloy. The friction coefficients of the composite coatings are reduced by 6.8 %–21.6 % and 13.2 %–32.4 % compared with those of the Ni-base alloy coatings and the aluminum alloys, while the wear losses are 27.4 %–43.2 % less than those of the Ni-base alloy coatings and are only 16.5 %–32.7 % of those of the aluminum alloys at different loads. At the light loads ranging from 3 to 6 N, the calculated maximum contact stress is smaller than the elastic limit contact stress. The wear mechanism of the composite coatings is micro-cutting wear, but changes into multi-plastic deformation wear at 9 N due to the higher calculated maximum contact stress than the elastic limit contact stress. As the loads increase to 12 N, the calculated flash temperature rises to 332.1 °C.The composite coating experiences multi-plastic deformation wear, micro-brittle fracture wear, and oxidative wear.
基金Supported by the National Key R&D Program of China(2022ZD0119001).
文摘In this paper,a multi-strategy improved coati optimization algorithm(MICOA)for engineering applications is proposed to improve the performance of the coati optimization algorithm(COA)in terms of convergence speed and convergence accuracy.First,a chaotic mapping is applied to initial-ize the population in order to improve the quality of the population and thus the convergence speed of the algorithm.Second,the prey’s position is improved during the prey-hunting phase.Then,the COA is combined with the particle swarm optimization(PSO)and the golden sine algorithm(Gold-SA),and the position is updated with probabilities to avoid local extremes.Finally,a population decreasing strategy is applied as a way to improve the performance of the algorithm in a comprehen-sive approach.The paper compares the proposed algorithm MICOA with 7 well-known meta-heuristic optimization algorithms and evaluates the algorithm in 23 test functions as well as engineering appli-cation.Experimental results show that the MICOA proposed in this paper has good effectiveness and superiority,and has a strong competitiveness compared with the comparison algorithms.
基金supported by the National Natural Science Foundation of China(Grant Nos.52375264 and 62376212).
文摘The power sector is an important factor in ensuring the development of the national economy.Scientific simulation and prediction of power consumption help achieve the balance between power generation and power consumption.In this paper,a Multi-strategy Hybrid Coati Optimizer(MCOA)is used to optimize the parameters of the three-parameter combinatorial optimization model TDGM(1,1,r,ξ,Csz)to realize the simulation and prediction of China's daily electricity consumption.Firstly,a novel MCOA is proposed in this paper,by making the following improvements to the Coati Optimization Algorithm(COA):(ⅰ)Introduce improved circle chaotic mapping strategy.(ⅱ)Fusing Aquila Optimizer,to enhance MCOA's exploration capabilities.(ⅲ)Adopt an adaptive optimal neighborhood jitter learning strategy.Effectively improve MCOA escape from local optimal solutions.(ⅳ)Incorporating Differential Evolution to enhance the diversity of the population.Secondly,the superiority of the MCOA algorithm is verified by comparing it with the newly proposed algorithm,the improved optimiza-tion algorithm,and the hybrid algorithm on the CEC2019 and CEC2020 test sets.Finally,in this paper,MCOA is used to optimize the parameters of TDGM(1,1,r,ξ,Csz),and this model is applied to forecast the daily electricity consumption in China and compared with the predictions of 14 models,including seven intelligent algorithm-optimized TDGM(1,1,r,ξ,Csz),and seven forecasting models.The experimental results show that the error of the proposed method is minimized,which verifies the validity of the proposed method.
基金supported by the Natural Science Foundation of Hunan Province of China(Nos.2021JJ10045 and 2025JJ60072)the Open Research Subject of State Key Laboratory of Intelligent Game(No.ZBKF-24-01)+1 种基金the Postdoctoral Fellowship Program of CPSF(No.GZB20240989)the China Postdoctoral Science Foundation(No.2024M754304).
文摘Optimization problems are crucial for a wide range of engineering applications,as efficient solutions lead to better performance.This study introduces an improved coati optimization algorithm(ICOA)that overcomes the primary limitations of the original coati optimization algorithm(COA),notably its insufficient population diversity and propensity to become trapped in local optima.To address these issues,the ICOA integrates three innovative strategies:Latin hypercube sampling(LHS),Lévyflight,and an adaptive local search.LHS is employed to ensure a diverse initial population,thereby laying a foundation for the optimization.Lévy-flight is utilized to facilitate an efficient global search,enhancing the algorithm’s ability to explore the solution space.The adaptive local search is designed to refine solutions,enabling more precise local exploration.Together,these strategies significantly improve the population’s quality and diversity,thereby improving the algorithm’s convergence accuracy and optimization capabilities.The performance of the ICOA is tested against several established algorithms,using 12 benchmark functions.Additionally,the ICOA’s practicality and effectiveness are demonstrated through application to a real-world engineering problem,specifically the design optimization of tension/compression springs.Simulation results show that the ICOA consistently outperforms the other algorithms,providing robust solutions for a wide range of optimization problems.
文摘针对浣熊优化算法(coati optimization algorithm,COA)全局搜索能力不足、易陷入局部最优和收敛速度慢的问题,提出一种基于非线性自适应的改进浣熊优化算法(improved coati optimization algorithm based on nonlinear adaptation,NACOA)。采用Logistic-Tent映射初始化浣熊种群,提升算法初始搜索空间覆盖度,生成更加分散且高质量的初始解;引入莱维飞行策略,利用其长跳跃特性,增强算法的全局搜索能力,有效避免算法陷入局部最优;利用非线性递减惯性权重提高种群的适应性与搜索效率,平衡全局搜索和局部搜索能力,并通过黄金正弦策略提高种群收敛精度。在基准测试函数上进行对比仿真试验,结果表明NACOA具有更好的收敛速度和寻优精度。将NACOA应用到工程问题设计中,证明了该算法的有效性和实用性。
文摘光伏发电功率受气象因素的影响呈现出不稳定性和间歇性,准确预测光伏功率有助于实现大规模并网并保障电网的稳定运行。以澳大利亚DKASC Solar Centre光伏电站数据为研究对象,提出一种基于气象相似日的变分模态分解算法、长鼻浣熊算法和双向长短期记忆神经网络(VMD-COA-BiLSTM)的光伏功率短期预测模型。针对光伏数据的复杂非线性特征、噪声干扰以及高维特征等问题,通过K均值聚类将数据划分为3种天气类型,增强模型映射能力;利用VMD将聚类之后的原始信号分解,采用中心频率法确定最佳模态数,充分提取集合中的输入因素信息,提高数据质量;将分解后的各分量分别输入BiLSTM网络进行预测,采用COA优化BiLSTM的超参数配置,实现不同天气类型下的光伏功率的准确预测。结果表明:K均值聚类和VMD算法有效提升了数据质量,增强了输入、输出数据的耦合强度;COA优化BiLSTM模型在优化能力和收敛速度上均优于粒子群算法(PSO);所提出的VMD-COA-BiLSTM模型在晴天、多云和阴雨天的RMSE分别降低了35.24%,45.54%和42.88%,显著提高了预测精度,且能适应不同环境下的可靠预测。
