Accurate prediction of concrete compressive strength is fundamental for optimizing mix designs,improving material utilization,and ensuring structural safety in modern construction.Traditional empirical methods often f...Accurate prediction of concrete compressive strength is fundamental for optimizing mix designs,improving material utilization,and ensuring structural safety in modern construction.Traditional empirical methods often fail to capture the non-linear relationships among concrete constituents,especially with the growing use of supple-mentary cementitious materials and recycled aggregates.This study presents an integrated machine learning framework for concrete strength prediction,combining advanced regression models—namely CatBoost—with metaheuristic optimization algorithms,with a particular focus on the Somersaulting Spider Optimizer(SSO).A comprehensive dataset encompassing diverse mix proportions and material types was used to evaluate baseline machine learning models,including CatBoost,XGBoost,ExtraTrees,and RandomForest.Among these,CatBoost demonstrated superior accuracy across multiple performance metrics.To further enhance predictive capability,several bio-inspired optimizers were employed for hyperparameter tuning.The SSO-CatBoost hybrid achieved the lowest mean squared error and highest correlation coefficients,outperforming other metaheuristic approaches such as Genetic Algorithm,Particle Swarm Optimization,and Grey Wolf Optimizer.Statistical significance was established through Analysis of Variance and Wilcoxon signed-rank testing,confirming the robustness of the optimized models.The proposed methodology not only delivers improved predictive performance but also offers a transparent framework for mix design optimization,supporting data-driven decision making in sustainable and resilient infrastructure development.展开更多
To address the limitations of the sand cat swarm optimization(SCSO) algorithm which are slow convergence and low accuracy in complex problems,this study proposes an improved SCSO(ISCSO) algorithm that integrates multi...To address the limitations of the sand cat swarm optimization(SCSO) algorithm which are slow convergence and low accuracy in complex problems,this study proposes an improved SCSO(ISCSO) algorithm that integrates multiple enhancement strategies.Firstly,Kent chaotic mapping initializes the population for uniform distribution.Secondly,somersault foraging strategy is introduced during the search and attack phases,allowing the algorithm to escape local optima by intercepting evasive prey.Simultaneously,an adaptive Lévy flight strategy is incorporated into the attack phase to bolster global exploration.Finally,the vertical and horizontal crossover strategy is implemented to enhance population diversity.The performance of the proposed algorithm is evaluated using 16 benchmark test functions.The experimental results demonstrate that ISCSO significantly outperforms the original SCSO and shows notable advantages over other metaheuristic algorithms.Furthermore,application to a pressure vessel design problem verifies ISCSO's effectiveness in solving practical engineering optimization challenges.展开更多
针对水下无线传感器网络分簇路由协议的簇头选举不合理和能耗不均衡的问题,提出一种基于翻筋斗觅食策略的改进灰狼算法的水下低功耗分簇路由协议IGBSU(an improved Grey Wolf algorithm based on somersault foraging strategy for unde...针对水下无线传感器网络分簇路由协议的簇头选举不合理和能耗不均衡的问题,提出一种基于翻筋斗觅食策略的改进灰狼算法的水下低功耗分簇路由协议IGBSU(an improved Grey Wolf algorithm based on somersault foraging strategy for underwater low-power cluster routing protocols)。提出一种基于翻筋斗觅食策略的改进灰狼算法IGBSU,来解决传统灰狼算法的局部收敛与收敛速度慢的问题;同时结合网络能耗模型得出最优簇头数量;利用改进的灰狼算法进行簇头选举,在设计适应值函数时综合考虑了节点能量、距离、密度、当选簇头频数、能耗速率5种因素;在簇间路由过程中采用考虑角度因子的蚁群算法选取最优传输路径。实验结果表明,协议IGBSU能够有效减缓节点死亡速度,降低网络能耗,延长网络生命周期。展开更多
针对基本正弦余弦算法(sine cosine algorithm,SCA)求解高维复杂优化问题时存在精度低、收敛慢和易陷入局部最优等缺点,提出一种改进的SCA(improved sine cosine algorithm,iSCA)。首先,该算法设计出一种基于倒S形函数的非线性转换参数...针对基本正弦余弦算法(sine cosine algorithm,SCA)求解高维复杂优化问题时存在精度低、收敛慢和易陷入局部最优等缺点,提出一种改进的SCA(improved sine cosine algorithm,iSCA)。首先,该算法设计出一种基于倒S形函数的非线性转换参数规则替代原有线性策略,从而实现从全局搜索到局部搜索的良好过渡;其次,嵌入个体历史最佳信息修改位置搜索方程以指导寻优过程,进一步改善算法的解精度和加快收敛;最后,引入翻筋斗觅食机制生成新的位置以增加群体多样性,从而降低算法陷入局部最优的概率。选取10个高维基准测试函数、10个UCI高维数据集和2个风电机组故障数据集进行仿真实验,并与基本SCA、MSCA(memoryguided SCA)和I-GWO(improved grey wolf optimizer)算法比较,结果表明,iSCA算法在精度和收敛指标上均优于其他比较方法。展开更多
文摘Accurate prediction of concrete compressive strength is fundamental for optimizing mix designs,improving material utilization,and ensuring structural safety in modern construction.Traditional empirical methods often fail to capture the non-linear relationships among concrete constituents,especially with the growing use of supple-mentary cementitious materials and recycled aggregates.This study presents an integrated machine learning framework for concrete strength prediction,combining advanced regression models—namely CatBoost—with metaheuristic optimization algorithms,with a particular focus on the Somersaulting Spider Optimizer(SSO).A comprehensive dataset encompassing diverse mix proportions and material types was used to evaluate baseline machine learning models,including CatBoost,XGBoost,ExtraTrees,and RandomForest.Among these,CatBoost demonstrated superior accuracy across multiple performance metrics.To further enhance predictive capability,several bio-inspired optimizers were employed for hyperparameter tuning.The SSO-CatBoost hybrid achieved the lowest mean squared error and highest correlation coefficients,outperforming other metaheuristic approaches such as Genetic Algorithm,Particle Swarm Optimization,and Grey Wolf Optimizer.Statistical significance was established through Analysis of Variance and Wilcoxon signed-rank testing,confirming the robustness of the optimized models.The proposed methodology not only delivers improved predictive performance but also offers a transparent framework for mix design optimization,supporting data-driven decision making in sustainable and resilient infrastructure development.
基金Supported by the National Key R&D Program of China (No.2022ZD0119000)the Natural Science Foundation of Shaanxi Province (No.2025JC-YBMS-736,2025JC-YBMS-343)Shaanxi Province Key Research and Development Project (2025CY-YBXM-061)。
文摘To address the limitations of the sand cat swarm optimization(SCSO) algorithm which are slow convergence and low accuracy in complex problems,this study proposes an improved SCSO(ISCSO) algorithm that integrates multiple enhancement strategies.Firstly,Kent chaotic mapping initializes the population for uniform distribution.Secondly,somersault foraging strategy is introduced during the search and attack phases,allowing the algorithm to escape local optima by intercepting evasive prey.Simultaneously,an adaptive Lévy flight strategy is incorporated into the attack phase to bolster global exploration.Finally,the vertical and horizontal crossover strategy is implemented to enhance population diversity.The performance of the proposed algorithm is evaluated using 16 benchmark test functions.The experimental results demonstrate that ISCSO significantly outperforms the original SCSO and shows notable advantages over other metaheuristic algorithms.Furthermore,application to a pressure vessel design problem verifies ISCSO's effectiveness in solving practical engineering optimization challenges.
文摘针对水下无线传感器网络分簇路由协议的簇头选举不合理和能耗不均衡的问题,提出一种基于翻筋斗觅食策略的改进灰狼算法的水下低功耗分簇路由协议IGBSU(an improved Grey Wolf algorithm based on somersault foraging strategy for underwater low-power cluster routing protocols)。提出一种基于翻筋斗觅食策略的改进灰狼算法IGBSU,来解决传统灰狼算法的局部收敛与收敛速度慢的问题;同时结合网络能耗模型得出最优簇头数量;利用改进的灰狼算法进行簇头选举,在设计适应值函数时综合考虑了节点能量、距离、密度、当选簇头频数、能耗速率5种因素;在簇间路由过程中采用考虑角度因子的蚁群算法选取最优传输路径。实验结果表明,协议IGBSU能够有效减缓节点死亡速度,降低网络能耗,延长网络生命周期。
文摘针对基本正弦余弦算法(sine cosine algorithm,SCA)求解高维复杂优化问题时存在精度低、收敛慢和易陷入局部最优等缺点,提出一种改进的SCA(improved sine cosine algorithm,iSCA)。首先,该算法设计出一种基于倒S形函数的非线性转换参数规则替代原有线性策略,从而实现从全局搜索到局部搜索的良好过渡;其次,嵌入个体历史最佳信息修改位置搜索方程以指导寻优过程,进一步改善算法的解精度和加快收敛;最后,引入翻筋斗觅食机制生成新的位置以增加群体多样性,从而降低算法陷入局部最优的概率。选取10个高维基准测试函数、10个UCI高维数据集和2个风电机组故障数据集进行仿真实验,并与基本SCA、MSCA(memoryguided SCA)和I-GWO(improved grey wolf optimizer)算法比较,结果表明,iSCA算法在精度和收敛指标上均优于其他比较方法。
