Reducing casualties and property losses through effective evacuation route planning has been a key focus for researchers in recent years.As part of this effort,an enhanced sparrow search algorithm(MSSA)was proposed.Fi...Reducing casualties and property losses through effective evacuation route planning has been a key focus for researchers in recent years.As part of this effort,an enhanced sparrow search algorithm(MSSA)was proposed.Firstly,the Golden Sine algorithm and a nonlinear weight factor optimization strategy were added in the discoverer position update stage of the SSA algorithm.Secondly,the Cauchy-Gaussian perturbation was applied to the optimal position of the SSA algorithm to improve its ability to jump out of local optima.Finally,the local search mechanism based on the mountain climbing method was incorporated into the local search stage of the SSA algorithm,improving its local search ability.To evaluate the effectiveness of the proposed algorithm,the Whale Algorithm,Gray Wolf Algorithm,Improved Gray Wolf Algorithm,Sparrow Search Algorithm,and MSSA Algorithm were employed to solve various test functions.The accuracy and convergence speed of each algorithm were then compared and analyzed.The results indicate that the MSSA algorithm has superior solving ability and stability compared to other algorithms.To further validate the enhanced algorithm’s capabilities for path planning,evacuation experiments were conducted using different maps featuring various obstacle types.Additionally,a multi-exit evacuation scenario was constructed according to the actual building environment of a teaching building.Both the sparrow search algorithm and MSSA algorithm were employed in the simulation experiment for multiexit evacuation path planning.The findings demonstrate that the MSSA algorithm outperforms the comparison algorithm,showcasing its greater advantages and higher application potential.展开更多
针对通信机房不间断电源(Uninterruptible Power Supply,UPS)系统在传统运行模式下存在的能耗高、负载率常偏离高效区间的问题,提出基于多策略改进麻雀搜索算法的动态节能优化方法。构建以系统总能耗最小化为目标的优化模型,并通过引入...针对通信机房不间断电源(Uninterruptible Power Supply,UPS)系统在传统运行模式下存在的能耗高、负载率常偏离高效区间的问题,提出基于多策略改进麻雀搜索算法的动态节能优化方法。构建以系统总能耗最小化为目标的优化模型,并通过引入混沌映射初始化、非线性递减惯性权重及动态步长调整等多种策略改进麻雀搜索算法,以高效求解最优的UPS运行参数。基于求解结果,设计动态调控策略,根据实时负载智能切换UPS工作模式并调整关键参数。测试结果表明,经所提方法优化后,机房核心设备群能耗有明显下降,且所有设备能耗均低于80 kW·h的预设阈值;在模拟多种负载工况的4个测试小组中,UPS负载率稳定在60%~80%的高效区间,保障了供电可靠性,为通信机房的绿色低碳运维提供了有效的解决方案。展开更多
In cloud computing, efficient multi-objective task scheduling, aiming at minimizing makespan, energy consumption,and load variance,remains a critical challenge due to the non-deterministic polynomial( NP)-completeness...In cloud computing, efficient multi-objective task scheduling, aiming at minimizing makespan, energy consumption,and load variance,remains a critical challenge due to the non-deterministic polynomial( NP)-completeness of the problem and the limitations of traditional algorithms like premature convergence. In this paper,a multi-strategy improved sparrow search algorithm( MISSA) was proposed to address these issues. MISSA integrates specular reflection learning for initial population optimization,nonlinear adaptive decay weights to balance global exploration and local exploitation,and an innovative strategy based on T-distribution mutation to enhance population diversity. Experimental results on benchmark functions and real cloud task scheduling scenarios using CloudSim demonstrate that MISSA outperforms comparative algorithms such as sparrow search algorithm( SSA),boosted sparrow search algorithm( BSSA),and genetic algorithm-grey wolf optimizer( GA-GWO),achieving significant reductions in makespan,energy consumption,and load variance. MISSA provides an effective solution for intelligent resource allocation in heterogeneous cloud environments,showcasing robust performance in complex multi-objective optimization tasks.展开更多
In the classification problem,deep kernel extreme learning machine(DKELM)has the characteristics of efficient processing and superior performance,but its parameters optimization is difficult.To improve the classificat...In the classification problem,deep kernel extreme learning machine(DKELM)has the characteristics of efficient processing and superior performance,but its parameters optimization is difficult.To improve the classification accuracy of DKELM,a DKELM algorithm optimized by the improved sparrow search algorithm(ISSA),named as ISSA-DKELM,is proposed in this paper.Aiming at the parameter selection problem of DKELM,the DKELM classifier is constructed by using the optimal parameters obtained by ISSA optimization.In order to make up for the shortcomings of the basic sparrow search algorithm(SSA),the chaotic transformation is first applied to initialize the sparrow position.Then,the position of the discoverer sparrow population is dynamically adjusted.A learning operator in the teaching-learning-based algorithm is fused to improve the position update operation of the joiners.Finally,the Gaussian mutation strategy is added in the later iteration of the algorithm to make the sparrow jump out of local optimum.The experimental results show that the proposed DKELM classifier is feasible and effective,and compared with other classification algorithms,the proposed DKELM algorithm aciheves better test accuracy.展开更多
基金supported by National Natural Science Foundation of China(71904006)Henan Province Key R&D Special Project(231111322200)+1 种基金the Science and Technology Research Plan of Henan Province(232102320043,232102320232,232102320046)the Natural Science Foundation of Henan(232300420317,232300420314).
文摘Reducing casualties and property losses through effective evacuation route planning has been a key focus for researchers in recent years.As part of this effort,an enhanced sparrow search algorithm(MSSA)was proposed.Firstly,the Golden Sine algorithm and a nonlinear weight factor optimization strategy were added in the discoverer position update stage of the SSA algorithm.Secondly,the Cauchy-Gaussian perturbation was applied to the optimal position of the SSA algorithm to improve its ability to jump out of local optima.Finally,the local search mechanism based on the mountain climbing method was incorporated into the local search stage of the SSA algorithm,improving its local search ability.To evaluate the effectiveness of the proposed algorithm,the Whale Algorithm,Gray Wolf Algorithm,Improved Gray Wolf Algorithm,Sparrow Search Algorithm,and MSSA Algorithm were employed to solve various test functions.The accuracy and convergence speed of each algorithm were then compared and analyzed.The results indicate that the MSSA algorithm has superior solving ability and stability compared to other algorithms.To further validate the enhanced algorithm’s capabilities for path planning,evacuation experiments were conducted using different maps featuring various obstacle types.Additionally,a multi-exit evacuation scenario was constructed according to the actual building environment of a teaching building.Both the sparrow search algorithm and MSSA algorithm were employed in the simulation experiment for multiexit evacuation path planning.The findings demonstrate that the MSSA algorithm outperforms the comparison algorithm,showcasing its greater advantages and higher application potential.
文摘针对通信机房不间断电源(Uninterruptible Power Supply,UPS)系统在传统运行模式下存在的能耗高、负载率常偏离高效区间的问题,提出基于多策略改进麻雀搜索算法的动态节能优化方法。构建以系统总能耗最小化为目标的优化模型,并通过引入混沌映射初始化、非线性递减惯性权重及动态步长调整等多种策略改进麻雀搜索算法,以高效求解最优的UPS运行参数。基于求解结果,设计动态调控策略,根据实时负载智能切换UPS工作模式并调整关键参数。测试结果表明,经所提方法优化后,机房核心设备群能耗有明显下降,且所有设备能耗均低于80 kW·h的预设阈值;在模拟多种负载工况的4个测试小组中,UPS负载率稳定在60%~80%的高效区间,保障了供电可靠性,为通信机房的绿色低碳运维提供了有效的解决方案。
基金supported by the Key Research and Development Project of Heilongjiang Province (JD2023SJ20)。
文摘In cloud computing, efficient multi-objective task scheduling, aiming at minimizing makespan, energy consumption,and load variance,remains a critical challenge due to the non-deterministic polynomial( NP)-completeness of the problem and the limitations of traditional algorithms like premature convergence. In this paper,a multi-strategy improved sparrow search algorithm( MISSA) was proposed to address these issues. MISSA integrates specular reflection learning for initial population optimization,nonlinear adaptive decay weights to balance global exploration and local exploitation,and an innovative strategy based on T-distribution mutation to enhance population diversity. Experimental results on benchmark functions and real cloud task scheduling scenarios using CloudSim demonstrate that MISSA outperforms comparative algorithms such as sparrow search algorithm( SSA),boosted sparrow search algorithm( BSSA),and genetic algorithm-grey wolf optimizer( GA-GWO),achieving significant reductions in makespan,energy consumption,and load variance. MISSA provides an effective solution for intelligent resource allocation in heterogeneous cloud environments,showcasing robust performance in complex multi-objective optimization tasks.
文摘In the classification problem,deep kernel extreme learning machine(DKELM)has the characteristics of efficient processing and superior performance,but its parameters optimization is difficult.To improve the classification accuracy of DKELM,a DKELM algorithm optimized by the improved sparrow search algorithm(ISSA),named as ISSA-DKELM,is proposed in this paper.Aiming at the parameter selection problem of DKELM,the DKELM classifier is constructed by using the optimal parameters obtained by ISSA optimization.In order to make up for the shortcomings of the basic sparrow search algorithm(SSA),the chaotic transformation is first applied to initialize the sparrow position.Then,the position of the discoverer sparrow population is dynamically adjusted.A learning operator in the teaching-learning-based algorithm is fused to improve the position update operation of the joiners.Finally,the Gaussian mutation strategy is added in the later iteration of the algorithm to make the sparrow jump out of local optimum.The experimental results show that the proposed DKELM classifier is feasible and effective,and compared with other classification algorithms,the proposed DKELM algorithm aciheves better test accuracy.
