Coronavirus Disease 2019(COVID-19)is the most severe epidemic that is prevalent all over the world.How quickly and accurately identifying COVID-19 is of great significance to controlling the spread speed of the epidem...Coronavirus Disease 2019(COVID-19)is the most severe epidemic that is prevalent all over the world.How quickly and accurately identifying COVID-19 is of great significance to controlling the spread speed of the epidemic.Moreover,it is essential to accurately and rapidly identify COVID-19 lesions by analyzing Chest X-ray images.As we all know,image segmentation is a critical stage in image processing and analysis.To achieve better image segmentation results,this paper proposes to improve the multi-verse optimizer algorithm using the Rosenbrock method and diffusion mechanism named RDMVO.Then utilizes RDMVO to calculate the maximum Kapur’s entropy for multilevel threshold image segmentation.This image segmentation scheme is called RDMVO-MIS.We ran two sets of experiments to test the performance of RDMVO and RDMVO-MIS.First,RDMVO was compared with other excellent peers on IEEE CEC2017 to test the performance of RDMVO on benchmark functions.Second,the image segmentation experiment was carried out using RDMVO-MIS,and some meta-heuristic algorithms were selected as comparisons.The test image dataset includes Berkeley images and COVID-19 Chest X-ray images.The experimental results verify that RDMVO is highly competitive in benchmark functions and image segmentation experiments compared with other meta-heuristic algorithms.展开更多
<div style="text-align:justify;"> In the multi-objective of intelligent building load scheduling, aiming at the problem of how to select Pareto frontier scheme for multi-objective optimization algorith...<div style="text-align:justify;"> In the multi-objective of intelligent building load scheduling, aiming at the problem of how to select Pareto frontier scheme for multi-objective optimization algorithm, the current optimal scheme mechanism combined with multi-objective multi-verse algorithm is used to optimize the intelligent building load scheduling. The update mechanism is changed in updating the position of the universe, and the process of correction coding is omitted in the iterative process of the algorithm, which reduces the com-putational complexity. The feasibility and effectiveness of the proposed method are verified by the optimal scheduling experiments of residential loads. </div>展开更多
为进一步降低综合能源系统(integrated energy system,IES)的碳排放和提高新能源消纳率,提出了一种含阶梯式碳交易的电热气耦合综合能源系统优化调度策略。首先,建立了热电联供机组、空气源热泵和电转气的电热气耦合调度框架,建立了因...为进一步降低综合能源系统(integrated energy system,IES)的碳排放和提高新能源消纳率,提出了一种含阶梯式碳交易的电热气耦合综合能源系统优化调度策略。首先,建立了热电联供机组、空气源热泵和电转气的电热气耦合调度框架,建立了因空气源热泵变工况引起的调度偏差模型,并引入考虑余热回收的电转气以实现能源互补协同;其次,引入阶梯式碳交易机制以约束系统碳排放,以经济性作为优化目标;最后,通过改进旅行距离率和虫洞存在概率、结合差分变异策略、引入压缩因子三方面对多元宇宙优化(multi-verse optimization,MVO)算法进行改进,克服了常规多元宇宙优化算法在求解所提模型时易陷入局部最优解的问题。通过改进的算法对模型进行求解,结果表明,改进后的多元宇宙优化算法具有更好的收敛性与稳定性,优化后总成本比原始多元宇宙优化算法、粒子群算法、遗传算法分别降低了8.13%、12.90%和9.07%,实现了综合能源系统的经济运行。展开更多
基金supported by the Natural Science Foundation of Zhejiang Province(LY21F020001,LZ22F020005)National Natural Science Foundation of China(62076185,U1809209)+1 种基金Science and Technology Plan Project of Wenzhou,China(ZG2020026)We also acknowledge the respected editor and reviewers'efforts to enhance the quality of this research.
文摘Coronavirus Disease 2019(COVID-19)is the most severe epidemic that is prevalent all over the world.How quickly and accurately identifying COVID-19 is of great significance to controlling the spread speed of the epidemic.Moreover,it is essential to accurately and rapidly identify COVID-19 lesions by analyzing Chest X-ray images.As we all know,image segmentation is a critical stage in image processing and analysis.To achieve better image segmentation results,this paper proposes to improve the multi-verse optimizer algorithm using the Rosenbrock method and diffusion mechanism named RDMVO.Then utilizes RDMVO to calculate the maximum Kapur’s entropy for multilevel threshold image segmentation.This image segmentation scheme is called RDMVO-MIS.We ran two sets of experiments to test the performance of RDMVO and RDMVO-MIS.First,RDMVO was compared with other excellent peers on IEEE CEC2017 to test the performance of RDMVO on benchmark functions.Second,the image segmentation experiment was carried out using RDMVO-MIS,and some meta-heuristic algorithms were selected as comparisons.The test image dataset includes Berkeley images and COVID-19 Chest X-ray images.The experimental results verify that RDMVO is highly competitive in benchmark functions and image segmentation experiments compared with other meta-heuristic algorithms.
文摘<div style="text-align:justify;"> In the multi-objective of intelligent building load scheduling, aiming at the problem of how to select Pareto frontier scheme for multi-objective optimization algorithm, the current optimal scheme mechanism combined with multi-objective multi-verse algorithm is used to optimize the intelligent building load scheduling. The update mechanism is changed in updating the position of the universe, and the process of correction coding is omitted in the iterative process of the algorithm, which reduces the com-putational complexity. The feasibility and effectiveness of the proposed method are verified by the optimal scheduling experiments of residential loads. </div>
文摘为进一步降低综合能源系统(integrated energy system,IES)的碳排放和提高新能源消纳率,提出了一种含阶梯式碳交易的电热气耦合综合能源系统优化调度策略。首先,建立了热电联供机组、空气源热泵和电转气的电热气耦合调度框架,建立了因空气源热泵变工况引起的调度偏差模型,并引入考虑余热回收的电转气以实现能源互补协同;其次,引入阶梯式碳交易机制以约束系统碳排放,以经济性作为优化目标;最后,通过改进旅行距离率和虫洞存在概率、结合差分变异策略、引入压缩因子三方面对多元宇宙优化(multi-verse optimization,MVO)算法进行改进,克服了常规多元宇宙优化算法在求解所提模型时易陷入局部最优解的问题。通过改进的算法对模型进行求解,结果表明,改进后的多元宇宙优化算法具有更好的收敛性与稳定性,优化后总成本比原始多元宇宙优化算法、粒子群算法、遗传算法分别降低了8.13%、12.90%和9.07%,实现了综合能源系统的经济运行。
