In order to improve the performance of multi-objective workflow scheduling in cloud system, a multi-swarm multiobjective optimization algorithm(MSMOOA) is proposed to satisfy multiple conflicting objectives. Inspired ...In order to improve the performance of multi-objective workflow scheduling in cloud system, a multi-swarm multiobjective optimization algorithm(MSMOOA) is proposed to satisfy multiple conflicting objectives. Inspired by division of the same species into multiple swarms for different objectives and information sharing among these swarms in nature, each physical machine in the data center is considered a swarm and employs improved multi-objective particle swarm optimization to find out non-dominated solutions with one objective in MSMOOA. The particles in each swarm are divided into two classes and adopt different strategies to evolve cooperatively. One class of particles can communicate with several swarms simultaneously to promote the information sharing among swarms and the other class of particles can only exchange information with the particles located in the same swarm. Furthermore, in order to avoid the influence by the elastic available resources, a manager server is adopted in the cloud data center to collect the available resources for scheduling. The quality of the proposed method with other related approaches is evaluated by using hybrid and parallel workflow applications. The experiment results highlight the better performance of the MSMOOA than that of compared algorithms.展开更多
This work proposes an improved multi-objective slime mould algorithm, called IBMSMA, for solving the multi-objective truss optimization problem. In IBMSMA, the chaotic grouping mechanism and dynamic regrouping strateg...This work proposes an improved multi-objective slime mould algorithm, called IBMSMA, for solving the multi-objective truss optimization problem. In IBMSMA, the chaotic grouping mechanism and dynamic regrouping strategy are employed to improve population diversity;the shift density estimation is used to assess the superiority of search agents and to provide selection pressure for population evolution;and the Pareto external archive is utilized to maintain the convergence and distribution of the non-dominated solution set. To evaluate the performance of IBMSMA, it is applied to eight multi-objective truss optimization problems. The results obtained by IBMSMA are compared with other 14 well-known optimization algorithms on hypervolume, inverted generational distance and spacing-to-extent indicators. The Wilcoxon statistical test and Friedman ranking are used for statistical analysis. The results of this study reveal that IBMSMA can find the Pareto front with better convergence and diversity in less time than state-of-the-art algorithms, demonstrating its capability in tackling large-scale engineering design problems.展开更多
与单阈值分割相比,多阈值彩色图像分割具有更高的分割精度,对复杂的彩色图像分割有着较好的效果.但是由于阈值增多导致计算量增大,整体算法的运算时间增加.针对此问题,提出了一种改进的多元宇宙优化算法对多阈值彩色图像分割算法进行优...与单阈值分割相比,多阈值彩色图像分割具有更高的分割精度,对复杂的彩色图像分割有着较好的效果.但是由于阈值增多导致计算量增大,整体算法的运算时间增加.针对此问题,提出了一种改进的多元宇宙优化算法对多阈值彩色图像分割算法进行优化.首先,对多元宇宙优化算法进行改进,引入樽海鞘优化算法中的收敛因子,提高算法的寻优能力;然后,选取多阈值大津法作为图像分割算法,将其作为优化算法的适应度函数;最后,通过对标准数学公式的仿真实验,以及选取四幅伯克利大学图像库图像进行实验分析,实验结果表明该算法能够对图像进行精确分割,在PSNR(Peak Signal to Noise Ratio)和FSIM(Feature Similarity Index)两个指标上均优于其他算法,提高了分割精度.展开更多
基金Project(61473078)supported by the National Natural Science Foundation of ChinaProject(2015-2019)supported by the Program for Changjiang Scholars from the Ministry of Education,China+1 种基金Project(16510711100)supported by International Collaborative Project of the Shanghai Committee of Science and Technology,ChinaProject(KJ2017A418)supported by Anhui University Science Research,China
文摘In order to improve the performance of multi-objective workflow scheduling in cloud system, a multi-swarm multiobjective optimization algorithm(MSMOOA) is proposed to satisfy multiple conflicting objectives. Inspired by division of the same species into multiple swarms for different objectives and information sharing among these swarms in nature, each physical machine in the data center is considered a swarm and employs improved multi-objective particle swarm optimization to find out non-dominated solutions with one objective in MSMOOA. The particles in each swarm are divided into two classes and adopt different strategies to evolve cooperatively. One class of particles can communicate with several swarms simultaneously to promote the information sharing among swarms and the other class of particles can only exchange information with the particles located in the same swarm. Furthermore, in order to avoid the influence by the elastic available resources, a manager server is adopted in the cloud data center to collect the available resources for scheduling. The quality of the proposed method with other related approaches is evaluated by using hybrid and parallel workflow applications. The experiment results highlight the better performance of the MSMOOA than that of compared algorithms.
基金supported by the National Science Foundation of China under Grant No.U21A20464,62066005Innovation Project of Guangxi University for Nationalities Graduate Education under Grant gxun-chxs2021058.
文摘This work proposes an improved multi-objective slime mould algorithm, called IBMSMA, for solving the multi-objective truss optimization problem. In IBMSMA, the chaotic grouping mechanism and dynamic regrouping strategy are employed to improve population diversity;the shift density estimation is used to assess the superiority of search agents and to provide selection pressure for population evolution;and the Pareto external archive is utilized to maintain the convergence and distribution of the non-dominated solution set. To evaluate the performance of IBMSMA, it is applied to eight multi-objective truss optimization problems. The results obtained by IBMSMA are compared with other 14 well-known optimization algorithms on hypervolume, inverted generational distance and spacing-to-extent indicators. The Wilcoxon statistical test and Friedman ranking are used for statistical analysis. The results of this study reveal that IBMSMA can find the Pareto front with better convergence and diversity in less time than state-of-the-art algorithms, demonstrating its capability in tackling large-scale engineering design problems.
文摘与单阈值分割相比,多阈值彩色图像分割具有更高的分割精度,对复杂的彩色图像分割有着较好的效果.但是由于阈值增多导致计算量增大,整体算法的运算时间增加.针对此问题,提出了一种改进的多元宇宙优化算法对多阈值彩色图像分割算法进行优化.首先,对多元宇宙优化算法进行改进,引入樽海鞘优化算法中的收敛因子,提高算法的寻优能力;然后,选取多阈值大津法作为图像分割算法,将其作为优化算法的适应度函数;最后,通过对标准数学公式的仿真实验,以及选取四幅伯克利大学图像库图像进行实验分析,实验结果表明该算法能够对图像进行精确分割,在PSNR(Peak Signal to Noise Ratio)和FSIM(Feature Similarity Index)两个指标上均优于其他算法,提高了分割精度.
