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
玄武岩纤维增强复合材料(basalt fiber-reinforced polymer,BFRP)具有优异的力学性能和可熔融再生特性,在汽车轻量化领域的应用前景广阔。针对某车型的铝制防撞装置,开展BFRP/铝混合防撞装置的多目标优化设计。首先,对BFRP层合板开展力...玄武岩纤维增强复合材料(basalt fiber-reinforced polymer,BFRP)具有优异的力学性能和可熔融再生特性,在汽车轻量化领域的应用前景广阔。针对某车型的铝制防撞装置,开展BFRP/铝混合防撞装置的多目标优化设计。首先,对BFRP层合板开展力学性能测试,并利用HyperMesh软件建立防撞装置有限元模型;其次,采用拉丁超立方抽样生成代理模型的训练样本,结合敏感度分析识别关键设计参数,并通过基于加权欧式距离的空间填充采样法来提升代理模型对响应指标的预测精度;最后,以防撞装置峰值载荷、总质量及横梁最大位移最小为优化目标,运用MOPSO(multi-objective particle swarm optimization,多目标粒子群优化)算法求解Pareto前沿,并基于熵权-TOPSIS(technique for order preference by similarity to an ideal solution,逼近理想解排序法)确定最优设计参数组合。结果显示:优化后防撞装置的峰值载荷降低了36.15%,总质量减小了12.23%,显著提升了耐撞性能并实现了轻量化目标。所提出的方法可为BFRP/铝混合防撞装置的轻量化设计提供一套系统性的解决方案。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.
文摘玄武岩纤维增强复合材料(basalt fiber-reinforced polymer,BFRP)具有优异的力学性能和可熔融再生特性,在汽车轻量化领域的应用前景广阔。针对某车型的铝制防撞装置,开展BFRP/铝混合防撞装置的多目标优化设计。首先,对BFRP层合板开展力学性能测试,并利用HyperMesh软件建立防撞装置有限元模型;其次,采用拉丁超立方抽样生成代理模型的训练样本,结合敏感度分析识别关键设计参数,并通过基于加权欧式距离的空间填充采样法来提升代理模型对响应指标的预测精度;最后,以防撞装置峰值载荷、总质量及横梁最大位移最小为优化目标,运用MOPSO(multi-objective particle swarm optimization,多目标粒子群优化)算法求解Pareto前沿,并基于熵权-TOPSIS(technique for order preference by similarity to an ideal solution,逼近理想解排序法)确定最优设计参数组合。结果显示:优化后防撞装置的峰值载荷降低了36.15%,总质量减小了12.23%,显著提升了耐撞性能并实现了轻量化目标。所提出的方法可为BFRP/铝混合防撞装置的轻量化设计提供一套系统性的解决方案。
