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
In this study,a theoretical nonlinear dynamic model was established for a saddle ring based on a dynamic force analysis of the launching process and the structure according to contact-impact theory.The ADAMS software ...In this study,a theoretical nonlinear dynamic model was established for a saddle ring based on a dynamic force analysis of the launching process and the structure according to contact-impact theory.The ADAMS software was used to build a parameterized dynamic model of the saddle ring.A parameter identification method for the ring was proposed based on the particle swarm optimization algorithm.A loading test was designed and performed several times at different elevation angles.The response histories of the saddle ring with different loads were then obtained.The parameters of the saddle ring dynamic model were identified from statistics generated at a 500 elevation angle to verify the feasibility and accuracy of the proposed method.The actual loading history of the ring at a 70°elevation angle was taken as the model input.The response histories of the ring under these working conditions were obtained through a simulation.The simulation results agreed with the actual response.Thus,the effectiveness and applicability of the proposed dynamic model were verified,and it provides an effective method for modeling saddle rings.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 National Natural Science Foundation of China(11472137)the Natural Science Foundation of Jiangsu Province,China(BK20140773)。
文摘In this study,a theoretical nonlinear dynamic model was established for a saddle ring based on a dynamic force analysis of the launching process and the structure according to contact-impact theory.The ADAMS software was used to build a parameterized dynamic model of the saddle ring.A parameter identification method for the ring was proposed based on the particle swarm optimization algorithm.A loading test was designed and performed several times at different elevation angles.The response histories of the saddle ring with different loads were then obtained.The parameters of the saddle ring dynamic model were identified from statistics generated at a 500 elevation angle to verify the feasibility and accuracy of the proposed method.The actual loading history of the ring at a 70°elevation angle was taken as the model input.The response histories of the ring under these working conditions were obtained through a simulation.The simulation results agreed with the actual response.Thus,the effectiveness and applicability of the proposed dynamic model were verified,and it provides an effective method for modeling saddle rings.
