Enterprises have extensively taken on cloud computing environment since it provides on-demand virtualized cloud application resources.The scheduling of the cloud tasks is a well-recognized NP-hard problem.The Task sch...Enterprises have extensively taken on cloud computing environment since it provides on-demand virtualized cloud application resources.The scheduling of the cloud tasks is a well-recognized NP-hard problem.The Task scheduling problem is convoluted while convincing different objectives,which are dispute in nature.In this paper,Multi-Objective Improved Monarch Butterfly Optimization(MOIMBO)algorithm is applied to solve multi-objective task scheduling problems in the cloud in preparation for Pareto optimal solutions.Three different dispute objectives,such as makespan,reliability,and resource utilization,are deliberated for task scheduling problems.The Epsilonfuzzy dominance sort method is utilized in the multi-objective domain to elect the foremost solutions from the Pareto optimal solution set.MOIMBO,together with the Self Adaptive and Greedy Strategies,have been incorporated to enrich the performance of the proposed algorithm.The capability and effectiveness of the proposed algorithm are measured with NSGA-II and MOPSO algorithms.The simulation results prompt that the proposed MOIMBO algorithm extensively diminishes the makespan,maximize the reliability,and guarantees the appropriate resource utilization when associating it with identified existing algorithms.展开更多
Bilinear singular systems can be used in the investigation of different types of engineering systems.In the past decade,considerable attention has been paid to analyzing and synthesizing singular bilinear systems.Thei...Bilinear singular systems can be used in the investigation of different types of engineering systems.In the past decade,considerable attention has been paid to analyzing and synthesizing singular bilinear systems.Their importance lies in their real world application such as economic,ecological,and socioeconomic processes.They are also applied in several biological processes,such as population dynamics of biological species,water balance,temperature regulation in the human body,carbon dioxide control in lungs,blood pressure,immune system,cardiac regulation,etc.Bilinear singular systems naturally represent different physical processes such as the fundamental law of mass action,the DC motor,the induction motor drives,the mechanical brake systems,aerial combat between two aircraft,the missile intercept problem,modeling and control of small furnaces and hydraulic rotary multimotor systems.The current research work discusses the Legendre Neural Network’s implementation to evaluate time-varying singular bilinear systems for finding the exact solution.The results were obtained from two methods namely the RK-Butcher algorithm and the Runge Kutta Arithmetic Mean(RKAM)method.Compared with the results attained from Legendre Neural Network Method for time-varying singular bilinear systems,the output proved to be accurate.As such,this research article established that the proposed Legendre Neural Network could be easily implemented in MATLAB.One can obtain the solution for any length of time from this method in time-varying singular bilinear systems.展开更多
文摘Enterprises have extensively taken on cloud computing environment since it provides on-demand virtualized cloud application resources.The scheduling of the cloud tasks is a well-recognized NP-hard problem.The Task scheduling problem is convoluted while convincing different objectives,which are dispute in nature.In this paper,Multi-Objective Improved Monarch Butterfly Optimization(MOIMBO)algorithm is applied to solve multi-objective task scheduling problems in the cloud in preparation for Pareto optimal solutions.Three different dispute objectives,such as makespan,reliability,and resource utilization,are deliberated for task scheduling problems.The Epsilonfuzzy dominance sort method is utilized in the multi-objective domain to elect the foremost solutions from the Pareto optimal solution set.MOIMBO,together with the Self Adaptive and Greedy Strategies,have been incorporated to enrich the performance of the proposed algorithm.The capability and effectiveness of the proposed algorithm are measured with NSGA-II and MOPSO algorithms.The simulation results prompt that the proposed MOIMBO algorithm extensively diminishes the makespan,maximize the reliability,and guarantees the appropriate resource utilization when associating it with identified existing algorithms.
文摘Bilinear singular systems can be used in the investigation of different types of engineering systems.In the past decade,considerable attention has been paid to analyzing and synthesizing singular bilinear systems.Their importance lies in their real world application such as economic,ecological,and socioeconomic processes.They are also applied in several biological processes,such as population dynamics of biological species,water balance,temperature regulation in the human body,carbon dioxide control in lungs,blood pressure,immune system,cardiac regulation,etc.Bilinear singular systems naturally represent different physical processes such as the fundamental law of mass action,the DC motor,the induction motor drives,the mechanical brake systems,aerial combat between two aircraft,the missile intercept problem,modeling and control of small furnaces and hydraulic rotary multimotor systems.The current research work discusses the Legendre Neural Network’s implementation to evaluate time-varying singular bilinear systems for finding the exact solution.The results were obtained from two methods namely the RK-Butcher algorithm and the Runge Kutta Arithmetic Mean(RKAM)method.Compared with the results attained from Legendre Neural Network Method for time-varying singular bilinear systems,the output proved to be accurate.As such,this research article established that the proposed Legendre Neural Network could be easily implemented in MATLAB.One can obtain the solution for any length of time from this method in time-varying singular bilinear systems.
