Due to the security and scalability features of hybrid cloud architecture,it can bettermeet the diverse requirements of users for cloud *** a reasonable resource allocation solution is the key to adequately utilize th...
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Due to the security and scalability features of hybrid cloud architecture,it can bettermeet the diverse requirements of users for cloud *** a reasonable resource allocation solution is the key to adequately utilize the hybrid ***,most previous studies have not comprehensively optimized the performance of hybrid cloud task scheduling,even ignoring the conflicts between its security privacy features and other *** on the above problems,a many-objective hybrid cloud task scheduling optimizationmodel(HCTSO)is constructed combining risk rate,resource utilization,total cost,and task completion ***,an opposition-based learning knee point-driven many-objective evolutionary algorithm(OBL-KnEA)is proposed to improve the performance of model *** algorithm uses opposition-based learning to generate initial populations for faster ***,a perturbation-based multipoint crossover operator and a dynamic range mutation operator are designed to extend the search *** comparing the experiments with other excellent algorithms on HCTSO,OBL-KnEA achieves excellent results in terms of evaluation metrics,initial populations,and modeloptimization effects.
In the present paper, a scenario-based many-objective optimization model is developed for the spatio-temporal optimal design of reservoir water quality monitoring systems considering uncertainties. The proposed method...
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In the present paper, a scenario-based many-objective optimization model is developed for the spatio-temporal optimal design of reservoir water quality monitoring systems considering uncertainties. The proposed methodology is based on the concept of nonlinear interval number programming and information theory, while handling uncertainties of temperature, reservoir inflow, and inflow constituent concentration. A reference-point-based non-dominated sorting genetic algorithm (NSGA-III) is used to deal with the many-objectiveoptimization problem. The proposed model is developed for the Karkheh reservoir system in Iran as a real-world problem. The results show excellent performance of the optimized water quality sampling locations instead of all potential ones in providing adequate information about the reservoir water quality status. The presented uncertainty-based model leads to a 55.73% reduction in the radius of the uncertain interval caused by different scenarios. Handling uncertainties in a spatio-temporal many-objectiveoptimization problem is the main contribution of this study, yielding a reliable and robust design of a reservoir monitoring system that is less sensitive to various scenarios. (C) 2020 Elsevier Ltd. All rights reserved.
For the earth observation satellite mission planning problem, objectives such as observed target quantity, observation profit, energy consumption, image quality should be considered simultaneously, which is a many-obj...
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For the earth observation satellite mission planning problem, objectives such as observed target quantity, observation profit, energy consumption, image quality should be considered simultaneously, which is a many-objectiveoptimization problem. Classical optimization-based mission planning algorithms obtain a set of non-dominated solutions in the entire search space, while only a single satisfy final plan is desired by decision maker. In this paper, a five-objectiveoptimizationmodel for satellite mission planning problem is constructed, then a region preference-based evolutionary algorithm, HMOEA-T, is applied to obtain the desired solutions. The decision makers describe the preference on each objective in target region form, then the algorithm guides a more detailed search within the preference region rather than the entire Pareto front. Comparative studies with preference-based methods(T-NSGA-III) and classical methods(NSGA-III) are conducted. We have exemplified the proposed method manage to obtain the solutions satisfying the mission planning preference and achieve better performance in convergence and diversity.
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