A Systematic Approach to Multiobjective Optimization

Voronin, Albert N.; Савченко, Аліна

doi:10.1007/s10559-020-00320-y

Cited by 6 publications

(2 citation statements)

References 8 publications

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“…Theoretical Basis of HP Method. The analytic hierarchy process refers to taking a complex multiobjective decisionmaking problem as a system, decomposing the goal into multiple goals or criteria, then decomposing it into several levels of multiple indicators (or criteria and constraints), and calculating the hierarchical single ranking (weight) and total ranking through the fuzzy quantitative method of qualitative indicators as the goal (multiple indicators) or a systematic method of multischeme optimization decision [20,21]. The analytic hierarchy process decomposes the decision-making problem into different hierarchical structures according to the order of general objectives, subobjectives of each level, evaluation criteria, and specific standby investment scheme and then obtains the priority weight of each element of each level to an element of the upper level by solving the eigenvector of the judgment matrix.…”

Section: Design Of Tourism-driven Real Estate Economic Growth Quality...mentioning

confidence: 99%

Evaluation Method of Economic Growth Quality of Tourism-Driven Real Estate Based on AHP

2022

Wireless Communications and Mobile Computing

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The traditional tourism-driven real estate economic growth quality evaluation model has the problem of low fitting degree and dynamic degree. In order to solve this problem, a tourism-driven real estate economic growth quality evaluation method based on an analytic hierarchy process (AHP) is proposed. In order to reduce the solution scale of tourism-driven real estate economic growth quality evaluation, the analytic hierarchy process is used to calculate the possibility index, severity index, and economic growth quality index of tourism-driven real estate economic growth and screen and rank the economic growth quality. The discrete zoning model is used to determine the economic growth level of tourism-driven real estate. Using the Mahalanobis distance of multiattribute vector of economic growth quality evaluation index, the distance from the risk element to positive ideal solution and negative ideal solution is defined. By calculating the closeness of data economic growth quality element, this paper constructs a tourism-driven real estate economic growth quality evaluation model. The experimental results show that the model has high dynamics and reliability and greatly improves the joint evaluation effect of tourism-driven real estate economic growth quality.

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Section: Design Of Tourism-driven Real Estate Economic Growth Quality...mentioning

confidence: 99%

Evaluation Method of Economic Growth Quality of Tourism-Driven Real Estate Based on AHP

2022

Wireless Communications and Mobile Computing

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“…In large-scale manufacturing systems, separating subsystems does not always achieve significant dimensionality reductions because each subsystem is characterized by a large number of correlated parameters and dynamic behavior. Therefore, the task of reducing the dimensionality and correlation of subsystems remains relevant [6,7].…”

Section: Introductionmentioning

confidence: 99%

Industrial Process Control Using DPCA and Hierarchical Pareto Optimization

Arsenyev,

Malykhina,

Shkodyrev

2023

Processes

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The control of large-scale industrial systems has several criteria, such as ensuring high productivity, low production costs and the lowest possible environmental impact. These criteria must be established for all subsystems of the large-scale system. This study is devoted to the development of a hierarchical control system that meets several of these criteria and allows for the separate optimization of each subsystem. Multicriteria optimization is based on the processing of data characterizing production processes, which makes it possible to organize a multidimensional statistical control process. Using neural networks to model the technological processes of subsystems and the method of dynamic principal component analysis (DPCA) to reduce the dimensionality of control problems allows us to find more efficient solutions. Using the example of a two-level hierarchy, we showed a variant of the connection between two subsystems by parameters.

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