Multi-Criteria Optimization Based on the Regression Equation Systems Identification

Kotenko, Andrey Petrovich; Pshenina, Darya A.

doi:10.18287/1613-0073-2016-1638-593-599

Cited by 1 publication

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“…A feature of setting the optimization problem in a market economy about irrigation systems of agriculture is multi-criteria, since along with technological (yield), it involves the use of economic optimality criteria (cash costs for irrigation, mineral fertilizers, etc.). The vector optimality criterion gives rise to two problems in practice [1][2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Intelligent decision support in the optimization of irrigation systems in agriculture

2023

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The issues of determining the optimal values of the regulatory parameters of irrigation systems engaged in the cultivation of agricultural crops are considered. Following the requirements of a market economy, the main emphasis is placed on taking into account two types of criteria: maximizing the yield of agricultural crops and minimizing monetary costs. The proposed method for solving the multi-criteria optimization problem is based on the combination of the minimax criterion and the medium-step convolution, which makes it possible to scalarize the vector optimality criterion with access to smooth optimization methods. Concerning the case of priority uncertainty according to particular optimality criteria, an intelligent algorithm is proposed based on the approximation of the preference function of the decision-maker by the fuzzy Mamdani model. The multi-criteria optimization of the irrigation system used for growing cotton results differ favorably from the average values. The one hectare yield in the republic- increased by 2%, monetary costs - reduced by 4.5%. It could be concluded that the developed methodology makes it possible to bypass the computational difficulties that arise when solving problems of multi-criteria optimization of irrigation systems engaged in the cultivation of agricultural crops and to obtain real results in conditions of certainty and uncertainty goals.

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Section: Introductionmentioning

confidence: 99%