Generalized techniques for solving intuitionistic fuzzy multi-objective non-linear optimization problems

Rani, Deepika; Ebrahimnejad, Ali

doi:10.1016/j.eswa.2022.117264

Cited by 27 publications

(8 citation statements)

References 40 publications

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“…To adapt to the complexity and variability of the decision-making process, researchers have developed different types of fuzzy sets, such as intuitionistic fuzzy sets [ 52 ], triangular fuzzy sets [ 43 ], trapezoid fuzzy sets [ 53 ], probabilistic hesitant fuzzy sets [ 54 ], interval type-2 fuzzy sets [ 17 ], etc. Through practical application and theoretical verification, intuitionistic fuzzy sets have been shown to have high stability and wide applicability [ 55 ]. In emergency decision making with high requirements for time cost and economic cost, it is essential to accurately describe the DMs’ attitude toward the decision object [ 56 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Extended Multicriteria Group Decision Making with a Novel Aggregation Operator for Emergency Material Supplier Selection

Liu

Zhu

Yang

et al. 2023

Entropy

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How to ensure the normal production of industries in an uncertain emergency environment has aroused a lot of concern in society. Selecting the best emergency material suppliers using the multicriteria group decision making (MCGDM) method will ensure the normal production of industries in this environment. However, there are few studies in emergency environments that consider the impact of the decision order of decision makers (DMs) on the decision results. Therefore, in order to fill the research gap, we propose an extended MCGDM method, whose main steps include the following: Firstly, the DMs give their assessment of all alternatives. Secondly, we take the AHP method and entropy weight method to weight the criteria and the DMs. Thirdly, we take the intuitionistic fuzzy hybrid priority weight average (IFHPWA) operator we proposed to aggregate evaluation information and take the TOPSIS method to rank all the alternatives. Finally, the proposed method is applied in a case to prove its practicability and effectiveness. The proposed method considers the influence of the decision order of the DMs on the decision results, which improves the accuracy and efficiency of decision-making results.

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Section: Literature Reviewmentioning

confidence: 99%

Extended Multicriteria Group Decision Making with a Novel Aggregation Operator for Emergency Material Supplier Selection

Liu

Zhu

Yang

et al. 2023

Entropy

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“…Obtaining the real-time optimal solutions for FNLOPs is the difficulty of dynamic system model. There are a variety of numerical plans to solve optimization problems, especially in the presence of uncertainty (see References 1,[18][19][20][21][22][23][24][25][26][27]. The general numerical algorithms have usually less capability in real-time applications.…”

Section: Introductionmentioning

confidence: 99%

A cooperative neural dynamic model for solving general convex nonlinear optimization problems with fuzzy parameters and an application in manufacturing systems

Jahangiri,

Nazemi

2024

Adaptive Control & Signal

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SummaryIn the presented study, the solution of the fuzzy nonlinear optimization problems (FNLOPs) is calculated using a recurrent neural network (RNN) model. Since there is a few research for solving FNLOP by RNN's, we give a new approach to solve the problem. By reducing the original program to an interval problem and then weighting problem, the Karush–Kuhn–Tucker (KKT) conditions are given. Moreover, we use the KKT conditions into a RNN as an important tool to solve the problem. Besides, the global convergence properties and the Lyapunov stability of the dynamic model are studied in this study. In the final step, some illustrative examples are considered to establish the obtained results. Reported results are compared with some others network models.

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“…In many MOPs, there are often some practical problems such as less modeling data and imprecise data, so that the objective function or constraint function in these optimization problems contain uncertain parameters. Such optimization problems with uncertainty are called uncertain MOPs (Rani et al , 2022). According to the characteristics of the uncertain parameters, the methods to deal with the uncertain MOPs include stochastic programming, fuzzy programming, the interval method, etc (Liu and Liu, 2020).…”

Section: Introductionmentioning

confidence: 99%

Interval multi-objective grey wolf optimization algorithm based on fuzzy system

Lin

2023

IJICC

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PurposeThe interval multi-objective optimization problems (IMOPs) are universal and vital uncertain optimization problems. In this study, an interval multi-objective grey wolf optimization algorithm (GWO) based on fuzzy system is proposed to solve IMOPs effectively.Design/methodology/approachFirst, the classical genetic operators are embedded into the interval multi-objective GWO as local search strategies, which effectively balanced the global search ability and local development ability. Second, by constructing a fuzzy system, an effective local search activation mechanism is proposed to save computing resources as much as possible while ensuring the performance of the algorithm. The fuzzy system takes hypervolume, imprecision and number of iterations as inputs and outputs the activation index, local population size and maximum number of iterations. Then, the fuzzy inference rules are defined. It uses the activation index to determine whether to activate the local search process and sets the population size and the maximum number of iterations in the process.FindingsThe experimental results show that the proposed algorithm achieves optimal hypervolume results on 9 of the 10 benchmark test problems. The imprecision achieved on 8 test problems is significantly better than other algorithms. This means that the proposed algorithm has better performance than the commonly used interval multi-objective evolutionary algorithms. Moreover, through experiments show that the local search activation mechanism based on fuzzy system proposed in this study can effectively ensure that the local search is activated reasonably in the whole algorithm process, and reasonably allocate computing resources by adaptively setting the population size and maximum number of iterations in the local search process.Originality/valueThis study proposes an Interval multi-objective GWO, which could effectively balance the global search ability and local development ability. Then an effective local search activation mechanism is developed by using fuzzy inference system. It closely combines global optimization with local search, which improves the performance of the algorithm and saves computing resources.

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Generalized techniques for solving intuitionistic fuzzy multi-objective non-linear optimization problems

Cited by 27 publications

References 40 publications

Extended Multicriteria Group Decision Making with a Novel Aggregation Operator for Emergency Material Supplier Selection

Extended Multicriteria Group Decision Making with a Novel Aggregation Operator for Emergency Material Supplier Selection

A cooperative neural dynamic model for solving general convex nonlinear optimization problems with fuzzy parameters and an application in manufacturing systems

Interval multi-objective grey wolf optimization algorithm based on fuzzy system

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