Type-3 fuzzy logic has been recently used in many control methods. The type-3 fuzzy controller enhances the handling of uncertainty and improves robustness by integrating fuzzy sets with fuzzy membership functions. Th...
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Type-3 fuzzy logic has been recently used in many control methods. The type-3 fuzzy controller enhances the handling of uncertainty and improves robustness by integrating fuzzy sets with fuzzy membership functions. The latest approaches using type-3 fuzzy logic in the field of control are studied and evaluated. An overview of developments in control methods based on type-3 fuzzy logic is also provided. It is shown that type-3 fuzzy system has many advantages compared to type-1 and type-2 fuzzy. The advantages and challenges of using type-3 fuzzy logic are identified and discussed. The studies are classified according to the type of control approach, as well as by the type of control applications. Finally, the main achievements, open challenges, and future directions and impacts are identified, to provide important guidance for interested researchers.
In the past few years, photovoltaic production has significantly increased worldwide and has become a necessary element for achieving global agreements to minimize carbon dioxide emissions. Therefore, a precise and re...
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The ethylene cracking furnace (ECF) is an important device for producing ethylene and propylene, so the optimization problem of the ECF is crucial. However, traditional optimization algorithms such as the grey wolf op...
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In this paper we deal with stochastic optimization problems where the data distributions change in response to the decision variables. Traditionally, the study of optimization problems with decision-dependent distribu...
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This paper studies an enhanced distributed optimization algorithm in an undirected topology based on local communication and computation to optimize the sum of local objective functions under multiple constraints. In ...
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The termite life cycle optimizer algorithm (TLCO) is a new bionic meta-heuristic algorithm that emulates the natural behavior of termites in their natural habitat. This work presents an improved TLCO (ITLCO) to increa...
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The termite life cycle optimizer algorithm (TLCO) is a new bionic meta-heuristic algorithm that emulates the natural behavior of termites in their natural habitat. This work presents an improved TLCO (ITLCO) to increase the speed and accuracy of convergence. A novel strategy for worker generation is established to enhance communication between individuals in the worker population and termite population. This strategy would prevent the original worker generation strategy from effectively balancing algorithm convergence and population diversity to reduce the risk of the algorithm in reaching a local optimum. A novel soldier generation strategy is proposed, which incorporates a step factor that adheres to the principles of evolution to further enhance the algorithm’s convergence speed. Furthermore, a novel replacement update mechanism is executed when the new individual is of lower quality than the original individual. This mechanism ensures a balance between the convergence of the algorithm and the diversity of the population. The findings from CEC2013, CEC2019, and CEC2020 test sets indicate that ITLCO exhibits notable benefits regarding convergence speed, accuracy, and stability in comparison with the basic TLCO algorithm and the four most exceptional metaheuristic algorithms thus far. Copyright 2025 Wang and Wei
Environmental models are used extensively to evaluate the effectiveness of a range of design, planning, operational, management and policy options. However, the number of options that can be evaluated manually is gene...
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Environmental models are used extensively to evaluate the effectiveness of a range of design, planning, operational, management and policy options. However, the number of options that can be evaluated manually is generally limited, making it difficult to identify the most suitable options to consider in decision-making processes. By linking environmental models with evolutionary and other metaheuristic optimization algorithms, the decision options that make best use of scarce resources, achieve the best environmental outcomes for a given budget or provide the best trade-offs between competing objectives can be identified. This Introductory Overview presents reasons for embedding formal optimization approaches in environmental decision-making processes, details how environmental problems are formulated as optimization problems and outlines how single- and multi-objective optimization approaches find good solutions to environmental problems. Practical guidance and potential challenges are also provided.
In recent years, black-box distributed optimization (DBO) has been widely studied to solve complex optimization problems in multi-agent systems, such as hyperparameter optimization of distributed machine learning. How...
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Copper is an essential material for electrical conductivity and is a good conductor for heat. The porphyry copper deposits (PCD) are one of the most important resources of copper, where the determination of copper gra...
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Copper is an essential material for electrical conductivity and is a good conductor for heat. The porphyry copper deposits (PCD) are one of the most important resources of copper, where the determination of copper grade is one of the most important issues. The finding complex relationship between copper grade and kind of rocks is a major change for modelers. This study employed the adaptive neuro-fuzzy interface system (ANFIS) and multi-layer perceptron (MLP) to estimate the copper grade in PCDs. The Henry gas solubility optimization (HGSO), weed algorithm (WA), and moth flame optimization (MFO) were applied to set the parameters of the MLP and ANFIS models. The Iju PCD, as one of the important copper deposits in the Kerman province of Iran, was chosen as a case study for predicting the copper grade. Three scenarios were used as input to the models. The first scenario used the latitude and altitude of boreholes as input and the second scenario used the longitude and altitude of boreholes as input. The third scenario used the latitude, longitude, and altitude of boreholes as input. Results of the first scenario indicated that the percent bias of the ANFIS model was 0.26, while it was 0.19, 0.22, and 0.24 for the ANFIS-HGSO, ANFIS-MFO, and ANFIS-WA models. The accuracy of models indicated that the integration of ANFIS and HGSO decreased the root mean square error (RMSE)of the ANFIS-MFO, ANFIS-WA, and ANFIS models about 14%, 21%, and 27%, respectively, in the training phase in the second scenario. The RMSE for the ANFIS-HGSO was 1.98 in the training phase, while it was 2.31, 2.45, and 2.67 for the ANFIS-MFO, ANFIS-WA, and ANFIS models, respectively, in the third scenario. The accuracy of three input scenarios was compared with that of ANFIS-HSGO. The Mean absolute error of ANFIS-HSGO for the third input scenario was 67% and 40% less than for the first and second input scenarios in the testing phase. The third scenario was the best input scenario. Uncertainty analysis f
Sequential randomized algorithms are considered for robust convex optimization which minimizes a linear objective function subject to a parameter dependent convex constraint. Employing convex optimization and random s...
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Sequential randomized algorithms are considered for robust convex optimization which minimizes a linear objective function subject to a parameter dependent convex constraint. Employing convex optimization and random sampling of parameter, these algorithms enable us to obtain a suboptimal solution within reasonable computational time. The suboptimal solution is feasible in a probabilistic sense and the suboptimal value belongs to an interval which contains the optimal value. The maximum of the interval is the optimal value of the robust convex optimization plus a specified tolerance. On the other hand, its minimum is the optimal value of the chance constrained optimization which is a probabilistic relaxation of the robust convex optimization, with high probability.
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