Evolutionary multiobjective optimization (EMO) has made significant strides over the past two decades. However, as problem scales and complexities increase, traditional EMO algorithms face substantial performance limi...
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For the composite multi-objective optimization problem composed of two nonsmooth terms, a smoothing method is used to overcome the nonsmoothness of the objective function, making the objective function contain at most...
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In this article, the performance analysis and multiobjective structure optimization of 4RRR parallel mechanism are carried out. Firstly, the 4RRR pure rotation parallel mechanism and its design route are introduced. S...
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In this article, the performance analysis and multiobjective structure optimization of 4RRR parallel mechanism are carried out. Firstly, the 4RRR pure rotation parallel mechanism and its design route are introduced. Secondly, the Jacobian matrices in 2DoF pure rotation and 3DoF pure rotation modes are derived using the motion equations of the mechanism. Next, the singularity analysis, kinematic dexterity analysis, dynamic dexterity analysis, and stiffness analysis of the mechanism are carried out, respectively, and it is proved that there is no singularity in the mechanism in its workspace. Since the dexterity performance expression is a nonlinear piecewise function, the kinematic local comprehensive dexterity index and the dynamic local comprehensive dexterity index are proposed as the objects of analysis. Furthermore, the kinematic global comprehensive dexterity index, the dynamic global comprehensive dexterity index, and the global comprehensive stiffness index are proposed to carry out the multiobjective structural optimization. Finally, NSGA3 was used to complete the optimization, and the comprehensive optimal solution of the structure size was obtained.
This article deals with multiobjective composite optimization problems that consist of simultaneously minimizing several objective functions, each of which is composed of a combination of smooth and non-smooth functio...
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This article deals with multiobjective composite optimization problems that consist of simultaneously minimizing several objective functions, each of which is composed of a combination of smooth and non-smooth functions. To tackle these problems, we propose a generalized version of the conditional gradient method, also known as Frank-Wolfe method. The method is analysed with three step size strategies, including Armijo-type, adaptive, and diminishing step sizes. We establish asymptotic convergence properties and iteration-complexity bounds, with and without convexity assumptions on the objective functions. Numerical experiments illustrating the practical behaviour of the methods are presented.
The aim of this paper is to state sequential optimality conditions for completely characterizing an approximate weak efficient solution of a vector-constrained robust multiobjective fractional programming problem in t...
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Response surface methodology (RSM) and the desirability function (DF) approach have been widely used for multiresponse optimization in lactic acid fermentation processes. However, multiobjective optimization (MOO) can...
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Response surface methodology (RSM) and the desirability function (DF) approach have been widely used for multiresponse optimization in lactic acid fermentation processes. However, multiobjective optimization (MOO) can provide a broader range of optimal cultivation conditions that compromise between multiple objectives. This study applied multiobjective optimization of murta ( Ugni molinae Turcz) juice lactic fermentation under monoculture and coculture conditions. The total phenolic content (TPC), Lactic Acid Bacteria (LAB) count, and Lactic Acid (LA) concentration were optimized simultaneously. Decision variables were temperature, initial pH, and inoculation percentage. Several multicriteria decision-making (MCDM) methods were applied to select the optimal compromising responses, considering 80% priority for TPC, 15% for LAB, and 5% for LA. The highest TPC (723 ± 15 mg/L) was achieved at 35.3°C, pH 5.9, and 2% (v/v) inoculum in monoculture. The highest LAB count (6.7 ± 0.0 log CFU/mL) was obtained in monoculture at 34.6°C, pH 6, and 2.7% (v/v) inoculum. The highest LA (0.48 ± 0.03 g/L) was achieved in coculture at 35.6°C, pH 5.9, and 2.1% (v/v) inoculum. The simple additive weighting (SAW) method generated optimal solutions that aligned better with our expected priorities. Experimental validation confirmed that monocultures closely matched predicted optima, yielding higher TPC and LAB values than cocultures. While MOO and DF produced similar Pareto fronts, MOO required less computational time and generated more diverse solutions. These findings highlight the practical utility of MOO-MCDM for designing efficient multiobjective fermentation processes in functional food development.
This article proposes a novel technique for designing microstrip filtering patch antenna by etching the generalized filtering slot (GFS) on the patch. Surface current-controlled in-band multiple resonances and radiati...
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This article proposes a novel technique for designing microstrip filtering patch antenna by etching the generalized filtering slot (GFS) on the patch. Surface current-controlled in-band multiple resonances and radiation nulls in both sidebands can be achieved relying only on the configuration of GFS without any parasitic or stacked structures outside of the radiation patch, which ensures the compact size and low profile. The GFS with two-part structure can be effectively characterized by the distributed contour nodes with distance parameters and L-shaped slot lines with controllable turning points, whose geometry is completely encoded by a binary design matrix. The compact patch antenna with wideband, high selectivity and high suppression level can be acquired by implementing multiobjective optimization search for the optimal nodes distribution including feed points, along with the optimal distance parameters and slot width for slot construction. For demonstration, a filtering antenna with square patch working at 3.5 GHz is designed and fabricated, showing a realized gain of 5.8 dBi, impedance bandwidth of 12.0%, and suppression over 20 dB with steep roll-off.
To meet the demands of advanced electronic devices, inorganic glasses are required to have comprehensive dielectric, thermal, and mechanical properties. However, the complex composition–property relationship and vast...
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To meet the demands of advanced electronic devices, inorganic glasses are required to have comprehensive dielectric, thermal, and mechanical properties. However, the complex composition–property relationship and vast compositional diversity hinder optimization. This study developed machine learning models to predict permittivity, dielectric loss, thermal conductivity, coefficient of thermal expansion, and Young’s modulus based on the composition features of inorganic glasses. The optimal models achieve R 2 values of 0.9614, 0.7411, 0.9454, 0.9684, and 0.8164, respectively. By integrating domain knowledge with model-agnostic interpretation methods, feature contributions and interactions were analyzed. The mixed alkali effect is crucial for property regulation, especially Na-K for dielectric loss and Na-Li for thermal conductivity. Boron anomaly shifts the high-λ region to a balanced composition of alkali metals with rising B%. The multiobjective optimization of properties was realized using a genetic algorithm framework. After 23 iterations, the optimal material in the MgO-Al 2 O 3 -B 2 O 3 -SiO 2 system exhibits ε r = 4.78, tanδ = 0.00063, λ = 2.59 W/(m·K), α = 50.27×10 −7 K −1 , and E = 82.41 GPa, outperforming all materials in the dataset. The computational effort was reduced to 1/19 of that required using exhaustive search methods. This study provides a model interpretation framework and an effective multiobjective optimization strategy for glass design.
In industrial amine plants the optimized operating conditions are obtained from the conclusion of occurred events and challenges that are normal in the working units. For the sake of reducing the costs, time consuming...
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In industrial amine plants the optimized operating conditions are obtained from the conclusion of occurred events and challenges that are normal in the working units. For the sake of reducing the costs, time consuming, and preventing unsuitable accidents, the optimization could be performed by a computer program. In this paper, simulation and parameter analysis of amine plant is performed at first. The optimization of this unit is studied using Non-Dominated Sorting Genetic Algorithm-II in order to produce sweet gas with CO 2 mole percentage less than 2.0% and H 2 S concentration less than 10 ppm for application in Fischer-Tropsch synthesis. The simulation of the plant in HYSYS v.3.1 software has been linked with MATLAB code for real-parameter NSGA-II to simulate and optimize the amine process. Three scenarios are selected to cover the effect of （DEA/MDEA） mass composition percent ratio at amine solution on objective functions. Results show that sour gas temperature and pressure of 33.98 ？ C and 14.96 bar, DEA/CO 2 molar flow ratio of 12.58, regeneration gas temperature and pressure of 94.92 ？ C and 3.0 bar, regenerator pressure of 1.53 bar, and ratio of DEA/MDEA = 20%/10% are the best values for minimizing plant energy consumption, amine circulation rate, and carbon dioxide recovery.
In this paper we propose the use of the genetic algorithm (GA) as a tool to solve multiobjective optimization problems in structures. Using the concept of min-max optimum, a new GA-based multiobjective optimization te...
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In this paper we propose the use of the genetic algorithm (GA) as a tool to solve multiobjective optimization problems in structures. Using the concept of min-max optimum, a new GA-based multiobjective optimization technique is proposed and two truss design problems are solved using it. The results produced by this new approach are compared to those produced by other mathematical programming techniques and GA-based approaches, proving that this technique generates better trade-offs and that the genetic algorithm can be used as a reliable numerical optimization tool. (C) 2000 Elsevier Science Ltd. All rights reserved.
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