Non-dominated Sorting Genetic Algorithm II Research Articles

A novel tree -like bionic structure for liquid-cooled lithium-ion battery plates

To maintain the optimal operating temperature range for the power batteries of new energy vehicles, this paper proposes a novel tree-structured channel cold plate. An orthogonal experimental approach was employed to investigate the effects of inlet flow rate m, inlet channel quantity n, channel width d, and hierarchy ratio α on battery temperatures Tmax, standard temperature difference Tσ, and pressure drop ΔP. Non-dominated sorting genetic algorithm II (NSGA-II) was utilized to obtain an optimized solution set. The results reveal that the tree-like cold plate configuration including m = 15 g/s, n = 7 and 8, d = 3 mm, and α = 1:2:1 demonstrates superior performance. Thus, the developed cold plate was further compared with the conventional straight-through cold plate design. The results indicate that the proposed tree-like cold plate exhibits the maximum decreases of 13.94% in Tmax, 52.94% in Tσ, and 61.5% in ΔP. The most significant improvement in the composite indicator PEC was 89%. In conclusion, the tree-like channel cold plate developed in this study provides excellent heat dissipation capability for power batteries, offering valuable insights for the thermal design of battery liquid cooling systems.

Optimal low-impact development Facility Design in Urban Environments: A multidimensional optimization approach employing slime mould and nondominated sorting genetic algorithms

This study presents the application of the slime mould algorithm (SMA) and the nondominated sorting genetic algorithm II (NSGA-II) to optimize low-impact development (LID) strategies in urban areas. The focus is on minimizing costs and improving water quality, using three LID practices (vegetated swales, bioretention systems, and porous pavements) in combination with a drainage system. The effectiveness of this stormwater management model (SWMM)-SMA-NSGA-II model is demonstrated in Tehran, Iran, where bioretention is found to be the most successful approach for improving water quality. The results also show that the SMA outperforms the NSGA-II in optimizing cost-effective LID solutions and is more computationally efficient, potentially due to its crossover operator. This research highlights the importance of considering qualitative aspects of urban runoff management. The study demonstrates that the combination of multiple LID strategies and the use of SMA and NSGA-II have the potential to achieve optimal solutions for managing stormwater in urban areas.

A logical retrofit strategy optimization framework for resiliency bridge infrastructure management considering life-cycle cost

Bridges play an important role in providing essential services to communities as one of the most critical components of transportation infrastructure. In this regard, selecting reliable, robust, and efficient indicators is necessary to prepare a disaster management strategy. This study presents a multi-objective optimization framework for decision-makers to find the most optimal retrofit strategies that satisfy a given threshold of functionality/Resilience (R) while minimizing a structure's Life-Cycle Cost (LCC). Accordingly, various retrofit strategies include different materials (steel, Carbon Fiber Reinforced Polymer (CFRP), and Glass Fiber Reinforced Polymer (GFRP)), thicknesses, arrangements, and timing of retrofitting actions. In each scenario, the fragility curves are derived through nonlinear time-history Incremental Dynamic Analysis (IDA) to evaluate the LCC and resilience. In the subsequent step, the LCC analysis is conducted, considering the proposed formulation of multiple occurrences of seismic events, which incorporates the effects of complete/incomplete repair actions of damage conditions induced by previous seismic events. This study employs an elitist Non-dominated Sorting Genetic Algorithm II (NSGA-II) to identify the optimal set of solutions. The various aspects of the optimal retrofit strategies are thoroughly investigated and discussed for a bridge as a case study infrastructure. Results show that the considered objectives lead to reasonable and sense-making retrofit strategies.

MEMS Gyroscope Temperature Compensation Based on Improved Complete Ensemble Empirical Mode Decomposition and Optimized Extreme Learning Machine.

Herein, we investigate the temperature compensation for a dual-mass MEMS gyroscope. After introducing and simulating the dual-mass MEMS gyroscope's working modes, we propose a hybrid algorithm for temperature compensation relying on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), sample entropy, time-frequency peak filtering, non-dominated sorting genetic algorithm-II (NSGA II) and extreme learning machine. Firstly, we use ICEEMDAN to decompose the gyroscope's output signal, and then we use sample entropy to classify the decomposed signals. For noise segments and mixed segments with different levels of noise, we use time-frequency peak filtering with different window lengths to achieve a trade-off between noise removal and signal retention. For the feature segment with temperature drift, we build a compensation model using extreme learning machine. To improve the compensation accuracy, NSGA II is used to optimize extreme learning machine, with the prediction error and the 2-norm of the output-layer connection weight as the optimization objectives. Enormous simulation experiments prove the excellent performance of our proposed scheme, which can achieve trade-offs in signal decomposition, classification, denoising and compensation. The improvement in the compensated gyroscope's output signal is analyzed based on Allen variance; its angle random walk is decreased from 0.531076°/h/√Hz to 6.65894 × 10-3°/h/√Hz and its bias stability is decreased from 32.7364°/h to 0.259247°/h.

Control co-design optimization of floating offshore wind turbines with tuned liquid multi-column dampers

Abstract. The technical progress in the development and industrialization of floating offshore wind turbines (FOWTs) over the past decade has been significant. Yet, the higher levelized cost of energy (LCOE) of FOWTs compared to onshore wind turbines is still limiting the market share. One of the reasons for this is the larger motions and loads caused by the rough environmental excitations. Many prototype projects tend to employ more conservative substructure designs to meet the requirements for motion dynamics and structural safety. Another challenge lies in the multidisciplinary nature of a FOWT system, which consists of several strongly coupled subsystems. If these subsystems cannot work in synergy, the overall system performance may not be optimized. Previous research has shown that a well-designed blade pitch controller is able to reduce the motions and structural loads of FOWTs. Nevertheless, due to the negative aerodynamic damping effect, improvement in the performance by tuning the controller is limited. One of the solutions is adding tuned liquid multi-column dampers (TLMCDs), meaning that there is a structural solution to mitigate this limiting factor for the controller performance. It has been found that the additional damping, provided by TLMCDs, is able to improve the platform pitch stability, which allows a larger blade pitch controller bandwidth and thus a better dynamic response. However, if a TLMCD is not designed with the whole FOWT system dynamics taken into account, it may even deteriorate the overall performance. Essentially, an integrated optimization of these subsystems is needed. For this paper, we develop a control co-design optimization framework for FOWTs installed with TLMCDs. Using the multi-objective optimizer non-dominated sorting genetic algorithm II (NSGA-II), the objective is to optimize the platform, the blade pitch controller, and the TLMCD simultaneously. Five free variables characterizing these subsystems are selected, and the objective function includes the FOWT's volume of displaced water (displacement) and several motion and load indicators. Instead of searching for a unique optimal design, an optimal Pareto surface of the defined objectives is determined. It has been found that the optimization is able to improve the dynamic performance of the FOWT, which is quantified by motions and loads, when the displacement remains similar. On the other hand, if motions and loads are constant, the displacement of the FOWT can be reduced, which is an important indication of lower manufacturing, transportation, and installation costs. In conclusion, this work demonstrates the potential of advanced technologies such as TLMCDs to advance FOWTs for commercial competitiveness.

Crushing Response and Optimization of a Modified 3D Re-Entrant Honeycomb.

A modified 3D re-entrant honeycomb is designed and fabricated utilizing Laser Cladding Deposition (LCD) technology, the mechanical properties of which are systematically investigated by experimental and finite element (FE) methods. Firstly, the influences of honeycomb angle on localized deformation and the response of force are studied by an experiment. Experimental results reveal that the honeycomb angles have a significant effect on deformation and force. Secondly, a series of numerical studies are conducted to analyze stress characteristics and energy absorption under different angles (α) and velocities (v). It is evident that two variables play an important role in stress and energy. Thirdly, response surface methodology (RSM) and the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) are implemented with high precision to solve multi-objective optimization. Finally, the final compromise solution is determined based on the fitness function, with an angle of 49.23° and an impact velocity of 16.40 m/s. Through simulation verification, the errors of energy absorption (EA) and peak crush stress (PCS) are 9.26% and 0.4%, respectively. The findings of this study offer valuable design guidance for selecting the optimal design parameters under the same mass conditions to effectively enhance the performance of the honeycomb.

Multi-objective optimization strategy for multipolar magnesium electrolysis cell based on thermal-electric model

In the operation of industrial multipolar magnesium electrolysis cell, cell voltage and current density are important indicators of the production performance of the cell, while thermal balance is a key factor for normal operation. This paper utilizes the finite element method to establish a three-dimensional full cell thermal-electric model, studying the thermal field distribution and current density distribution in a 165kA multipolar magnesium electrolysis cell. Based on the heat balance of the multipolar cell, the influence of structural parameters on current intensity, average current density at the anode surface and resistance voltage is assessed. Dimensionless analysis yielded optimization criteria, and the Non-dominated Sorting Genetic Algorithm II (NSGA-II) was employed for multi-objective optimization problems. When stringent optimization is executed, significant performance improvements can be achieved in terms of the electrolysis cell's resistance voltage and current density. Moreover, under the optimization criteria, the relative error of the 265kA multipolar magnesium electrolysis cell model is less than 1 %. The results indicate that the workflow combining the finite element method with multi-objective optimization algorithms can be applied to the optimization and scale-up design of high-current multipolar magnesium electrolysis cell.

Multi-objective optimization for low hydrogen consumption and long useful life in fuel cell emergency power supply systems

The power system using hydrogen fuel cells (FCs) as the primary energy source holds significant potential for applications in emergency fields such as post-disaster relief and outdoor power supply. The operational environment of the FC emergency power supply system (FCEPSS) is challenging, with long continuous operation time, difficulties in hydrogen supply, high maintenance costs, and, accordingly, requirements for hydrogen consumption and useful life of FCs are heightened. Hence, this paper proposes a multi-objective energy management strategy (EMS) for FCEPSS. First, the structure of FCEPSS is designed based on the demand analysis in emergency scenarios. Then, the hydrogen consumption and life degradation models of FCEPSS are established. Aiming at the multi-objective optimization problem of minimizing hydrogen consumption and life degradation value in the FCEPSS, an improved multi-objective optimization algorithm is proposed. This paper converts complex constraints into the penalty term and incorporates it as one of the objectives, constructing a three-objective optimization model. To enhance the solution of multi-objective optimization problems, an improved strategy based on relaxation-tightening and three-objective sorting is proposed. Combining the fast non-dominated sorting genetic algorithm-II and the above improved strategy, an improved multi-objective optimization algorithm (IMOOA) is constructed. In the simulation, the advantages of the proposed IMOOA algorithm are verified. In the experimental part, compared with the logical threshold strategy, the proposed strategy saves 0.60% of hydrogen consumption and reduces 5.65% of life degradation within 24 h, respectively. Finally, the superiority of the multi-objective EMS utilizing the IMOOA is demonstrated.

Metaheuristic approach to multi-objective ergonomic workforce scheduling for Bi-hazard industrial tasks

ABSTRACT This paper considers the daily scheduling of workers who are simultaneously exposed to two ergonomic hazards when performing their assigned tasks. Safe daily rotating work schedules are generated to minimize the maximum exposure amounts of both hazards simultaneously. A non-dominated sorting genetic algorithm II (NSGA-II) method is employed to generate near-optimal workforce scheduling solutions. A numerical example shows that for a small multi-objective problem, the NSGA-II is able to yield the same set of non-dominated Pareto points as those obtained from a multi-objective optimization model with ε-constraint method. A computational experiment further demonstrates that the NSGA-II can yield acceptable non-dominated Pareto points in reasonable computation times irrespective of the problem size.

Optimizing the Building Refurbishment Process Using Improved Evolutionary Algorithms

An optimization model that may be applied to analyze building retrofit strategies is presented in this research. The aim of this research paper is to identify the optimal thermal envelope configuration that will assure the minimum energy requirement for heating in the case of a residential building, while also considering price restrictions obtained through a specific market survey. To achieve this, several values for the following parameters are considered: thermal insulation materials’ conductivities and thicknesses, windows’ overall heat transfer coefficients and total solar energy transmittance and doors’ thermal proprieties. Additionally, this paper presents a method used to find the best option from among the available heat pumps that could cover most of the energy requirements for heating and domestic hot water systems, also considering the products’ prices. The proposed method is based on a Non-dominated Sorting Genetic Algorithm II (NSGA-II) model developed in the Pymoo (Multi-Objective Optimization in Python) library. The result shows that the energy requirement for heating can be reduced by up to approximately 75% compared to that obtained in the case of a non-insulated building by using suitable insulation materials and doors and windows with superior thermal proprieties chosen by the NSGA-II.

Enhancing heat transfer efficiency in corrugated tube heat exchangers: A comprehensive approach through structural optimization and field synergy analysis

In this paper, a precise and efficient method to optimize corrugated tube heat exchangers is proposed by combining computational fluid dynamics simulation with optimization. The optimization of tubular heat exchangers involves contradictory Colburn coefficient j, and the friction coefficient f, so it is a multi-objective optimization problem. The approximate model is obtained by an extreme learning machine, and the structure parameter of the heat exchanger is optimized by the nondominated sorting genetic algorithm-Ⅱ. Compared to the results between the original and optimized tube, the optimized structure Colburn coefficient increased by 5.1 % and the friction coefficient decreased by 9.3 %. Finally, the internal flow field is compared qualitatively from temperature, pressure, and velocity. The optimization effect is further emphasized by using the field synergy theory.

Multiple objective energy optimization of a trade center building based on genetic algorithm using ecological materials

It is crucial to optimize energy consumption in buildings while considering thermal comfort. The first step here involved an EnergyPlus simulation on a trade center building located in Tehran, Bandar Abbas, and Tabriz, Iran. A multi-objective optimization was then performed based on non-dominated sorting genetic algorithm II (NSGA-II) in jEPlus + EA to establish the building in the selected city where would benefit the most from implementing the radiant ceiling cooling system. Efforts were undertaken to choose environmentally-friendly materials. The final solution by Pareto charts resulted in a 52% reduction in energy consumption, a 37.3% decrease in cooling load, and a 17.4% improvement in comfort hours compared to the original design. Annual emission of greenhouse gas reduced as 167.67 tone of CO2 equivalent emission, 25.77 ton of CH4, and 0.2 ton of NO2. The mentioned algorithm was conducted for the first time on a trade center, including a DOAS system and radiant ceiling cooling system. Simultaneously, the environmental-friendly materials were dealt with. The procedure holds significant relevance for the design and optimization of buildings in Iran, especially wherever the climate is hot and humid. This approach offers advantages to the environment by reducing the impact on energy resources and utilizing environmentally-friendly materials.

Design and Optimization of Air Inlet in Cuttings Incubator

The microclimate environment can be conveniently controlled with accuracy by plant incubators, in which the cuttings propagation method can efficiently enhance seedling production. To ensure air flow evenly throughout the incubator, the scientific design of the air inlet is crucial. This study utilized a computational fluid dynamics (CFD) model to simulate the airflow patterns in a culture layer under different air inlet conditions. Furthermore, the optimal design parameters were determined by way of response surface methodology (RSM) and the Non-dominated Sorting Genetic Algorithm-II (NSGA-II). Adopting the optimal parameters, a prototype was manufactured, and a cuttings experiment was carried out with apple cuttings in the incubator. The results showed that the optimal air inlet radius is 90 mm, the optimal air inlet height is 188 mm, and the optimal uniform flow plate hole diameter is 13 mm. Meanwhile, the apple cuttings were able to root. Therefore, this incubator with optimal parameters can be used for cuttings. The study provides a methodological and theoretical basis for the future optimizing of air inlet parameters and promoting cuttings rooting.

Machine learning-based optimal design of an acoustic black hole metaplate for enhanced bandgap and load-bearing capacity

This paper introduces a novel machine learning-based optimization strategy for multi-functional acoustic black hole (ABH) metaplates. The primary objective is to achieve a multi-functional metaplate with excellent performance in elastic wave attenuation and load-bearing capacity simultaneously. The paper begins by describing the design of nanocomposite ABH metaplates, presenting a new pathway to realize multi-functional metaplates. Then, a semi-analytical method, based on plate theory and the Bloch–Floquet theorem, is introduced to consider the band structure of the nanocomposite metaplates. Through systematic analysis, the impacts of the ABH effect, nanocomposite reinforcements, and the viscoelastic damping layer on the bandgaps and strain energy compliance are highlighted. Meanwhile, two optimization objectives representing bandgap characteristics and in-plane stiffness are derived respectively. Subsequently, a deep learning surrogate model is employed to establish a relationship involving significant parameters with the optimization objectives. The performance evaluation confirms accuracy and computational speed of the surrogate model. Finally, an optimization strategy based on deep reinforcement learning is proposed to obtain multi-functional metaplates with superior bandgaps, enhanced in-plane stiffness, or both. The robustness and efficiency of the strategy are demonstrated under different tests. The results show that the proposed strategy can achieve identical results as the genetic algorithm and nondominated sorting genetic algorithm-II, while surpassing them in computational efficiency and balancing multiple objectives. The findings of this study serve as valuable references for the future development and application of multi-functional advanced metamaterials.

Event identification in acoustic emission from wire breaks in pre-stressing/post-tensioning cables

Steel tendons commonly used in pre-stressed/post-tensioned concrete structural systems can lose cross-section due to corrosion, eventually leading to acoustic emission (AE) events when the stress exceeds the breaking strength of the wires that make up the tendons. Reliable differentiation of wire break AE events from traffic or grout crack events is critical for monitoring large structures, even where the distance between sensors may produce highly attenuated signals. In this paper, the Fuzzy c-means clustering algorithm was employed to differentiate AEs released from breaking wires of steel tendons from a database of 13464 AEs, including wire breaks, environmental and grout crack AEs. Wire breaks and grout crack AEs were collected from axial loading tests of grouted tendons in which the load increased until a wire broke. Environmental acoustic signals were collected from a bridge. Then all the collected AEs were gathered in a database and post-processed to simulate attenuation of up to 20 m from source to sensor. To optimize the speed and reliability of the Fuzzy c-means clustering algorithm, a non-dominated sorting genetic algorithm-II (NSGA-II) was used to find the minimum number of acoustic features needed. The NSGA-II algorithm started with 201 possible acoustic features and found 12 combinations of features that resulted in more than 80% wire break detection accuracy. In contrast, less than 3% of grout cracks and 0% of environmental signals were detected as wire breaks. The proposed method is suitable for deployment in a large sensor network and has sufficiently low-computational requirements for at-the-sensor processing, eliminating the need to send high-frequency sampled data outside the sensor node.

Constructing a MOEA approach for product form Kansei design based on text mining and BPNN

Kansei Engineering (KE) is a product design method that aims to develop products to meet users’ emotional preferences. However, traditional KE faces the problem that the acquisition of Kansei factors does not represent the real consumers demands based on manual and reports, and using traditional methods to calculate relationship between Kansei factors and specific design elements, which can lead to the omission of key information. To address these problems, this study adopts text mining and backward propagation neural networks (BPNN) to propose a product form design method from a multi-objective optimization perspective. Firstly, Term Frequency-Inverse Document Frequency (TF-IDF) and WordNet are used to extract key user Kansei requirements from online review texts to obtain more accurate Kansei knowledge. Secondly, the BPNN is used to establish the non-linear relationship between product Kansei factors and specific design elements, and a preference mapping prediction model is constructed. Finally, BPNN is transformed into an iterative prediction value of non-dominated sorting genetic algorithm-II (NSGA-II), and the model is solved through multi-objective evolutionary algorithm (MOEA) to obtain the Pareto optimal solution set that satisfies the user’s multiple emotional needs, and the fuzzy Delphi method is used to obtain the best product form design scheme that meets the user’s multiple emotional images. Using the example of electric bicycle form design could show that this proposed method can effectively complete multi-objective product solutions innovation design.

A Bibliometric Analysis of a Genetic Algorithm for Supply Chain Agility

As a famous population-based metaheuristic algorithm, a genetic algorithm can be used to overcome optimization complexities. A genetic algorithm adopts probabilistic transition rules and is suitable for parallelism, which makes this algorithm attractive in many areas, including the logistics and supply chain sector. To obtain a comprehensive understanding of the development in this area, this paper presents a bibliometric analysis on the application of a genetic algorithm in logistics and supply chains using data from 1991 to 2024 from the Web of Science database. The authors found a growing trend in the number of publications and citations over the years. This paper serves as an important reference to researchers by highlighting important research areas, such as multi-objective optimization, metaheuristics, sustainability issues in logistics, and machine learning integration. This bibliometric analysis also underlines the importance of Non-Dominated Sorting Genetic Algorithm II (NSGA-II), sustainability, machine learning, and variable neighborhood search in the application of a genetic algorithm in logistics and supply chains in the near future. The integration of a genetic algorithm with machine learning is also a potential research gap to be filled to overcome the limitations of genetic algorithms, such as the long computational time, difficulties in obtaining optimal solutions, and convergence issues for application in logistics and supply chains.

Multi-Objective Optimization of an Organic Rankine Cycle (ORC) for a Hybrid Solar–Waste Energy Plant

In pursuit of sustainable development and mitigation of the intermittency challenge associated with solar energy, this study proposes a hybrid solar system integrating waste heat incineration alongside solar power generation and distinct heat provision. Leveraging the superior energy efficiency of the organic Rankine cycle (ORC) in medium- and low-temperature scenarios, a parabolic trough collector (PTC) is selected for its cost-effectiveness and long-term operational reliability. Dowtherm A and toluene are identified as the optimal working fluids for the PTC and ORC, respectively. To optimize this complex system, a combination of artificial neural networks (ANNs) and multi-objective optimization via non-dominated sorting genetic algorithm II (NSGA-II) is employed, streamlining the optimization process. Thermal dynamic simulations are executed using Engineering Equation Solver (EES, V11) to validate the proposed system’s performance. TOPSIS is employed to identify the optimal solution from the Pareto frontier. The results indicate that the hourly cost of the system stands at USD 43.08, with an exergy efficiency of 22.98%. The economic analysis reveals that the solar collector constitutes the most significant portion of the total initial cost, representing 53.2%, followed by the turbine, thermoelectric generator (TEG), and waste heat incineration, in descending order of costliness.

Multi-objective optimal input design for grey-box identification modelling of ship manoeuvring motion

ABSTRACT Grey-box modelling has been widely used to predict the ship manoeuvring motion. In order to design input signals for the grey-box model of ship manoeuvring motion, a multi-objective optimal input design (MOID) method is proposed in this paper. In the proposed method, the MOID is regarded as a multi-objective optimization problem (MOP), where both of D-optimality criterion and condition number are taken as two-objective functions to improve the accuracy and robustness of identifying the grey-box model. The correlation factor is also presented as a constraint for reducing the parameter drift. The MOP is solved by the non-dominated sorting genetic algorithm-II (NSGA-II) with a constraint handling technique. The technique for order preference by similarity to an ideal solution (TOPSIS) is also adopted to screen out an optimal solution from the Pareto solutions. The effectiveness of MOID signals is validated by Monte Carlo analysis and grey-box identification of the MMG model.

Optimized k-nearest neighbors for classification of prosthetic hand movements using electromyography signal

Electromyography (EMG) signals are essential, as they are used to measure muscular activity in different parts of the human body. The measurement and analysis of EMG signal lead to various applications of muscle disorders such as muscular dystrophy, myopathy, hand movements, etc. In this paper, an improved and effective hand movement classification model is developed for amputee subjects. It includes: (1) EMG feature extraction using Discrete Wavelet Transform (DWT), (2) EMG feature selection using binary Global Best Guided Gaussian Artificial Bee Colony (BGGABC), (3) Hand movements classification using Optimized k-nearest neighbors (OKNN) classifier. The EMG signal is taken from the DB3 of NinaPro dataset comprising 17 different prosthetic hand movements recorded from 11 amputee subjects. Thereafter, DWT is applied to decompose the EMG signal for extracting features. An improved wrapper-based feature selection technique (BGGABC) is used to select the optimal feature subset for effective classification. The two variants of KNN, i.e. Smallest Modified KNN and Largest Modified KNN are taken in which item strength to a class is optimized for efficient classification. The strength of an item to a class depends on distance and weight of an item to a class. Therefore, a multi-objective Non-dominated sorting genetic algorithm-II (NSGA-II) is used for optimizing these two contradictory parameters (distance and weight) simultaneously to have optimized variants, namely: Optimized Smallest KNN (OSKNN) and Optimized Largest KNN (OLKNN). Extensive results show that the proposed method OKNN achieved the highest classification accuracy of 93.07% (OLKNN) and 89.43% (OSKNN) compared with KNN variants and competitors.