Non-dominated Sorting Genetic Algorithm II Research Articles

Exergoeconomic evaluation and multi-objective optimization of a novel geothermal-driven zero-emission system for cooling, electricity, and hydrogen production: capable of working with low-temperature resources

Geothermal energy is an abundant natural resource in many regions around the world. However, in some areas, the temperature of the geothermal energy resource is too low to be efficiently harvested. Organic Rankine cycles (ORCs) are known for recovering heat from low-temperature resources and generating electricity. Furthermore, half-effect absorption chillers (HEACs) are designed to produce cooling with low-temperature resources. This study proposes a novel configuration that utilizes an ORC for electricity generation, a HEAC for cooling production, and a PEM electrolysis system to produce hydrogen. The power section consists of two turbines, one driven by the vapor produced from the geothermal flow expansion, which powers the PEM section, while the other turbine in the ORC is used to drive pumps and electricity production. First, the system is thermoeconomically analyzed for an initial set of inputs. Then, various parameters are analyzed to determine their influences on system performance. The analyses reveal that the system can work with geothermal source temperatures as low as 80 °C, but the exergy and energy (thermal) efficiencies decrease to around 17% under the base settings. Furthermore, the system is capable of working with resource temperatures up to 170 °C. Ten parameters are found to affect the system’s efficiency and effectiveness. To optimize the system, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) is implemented to find the optimum conditions. The objective functions are exergy efficiency and unit polygeneration cost (UPGC), which can conflict. The optimization shows that the exergy efficiency of the system can reach 48% in the optimal conditions (for a heat source temperature of 112 °C and a mass flow rate of geothermal fluid of 44 kg/s), with a hydrogen production rate of 1.1 kg/h.

Joint altitude and beamwidth optimization for LEO satellite‐based IoT constellation

SummaryLow Earth Orbit (LEO) satellite‐based Internet of Things (IoT) has become a hot topic in IoT networks due to the ability of global coverage, especially in remote areas. How to design a commercial LEO satellite‐based constellation to meet the IoT traffic requirement remains an open problem. In this paper, we propose a throughput optimization algorithm for LEO satellite‐based IoT networks meanwhile reducing the number of LEO satellite. Based on stochastic geometry theory, a closed‐form expression is derived for the throughput of a dynamic LEO satellite‐based IoT networks when LEO satellite equips with capture effect (CE) receiver and successive interference cancelation (SIC) receiver, respectively. Furthermore, a joint altitude and beamwidth optimization problem is formulated under the constraint of Walker constellation to optimize the throughput and the number of LEO satellites. To solve this multi‐objective optimization problem, we design an iterative non‐dominated sorting genetic algorithm II (NSGA‐II) for the rapid development of IoT traffic. Simulation results show that our proposed algorithm can effectively improve the throughput performance of random access (RA) protocol in LEO satellite‐based IoT networks compared to benchmark problems.

Multi-objective optimization for electric discharge drilling of waspaloy: A comparative analysis of NSGA-II, MOGA, MOGWO, and MOPSO

This study investigates the impact of pulse-on-time (TON), current (I), pulse-off-time (TOFF), voltage (V), and tool electrode speed (TES) on material removal rate (MRR), average surface roughness (Ra), power consumption (PC) and surface defects for electrical discharge drilling (EDD) of Waspaloy. Based on a Taguchi design with five factors and four levels, experimental trials reveal TOFF, TON, and TOFF as the most influential factors, contributing 50.45%, 34.29%, and 33.09% to MRR, Ra, and PC, respectively. To address conflicting conditions and optimize responses, non-dominated sorting genetic algorithm-II (NSGA-II), multi-objective genetic algorithm (MOGA), multi-objective grey wolf optimizer (MOGWO), and multi-objective particle swarm optimization (MOPSO) are employed. Comparative analysis of 8 optimal solutions provided by four optimization techniques, which exhibit less than 10% errors in confirmatory experiments, has been done, ensuring better prediction accuracy. MOPSO is preferable to the rest of the optimization techniques due to its lower percentage error. NSGA-II, MOGA, MOGWO, and MOPSO exhibit relative improvement in responses based on confirmatory experiments, establishing them as effective tools for exploring EDD applications. Higher current is confirmed as a primary contributor to increased surface damage through microscopic image analysis, and EDX analysis exposes carbon deposition migration on both the tool and workpiece.

An Optimization Strategy for Truncating Ultralong Bus Lines Integrated with Metro Networks

For most ultralong bus lines, low punctuality rates and vehicle bunching are two main challenges. One potential strategy to solve these two problems is to split an ultralong line into two or three shorter lines according to historical passenger flow patterns as well as other presetting objectives and constraints. Thus, in this paper, a multiobjective optimization model is first established in consideration of both passenger flow distribution and coline metro networks. Then, the optimal truncated stop is determined by estimating the model results generated by enumerating each potential bus stop derived from related metro stops. The nondominated sorting genetic algorithm II (NSGA-II) is applied to solve the model efficiently. A case study conducted on a real bus line associated with metro networks in Beijing, China, demonstrates the efficiency of the optimization strategy adoption in terms of operating cost and passenger travel time, even with a slight increase in passenger cost. Besides, the multivehicle type is also referred in the experiments to verify economic efficiency to reduce operational costs.

Research on Multi-Objective Process Parameter Optimization Method in Hard Turning Based on an Improved NSGA-II Algorithm

To address the issue of local optima encountered during the multi-objective optimization process with the Non-dominated Sorting Genetic Algorithm II (NSGA-II) algorithm, this paper introduces an enhanced version of the NSGA-II. This improved NSGA-II incorporates polynomial and simulated binary crossover operators into the genetic algorithm’s crossover phase to refine its performance. For evaluation purposes, the classic ZDT benchmark functions are employed. The findings reveal that the enhanced NSGA-II algorithm achieves higher convergence accuracy and surpasses the performance of the original NSGA-II algorithm. When applied to the machining of the high-hardness material 20MnCrTi, four algorithms were utilized: the improved NSGA-II, the conventional NSGA-II, NSGA-III, and MOEA/D. The experimental outcomes show that the improved NSGA-II algorithm delivers a more optimal combination of process parameters, effectively enhancing the workpiece’s surface roughness and material removal rate. This leads to a significant improvement in the machining quality of the workpiece surface, demonstrating the superiority of the improved algorithm in optimizing machining processes.

Optimizing pump operations in water distribution networks: Balancing energy efficiency, water quality and operational constraints

Optimizing pump operation in urban water distribution systems represents a critical task for enhancing energy efficiency. Despite extensive research, achieving optimal pump operation remains a challenge due to the presence of conflicting objectives, such as minimizing energy costs and ensuring adequate pressure head availability. The recent emphasis on maintaining distributed water quality further increases the complexity of the problem. Mathematically, the optimization problem manifests as a non-convex and non-continuous search space, necessitating the application of non-derivative optimization techniques, which are highly susceptible to constraints. This work presents a comprehensive analysis of optimal pump operation, considering energy efficiency and water quality as primary objectives. A series of constraints are progressively incorporated into the model to evaluate their impact on the final operational strategy. Initially, single-objective optimization is employed using the Particle Swarm Optimization (PSO) algorithm, a robust and widely adopted approach in water resources research. Subsequently, a multi-objective algorithm, the Non-dominated Sorting Genetic Algorithm-II (NSGA-II), is utilized to identify the Pareto front, encompassing both energy consumption and water quality indicators. The results demonstrate that constraints exert a significant influence on the final energy consumption and the convergence of the optimization process. Moreover, by simultaneously considering both objectives, this study proves the feasibility of identifying an optimal operational scheme that minimizes energy consumption while maintaining acceptable water quality.

Sound transmission loss optimization of clamped double-panels

The panel cavity structure is one of the key components of the aircraft (vehicle) body and is among the main noise transmission pathways. Based on the modal superposition and Galerkin method, this paper realizes the theoretical model of sound insulation of the clamped, double-panel structure. The non-dominated sorting genetic algorithm-II (NSGA-II) is used to realize the sound insulation of the clamped double-panel structure. Through optimization, the fitting function and law of structural surface density and the optimized normal weighted sound insulation Pareto fronts were obtained. The results show that among the optimization, for the Pareto front cases, their double-panel thickness ratio h1/ h2 is relatively far away from 1, and the corresponding cavity thickness H is relatively large. The influence of boundary conditions and size effects of lightweight sound insulation optimization are also discussed. The research on the influence of boundary and size indicates that the difference in the optimal weighted sound insulation Pareto fronts corresponding to the same surface density is mostly within the 1 dB range. Both the boundary and thickness of the panel will affect the frequency STL, while the boundary conditions or structure size changed, even the total thickness of panels needs to be the same, and the structure can also have similar weighted sound transmission loss ( Rw) when the thickness ratio of the double-panel structure is chosen properly. The difference of material effects is also discussed. This research provides a method for the sound insulation optimization of clamped double-panel structures concerning the boundary and size effect.

Multi-objective optimization of a hydrogen production process for polymer membrane coupled with electrochemical hydrogen pump from hydrogen-doped natural gas by NSGA-II

In this work, a novel process of polymer membrane coupled with electrochemical hydrogen pump (EHP) was proposed to achieve efficient hydrogen production from hydrogen-doped natural gas (HDNG), and was simulated by UniSim Design. A hydrogen separation unit of two-stage polymer membrane was used for achieving hydrogen enrichment from low H2 concentration HDNG. Due to the hydrogen upgrade, EHP was operated at a H2 concentration much higher than HDNG, resulting in low energy consumption. Benefiting from the synergy between hydrogen upgrade and purification, the coupling process brought substantial effect while improving the hydrogen purity and recovery rate and reducing the hydrogen separation cost. The real natural gas components were considered into HDNG to ensure the effectiveness of process simulation. An optimization strategy considering both product yield and production cost was proposed to achieve the trade-off between hydrogen recovery rate and separation cost. However, the impact of process parameters on the objectives is nonlinear and difficult to describe with mathematical expressions. Therefore, an agent model of the proposed process was established based on back propagation neural network (BPNN), and multi-objective optimization of H2 recovery rate and total annual cost (TAC) of hydrogen separation unit was achieved using non-dominated sorting genetic algorithm-II (NSGA-II). Investigations with HDNG under various operating conditions (feed pressure of 8–40 bar and feed H2 concentration of 5–25 mol%) were performed, which proved that 90 % overall H2 recovery rate and 99.99 mol% H2 purity of hydrogen product can be achieved at low cost, with the lowest hydrogen separation cost of 1.00 $/kg H2 achievable at 40 bar feed pressure and 25 mol% feed H2 concentration. In summary, this coupling process can provide engineering solutions for efficient hydrogen production from HDNG and promote the development of green hydrogen.

Towards artificial intelligence empowered performance enhancement of EDM process using nano-graphene mixed bio-dielectric supporting the carbon neutrality and sustainable development

The growing population with every passing day sets an alarming situation with respect to the conservation climate protocols. The increasing needs of society also demand a significant enhancement in the manufacturing capacity to augment the situation. However, it's a stringent requirement of the hour to propose sustainable and clean manufacturing processes to realize the goal of carbon neutrality to support a healthy life on the earth. Specifically, the processes that are energy intensive like electric discharge machining (EDM) are of serious concern regarding sustainability viewpoint. The role of the said process cannot be essentially eliminated as advent of new materials of superior characteristics demand the application of EDM for accurate cutting of intricate profiles. Nevertheless, the commonly used oil-based dielectric (kerosene) in EDM releases aerosol, deposit particles, oxides of carbon (CO2 & CO), thus contributing to the environmental contamination. It is pertinent to mention that industries are compelled to tune their processes to achieve the goals of Net-Zero. Therefore, this study thoroughly investigates the potential of nano-graphene mixed rice bran oil to make the EDM process cleaner and sustainable which has never been investigated so far. Moreover, the process has been successfully modeled using artificial neural network (ANN) and optimized by non-dominated sorting genetic algorithm-II (NSGA-II) which is another novel aspect of this study as it eradicates the need of extensive experimentation. Experimentation has been performed via Taguchi's experimental strategy followed by a detailed explanation of the findings based on process physics. In comparison to the traditional dielectric an improvement of 98.8% in material removal rate (MRR) and 93.9% reduction in specific energy consumption (SEC) are realized if the said novel combination is applied without compromising the quality. CO2 emissions determined for both rice bran oil and kerosene oil have revealed that rice bran oil provides 99.96% lesser CO2 emission in comparison to its counterpart.

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.

Quantitative study of solid-state mechanical mixing by high-pressure torsion based on image processing

Mechanical alloying through the process of severe plastic deformation can significantly enhance the mechanical properties of dissimilar metals. However, uneven mixing of these materials can adversely affect their mechanical performance and may even lead to material failure. To quantitatively assess the degree of mixing between dissimilar materials, a mechanical mixing efficiency index (η) has been introduced. This index is calculated accurately by combining an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II) with computer image processing techniques. The study focuses on the Al-Zn mechanical mixture, establishing a parametric correlation between the mechanical mixing efficiency index and the microstructural evolution of mechanical mixing. The findings indicate that when the η value falls below 0.27865, the material transitions from the mechanical mixing stage to the chemical fusion stage. This research facilitates the controllable preparation of mechanical alloying, providing theoretical support and practical evidence for the process.

Multi-objective capacity configuration optimization of the combined wind - storage system considering ELCC and LCOE

The optimal capacity configuration of combined wind-storage systems (CWSSs) serves as a foundation and premise for building new electricity system. This paper proposes a novel capacity configuration model of CWSS. The model simultaneously considers economy, stability and low carbon by respectively minimizing Levelized cost of electricity (LCOE) and maximizing effective load carrying capacity (ELCC). The multi-objective optimization model is solved by Non-dominated Sorting Genetic Algorithm II (NSGA-II). And Multi-Attributive Border Approximation Area Comparison (MABAC) method is used to score the alternative solutions on the Pareto front and make decisions under different scenarios. The results of the case study show that: (1) The evaluation of the ELCC for CWSS can quantify its ability to replace the installed capacity of thermal power, thus greatly reducing the uncertainty of wind power output. (2) Under low-carbon scenario, economic scenario and standard scenario, the results of optimal capacity configuration for the CWSS show that LCOE and ELCC are negatively correlated. When the weights of ELCC and LCOE are equal, the three goals of economy, low carbon and safety and stability are achieved. (3) The results of a single sensitivity analysis on LCOE show that the sensitivity coefficient of electricity generation is the highest, exerting the greatest impact on the capacity configuration decision of the CWSS. A 20% increase in power generation can reduce LCOE by 0.1203RMB/kWh. This paper provides energy planning recommendations for decision-makers in CWSSs, contributing to the development of a reliable new electricity system.

Integrating NSGA-II and CFD for Enhanced Urban Airflow Prediction: Recalibration of Closure Coefficients for a Nonlinear Eddy Viscosity Model

Accurately predicting urban environmental airflows using the Reynolds-averaged Navier‒Stokes (RANS) approach presents significant challenges due to unsolved closure issues. Previous attempts to address these limitations through coefficient adjustments were constrained by isotropic assumptions, resulting in limited generalizability and reliability, especially for complex building configurations. In this study, we proposed a recalibration approach within a multi-objective optimization framework. Nondominant sorting genetic algorithm-II (NSGA-II) and computational fluid dynamics (CFD) numerical computations were utilized to optimize the closure coefficients of a nonlinear eddy viscosity (NLEV) model, with a focus on improving the accuracy of the mean velocity, turbulent kinetic energy, and airflow characteristic predictions. To ensure the robustness of the model, appropriate objective functions were carefully defined. The proposed approach was evaluated using three-dimensional (3D) building cases related to urban prototypes, and the results demonstrated the necessity of multi-objective optimization. Our findings indicated that the trade-off solution on the Pareto front consistently outperformed the baseline and single-objective optimal versions across different types of urban airflow, demonstrating superior generalizability. The simulation results of this solution exhibited closer agreement with the wind tunnel experimental data and provided more accurate distributions of the time-averaged values and airflow characteristics. The normalized root mean square errors are reduced by about 33%, 52%, 35% and 9% in velocity, and 31%, 25%, 24% and 11% in turbulent kinetic energy, respectively. These results underscore the effectiveness of the multi-objective optimization framework in addressing closure issues and improving the prediction accuracy of complex urban environmental airflows.

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.