Multi-objective Genetic Algorithm Research Articles

Optimal design of vibration resistance of fiber-reinforced composite sandwich plates embedded in a viscoelastic square honeycomb core

This work focuses on investigating the optimal design of composite sandwich plates (FCSPs) with a viscoelastic square honeycomb core (VSHC). Firstly, using the cross-fill theory, the complex modulus technique, the first-order shear deformation theory, the minimum strain energy principle, and the Newmark-β method, a theoretical model of the VSHC-FCSPs under half-sine pulse excitation is formulated to calculate the inherent frequencies, the peak and vibration decay time of the transient response in time domain. The peak and vibration decay time are taken as the indexes of the anti-vibration performance. Considering an index of structural stiffness performance, the average value of the inherent frequencies is adopted to calculate the overall stiffness. After a set of literature validations and optimization validations, the multi-objective genetic algorithm is employed to study the optimization issue of VSHC-FCSPs. The optimization objectives are to minimize the three design variables of the transient response peak, vibration decay time, and reciprocal of overall stiffness. Then, the fiber laying angle of each layer, the core thickness ratio and the modulus ratio are assumed as optimization variables. Finally, the results with good vibration resistance and structural stiffness in the Pareto front are chosen as references, and these corresponding variations of the design variables and optimization objectives are obtained. The optimization results have revealed that the optimization variables corresponding to the intermediate points should be selected as references to improve the anti-vibration capacity and ensure the structural stiffness performance.

Thermo-economic investigation and comparative multi-objective optimization of dual-pressure evaporation ORC using binary zeotropic mixtures as working fluids for geothermal energy application

This contribution performs an energy, exergy, and exergoeconomic (3E) analysis of a dual-pressure evaporation organic Rankine cycle system employing twenty different binary zeotropic mixtures as working fluids for power production from a geothermal field. To this end, the designed system is modeled, invoking the mass and energy conservation laws and the exergy and cost balance analyses. Specific Exergy Costing (SPECO) procedure is utilized to provide practical insights into the exergoeconomic aspect of the system. Comparative optimization based on the multi-objective genetic algorithm is accomplished for each mixture in order to simultaneously maximize the exergy efficiency and minimize the total cost rate using the design variables of pressure factor in low-pressure and high-pressure stages, mixture fraction, pinch point temperature differences in low-pressure and high-pressure heat exchangers, and degree of superheat in the high-pressure heat exchanger. In this regard, the Pareto frontiers are drawn for the system with all twenty different binary zeotropic mixtures. The optimal point for each mixture is obtained via the decision-making technique of LINMAP. Subsequently, the LINMAP is re-utilized to find the preferred mixture. The optimization results suggest the R123/C2Butene (96.89/3.11) mixture for this system as the optimum working fluid, considering a trade-off between a low-cost rate of $88.0651 per hr and a high exergy efficiency of 64.07 %. Finally, the exergy flow diagram is plotted to provide the exergy flow rate and the amount of exergy destruction in each segment of the system considering the optimal working fluid. In the proposed system, exergy destruction chiefly occurs within the low-pressure preheater with a value of 1061 kW, followed by the low-pressure turbine and condenser with magnitudes of about 669 kW and 266 kW, respectively.

Optimization Design of Bionic Leading-edge Protuberances on Horizontal Axis Wind Turbine Blades

Abstract The complex operating environments of horizontal axis wind turbines has made the phenomenon of blade flow separation more obvious. As a passive flow control method, the bionic humpback whale leading-edge protuberances have been shown to effectively enhance the performance of the wind turbine. This study employs a multi-objective genetic algorithm and Computational Fluid Dynamics method to analyse the impact of leading-edge protuberances on the aerodynamic performance and flow characteristics of the blades. The results indicate a significant improvement in blade performance after optimization in the wind turbine’s output power. The streamwise vortices generated by the bionic protuberances not only reduce the suction surface pressure, but also suppress the spanwise flow over the blade surface. The length of the flow separation Region is reduced from 0.410 to 0.368 radius. In the process of wind speed change, the tangential forces on the bionic blade exhibit a wave-like variation. The bionic protuberance alters the pressure coefficient of the position. Compared with the original blade, the pressure coefficient of the trough sections on both sides of the bionic blade protuberance is improved. In this study, the bionic protuberance applied to the leading-edge of horizontal-axis wind turbine blades is proposed for the first time, and holds practical value for guiding practical applications.

Enhancing Oil Recovery in Eagle Ford Shale: A Multiscale Simulation Study of Surfactant Huff ’n’ Puff Methodology

In this paper, we present a simulation case study of a surfactant huff ’n’ puff pilot in the black oil window of the Eagle Ford (EF) Shale. The target horizontal well, which had been depleted for nearly 8 years, underwent stimulation via a surfactant huff ’n’ puff treatment. The surfactant was selected through laboratory screening using reservoir rock and fluid samples. After a 17-hour injection and a 1-month shut-in period, the well’s production increased fivefold from the baseline oil rate, sustaining incremental oil production for at least 2 years. The surfactant enhances oil recovery by altering rock wettability toward a more water-wet state and moderating oil/water interfacial tension (IFT). This process is modeled by surfactant adsorption in the simulator, indicating the degree of dynamic changes in relative permeability (krl) and capillary pressure (Pc) curves. We propose a comprehensive workflow comprising three stages: development of core-scale and field-scale models, sequential model calibrations, and multiobjective optimization to integrate laboratory measurements and field data from this pilot into multiscale numerical simulations. By matching oil recoveries from imbibition experiments on the core model and field production histories on the field model, krl and Pc profiles of two extreme states, basic reservoir properties, and additional reservoir properties altered during huff ’n’ puff operations are characterized. The matched core model reproduces a 15.1% incremental oil recovery for surfactant-assisted spontaneous imbibition (SASI) process relative to pure brine imbibition process. The matched reservoir model predicts the surfactant huff ’n’ puff treatment increases the oil production by 21.9% relative to water huff ’n’ puff treatment and by 52.9% relative to primary depletion for a 4-year period. The calibrated reservoir model also serves as a base case for optimizing well operation schedules through the implementation of a multiobjective genetic algorithm. The surfactant injection rate, injection time, and well shut-in time of the base case are varied to achieve higher oil production and reduced surfactant usage. Statistical analysis of eight trade-off cases indicates that optimal well operations, compared with existing practices, frequently involve increased injection rates [16.6–18.9 barrels per minute (bpm)], shorter injection periods (10–11.3 hours), and prolonged shut-indurations (49–65 days). This workflow offers valuable insights into surfactant huff ’n’ puff treatments for unconventional reservoirs, thereby facilitating the optimization of well operations and maximizing tertiary oil recovery.

Data-driven multi-objective optimization of aerodynamics and aeroacoustics in dual Savonius wind turbines using large eddy simulation and machine learning

Savonius rotor is a popular form of vertical-axis wind turbine (VAWT) for small-scale and urban applications because of its straightforward design and self-starting ability. Dual VAWTs present challenges in terms of wake interactions and noise, particularly in urban areas. Optimizing these parameters is essential for future wind energy adoption. This research is the first to analyze how the interaction of wakes from adjacent rotors, combined with a deflector, affects both the aerodynamic performance and noise levels of dual Savonius rotors. Large Eddy Simulation is applied, as it effectively captures detailed turbulent wind flows and their interactions with wind turbines. A multi-objective optimization method combining Machine Learning and Computational Fluid Dynamics (CFD) is developed to optimize rotors for maximum power efficiency and minimum noise, considering their wake interactions with a unique deflector system. First, the influence of geometric parameters on aerodynamics and aeroacoustics characteristics of rotors is analyzed, and the database is generated using Design of Experiment approach. Next, the CFD model is replaced by Artificial Neural Network (ANN) model established for predicting rotor performances. A Multi-Objective Genetic Algorithm method is used to optimize aerodynamics and aeroacoustics characteristics of rotors. Finally, optimal design parameters are identified from the Pareto front using the technique for order of preference by similarity to ideal solution decision-making method. The ANN model demonstrated high accuracy with an RANN2 of 0.995 and 0.971 for the average power coefficient (CP) and overall sound pressure level (OSPL) predictions, respectively. Multi-objective optimization revealed the best configuration of the deflector with bleed jets, improving the average CP up to 57.5% and reducing OSPL to an almost 5.2% compared to the dual rotor case at TSR = 0.8.

MACHINING OF INCONEL-825 SUPERALLOY WITH MAGNETIC FIELD ASSISTED ELECTRICAL DISCHARGE TURNING: A OPTIMIZATION APPROACH USING RESPONSE SURFACE METHODOLOGY AND MULTI-OBJECTIVE GENETIC ALGORITHM

The aim of this study is to highlight the importance of optimizing machining parameters to improve the performance and surface integrity of Inconel-825 superalloy using the Electrical Discharge Turning (EDT) process, an important configuration of Electrical Discharge Machining (EDM). The study uses a Face-Centered Central Composite Design (FCCCD) to conduct experiments and applies the Response Surface Methodology (RSM) and multi-objective genetic algorithm (MOGA) to optimize input parameters. Various factors like Gap Current (Ig), pulse on time (Ton), rotational speed (N), and Magnetic field assistance (B) are adjusted at different levels, while outcomes such as Material Removal Rate (MRR), Tool Wear Rate (TWR), Overcut (OC), and Surface Roughness (Ra) are measured. Analysis of Variance (ANOVA) is used to understand the impact of each input factor on the outcomes. The results demonstrate that both RSM and MOGA provide accurate predictions closely aligned with experimental results, with MOGA showing a slight advantage in predicting tool wear and surface roughness. Specifically, the RSM solution achieved a desirability of 0.693 with parameters Ig at 8 A, Ton at 48.082[Formula: see text][Formula: see text]s, speed at 1399.988[Formula: see text]RPM, and magnetic field at 0.3[Formula: see text]T, achieving MRR of 5.182[Formula: see text]mg/min, TWR of 3.138[Formula: see text]mg/min, OC of 166.716[Formula: see text][Formula: see text]m, and Ra of 2.047[Formula: see text][Formula: see text]m. The MOGA solution featured parameters Ig at 8.045 A, Ton at 48.557[Formula: see text][Formula: see text]s, speed at 1360.3 RPM, and magnetic field at 0.3[Formula: see text]T, yielding an MRR of 5.169[Formula: see text]mg/min, TWR of 2.983[Formula: see text]mg/min, OC of 170.037[Formula: see text][Formula: see text]m, and Ra of 2.060[Formula: see text][Formula: see text]m. SEM analysis confirmed improved surface quality under optimized conditions, while XRD analysis showed significant grain refinement and increased dislocation density.

Towards sustainable living in high radiation cold climates: A two-phase genetic algorithm approach for residential building optimization

The High Radiation Cold (HRC) region's unique climatic conditions, characterized by high solar radiation and low air temperatures, present distinct challenges for optimizing building climate responsiveness. In this study, residential building design is bifurcated into two phases: building geometry (Phase I) and envelope design (Phase II). In Phase II, we propose a heterogeneous vertical envelope (HVE) design to suit HRC climates. This research framework establishes multiple objectives: energy efficiency, indoor comfort, and economy. It aims to identify a balanced and sustainable design by optimizing various parameters and materials using a Multi-Objective Genetic Algorithms（MOGA). The results indicate enhanced building adaptability in HAV climates through MOGA. The optimal solution in phase I results in an annual heating load of 59.12 W/m2, 33.38 % annual indoor visual comfort hours and the adaptive model predicts 35.59 % annual comfort hours. Incorporating phase II optimization, the total annual energy demand is reduced by 34.42 % with an 11.35 % improvement in thermal comfort hours, culminating at 37.8 %.

Optimization and mechanistic analysis of the configurational parameters of a serrated trench for improving film cooling performance

Combinations of trench and holes in film cooling design for turbine blades have been suggested recently. In this work, structural optimization is performed for one of our previously proposed serrated trenches. Geometric parameters including serrated angle, width, and height of the trench, which are the key factors affecting the flow and cooling characteristics, are optimized. The relative area-averaged cooling effectiveness, the relative pressure drop coefficient, and the performance evaluation criterion (PEC) are the optimizing objectives. The multi-objective genetic algorithm is employed as the search strategy to achieve PEC maximization at blowing ratios in the range of 0.5–2.0. The individual variations of each parameter are studied by controlling the variables using the response surface method. It shows that the trench height is the most influential factor on flow and heat transfer; while the trench serrated angle mainly affects the heat transfer; and the trench width has a weak effect on both, depending on the blowing ratio. To achieve maximum PEC, the trench height needs to enlarge with the increase in blowing ratio, while contrary to this, the trench width needs to increase under low blowing ratios and decrease under high blowing ratios, and the optimum trench serrated angle is within the range of 80°–85° at all blowing ratios. The optimum geometry reduces the pressure loss while improves the cooling effectiveness by 8.43 %–17.97 % compared to the baseline trench. This work is instructive for the design and application of practical structures for blade cooling.

Design and Assessment of an Austenitic Stainless Alloy for Laser Powder Bed Additive Manufacturing

Recent developments in metallic additive manufacturing (AM) processes for the production of high-performance industrial pieces have been hampered by the limited availability of reliably processable or printable alloys. To date, most of the alloys used in AM are commercial grades that have been previously optimized for different manufacturing techniques. This study aims to design new alloys specifically tailored for AM processes, to minimize defects in the final products and to optimize their properties. A computational approach is proposed to design novel and optimized austenitic alloy compositions. This method integrates a suite of predictive tools, including machine learning, calculation of phase diagrams (CALPHAD) and physical models, all piloted by a multi-objective genetic algorithm. Within this framework, several material-dependent criteria are examined and their impact on properties and on the occurrence of defects is identified. To validate our approach, experimental tests are performed on a selected alloy composition: powder is produced by gas atomization and samples are fabricated by laser powder bed fusion. The microstructure and mechanical properties of the alloys are evaluated and its printability is compared with a commercial 316L stainless steel taken as a reference. The optimized alloy performs similarly to 316L in terms of coefficient of thermal expansion, hardness and elongation, but has a 17% lower yield strength and ultimate tensile strength (UTS), indicating that further optimization is required.

A Novel Pareto-Optimal Algorithm for Flow Shop Scheduling Problem

Minimizing job waiting time for completing related operations is a critical objective in industries such as chemical and food production, where efficient planning and production scheduling are paramount. Addressing the complex nature of flow shop scheduling problems, which pose significant challenges in the manufacturing process due to the vast solution space, this research employs a novel multiobjective genetic algorithm called distance from ideal point in genetic algorithm (DIPGA) to identify Pareto-optimal solutions. The effectiveness of the proposed algorithm is benchmarked against other powerful methods, namely, NSGA, MOGA, NSGA-II, WBGA, PAES, GWO, PSO, and ACO, using analysis of variance (ANOVA). The results demonstrate that the new approach significantly improves decision-making by evaluating a broader range of solutions, offering faster convergence and higher efficiency for large-scale scheduling problems with numerous jobs. This innovative method provides a comprehensive listing of Pareto-optimal solutions for minimizing makespan and total waiting time, showcasing its superiority in addressing highly complex problems.

Multi-objective parameter design and economic analysis of VSG-controlled hybrid energy systems in islanded grids

Photovoltaic (PV) systems offer cost-effective power solutions for outlying islands but often compromise system stability due to reduced inertia. This study introduces a Virtual Synchronous Generator (VSG) control strategy, integrated with Energy Storage Systems (ESS) and PV, to enhance system inertia. By optimizing coordination between these energy sources, the proposed method mitigates oscillations and improves grid stability. However, PV-VSG systems are generally not favored by energy providers due to the requirement for pre-curtailment of power output. To address this, the paper proposes a parameter design method for VSG control of ESS and PV, utilizing multi-objective genetic algorithm (MOGA) optimization to simultaneously increase the frequency nadir and minimize the settling time after disturbances. Additionally, an adaptive curtailment decision and parameter design method based on artificial neural networks is introduced to enhance the feasibility of PV-VSG systems by reducing PV pre-curtailment and prioritizing PV power release and ESS charging during frequency oscillations. Real data from the Penghu Archipelago in Taiwan are used to build a dynamic model in DIgSILENT, enabling interaction with MOGA. The Value at Risk (VaR) method with dual stochastic variables is employed to assess the allowable PV installed capacity. The results show that when VaR is set at 1%, the proposed PV-VSG method can increase PV penetration by 57.5% compared to scenarios without VSG. Furthermore, compared to traditional PV-VSG methods, the proposed approach achieves a 16.8% increase in PV penetration and reduces annual PV curtailment by 25 MWh. This study also evaluates the economic impact of planners choosing different risk levels, offering valuable insights for grid development in remote or island regions.

A heuristic method for multi-objective hybrid flow shop scheduling problem with parent-child relationships and space constraints

ABSTRACT In modern ship manufacturing, a ship is divided into hundreds of blocks in the design stage, and the productivity of the block manufacturing process is vital to ship delivery. This paper investigates the production scheduling problem of ship blocks with the parent-child relationship; this relationship is characterised by the fact that sibling jobs at the same BOM layer must be processed on adjacent machines at their assembly stages. We formulate this problem as a multi-objective hybrid flow shop scheduling problem: the first objective is to minimise the total deviation from the target time of the jobs and the second one is to minimise the total processing time. To solve this problem, we propose a heuristic method based on the multi-objective genetic algorithm, in which an adjacent machine priority method is first proposed to generate feasible solutions that satisfy the space constraints. Then, we improve the individual quality in crossover and mutation of the algorithm via tabu search. We verify the performance of our algorithm by test instances generated from real manufacturing data of a shipbuilding company. Results show that the proposed method outperforms the existing algorithms when identifying the Pareto optimal solutions, especially for large-sized problems.

Multi-Objective Optimization of the Pre-Swirl System in a Twin-Web Turbine Disc Cavity

Enhancing thermal efficiency and minimizing weight are prevailing issues in aero engines. Owing to its hollow structure, the twin-web turbine disc exhibits remarkable weight reduction properties, while its enhanced cooling constitutes a novel challenge. In this study, a twin-web turbine disc cavity system is numerically investigated. To enhance the cooling effect and minimize pressure loss, a multi-objective genetic algorithm and Kriging surrogate model are employed to optimize the radial height of the pre-swirl nozzle and receiver hole in the disc cavity system. The results indicate that the overall performance of Opt-3, derived from the Technique for Order Preference by Similarity to the Ideal Solution method within the Pareto frontier, is superior. This configuration achieves a uniform low distribution of rotor temperatures while maintaining moderate pressure losses. Notably, the maximum temperature is reduced by 21.1 K compared to the basic model, with pressure losses remaining largely unchanged. Additionally, an increase in the flow ratio leads to a reduction in both the maximum temperature and average temperature of the back web while simultaneously increasing the temperature of the front web and augmenting pressure losses. However, it is important to note that the degree of variation in these parameters diminishes with increasing flow ratios.

Optimal design of a wavy Micro-Channel based on Multi-Objective genetic algorithm

With the increase of heat generation in electronic equipment, microchannel heat sinks (MCHS) have been widely adopted as a solution for heat dissipation under high heat flux conditions. This study aims to optimize the thermal resistance (θ) and the pressure drop (Δp) of a wavy MCHS through a multi-objective optimization method. The channel shape of the MCHS is controlled by adjusting the coordinates of seven nodes (Si) evenly distributed along the flow direction. The genetic algorithm and CFD software COMSOL Multiphysics are combined to determine the Pareto front with the optimal θ and Δp. The Pareto front suggests that gradually increasing the channel width in the inlet section can enhance the comprehensive performance, by appropriately introducing perturbations in the middle and rear sections. The Spearman’s correlation is adopted to reveal the relevance between Si and objective functions, which also indicates that downstream nodes have a higher correlation. Furthermore, a multi-objective decision-making algorithm is employed to identify OptimalTOPSIS as the best compromise solution. Compared to the straight-through channel, OptimalTOPSIS not only reduces Δp by 40.8% with almost unchanged θ, but also improves the uniformity of the substrate temperature by 4.2%.

Structural optimization and heat-fluid-solid simulation of a graded corundum-calcium hexaluminate purging plug by a multi-objective genetic algorithm approach

The purging plug is essential for enhancing production efficiency in molten steel refining, yet it faces challenges related to structural integrity due to its lifespan often not aligning with the ladle's repair cycle. This study introduces a novel corundum-calcium hexaluminate purging plug with gradual holes designed to alleviate internal stress concentration. Utilizing a multi-objective optimization model and fluid-solid coupling heat transfer method, the impact of structural parameters on thermal and mechanical properties was systematically investigated. The numerical simulation results indicate that increasing the number of gradient layers positively impacts temperature distribution. Notably, the D-210 (1.0 mm diameter) aperture reduces the stress gradient Δσmax by 648.06 MPa compared to D-212 (1.2 mm diameter) at the Y = 0.313 m section. Additionally, variation in inclination angle impacts tensile stress σt and shear stress τ, an inclination angle of 6° (α6) reduces maximum tensile stress by 211.41 MPa compared to an angle of 0° (α0).

Design and multi-objective optimization of hybrid process of membrane separation and electrochemical hydrogen pump for hydrogen production from biogas

A new hybrid process for hydrogen (H2) production that including membrane separation, electrochemical hydrogen pump (EHP) and steam methane reforming (SMR) was proposed. Firstly, the combination of two different membranes (PEO-CA) is applied to upgrade the biogas and capture CO2. Secondly, to avoid the toxic effect of CO on the EHP catalyst and, more importantly, to improve the H2 purity, the reforming reaction module is supplemented with water gas shift after the SMR. Finally, EHP is used to achieve high purity hydrogen. Simultaneously, the heat and power integration are considered to improve system efficiency. Sensitivity analysis, maximal information coefficient method and response surface methodology are applied to establish the correlation between objectives and parameters. Then, the second generation multi-objective non-dominated genetic algorithm (NSGA-II) is utilized to achieve minimum levelized cost of hydrogen (LCoH) and carbon emission per unit of hydrogen (ECO2). The optimized LCoH is 2.72 $/kgH2, and the ECO2 is 3.24 kgCO2e. Multi-stage membrane carbon capture process enhances CH4 conversion in SMR. The application of EHP reduces energy consumption and enhances hydrogen yield compared to PSA. The innovative use of membrane is hybridized with EHP to achieve the system intensification. The new hybrid process has a significant advantage in terms of ECO2 (37.45% reduction), although the price is higher compared to conventional biogas-to-hydrogen processes. Thus the new process is a promising low-carbon hydrogen production process from biogas.

Numerical study on the forward and inverse problems of the mobile pump truck frame

Aiming at the requirements of strong mobility and high flexibility of rescue and relief mobile pump trucks, this paper designs a new type of mobile pump truck frame based on existing mobile vehicle frame models. The materials used for the frame are 40Cr and Q235, and the finite element method is utilized to carry out static mechanical analysis and dynamic characteristic analysis. Simultaneously utilizing topology optimization and multi-objective genetic algorithm to optimize the design of the frame structure. The results show that the optimized pump truck frame can meet the strength design requirements of four typical working conditions: full load bending, full load torsion, emergency turning and emergency braking, while avoiding resonance phenomena caused by road surface and diesel engine vibration. Compared with the original frame model, the weight of the optimized frame is reduced by 87.88 kg, with a weight reduction rate of 10.89%, realizing the lightweight design requirements.

Design optimization of finned multi-tube PCM heat exchanger for enhancing EV energy performance

This study presents an optimized design of a finned multi-tube phase change material-based thermal energy storage system for electric vehicle thermal management, addressing the challenge of low battery efficiency in cold climates. While finned multi-tube designs are recognized for their thermal advantages, their application in electric vehicles has been underexplored. Previous research often overlooks the full operational cycle of electric vehicles, including charging (melting), discharging (solidification), and driving conditions. This work bridges the gap by optimizing the thermal energy storage design using a multi-objective genetic algorithm to enhance both thermal efficiency and energy density. The optimized design reduces melting and solidification times by 68.6% and 60.3%, respectively, and increases energy density by 4.9% compared to the initial design. The study further integrates the optimized thermal energy storage system into an electric vehicle thermal management strategy, analyzing its performance under real-life driving cycles in various ambient temperatures. This integration reduces total energy consumption by 20.6% compared to a baseline system. These findings offer a novel approach to improving the driving range of electric vehicles in cold climates by minimizing power demand on electric batteries for cabin heating.

A multi-objective genetic algorithm based on two-stage reinforcement learning for green flexible shop scheduling problem considering machine speed

The consumption of energy and resources in the manufacturing industry has garnered significant attention due to the increasingly severe environmental issues. Green shop scheduling research is focused on optimizing economic and environmental indicators within the current manufacturing model. This paper specifically addresses the flexibility of job-shop scheduling problem considering machine speed (CMS-FJSP), as different machine speeds during the production process can impact energy and resource consumption. The objectives of this study include minimizing the makespan, total energy consumption, and tool wear. To tackle this problem, a multi-objective genetic algorithm that incorporates a two-stage reinforcement learning approach is proposed. In light of the characteristics of the problem, a three-layer encoding approach is suggested, which encompasses machine allocation, operation sequencing, and machine speed selection. Additionally, a decoding method that integrates energy-saving strategies is proposed to enhance the optimization process. To improve the quality of the population, three distinct initialization methods have been developed. Furthermore, a parameter adjustment strategy informed by two-stage reinforcement learning is introduced. This strategy incorporates a state set and action set tailored to the unique characteristics of two-stage reinforcement learning, alongside corresponding reward mechanisms. In 30 test cases, the proposed algorithm demonstrates superior uniformity and convergence compared to five classical algorithms. In a practical case within a hydraulic component production workshop conducted at a hydraulic component company, the proposed algorithm generates 26 scheduling schemes with different focuses, achieving a 14.39% reduction in makespan, a 2.13% decrease in energy consumption, and a 10.65% reduction in tool wear.

Optimal quantum circuit generation for pixel segmentation in multiband images

A novel approach is proposed for multiband image processing via quantum models in real situations. Quantum circuits are automatically generated ad-hoc for each use case via multiobjective genetic algorithms. Using this universal method, image processing tasks such as segmentation can be carried out by considering the properties that constitute each pixel. The generated circuits present a low level of correlation between qubits, and thus can be considered quantum-inspired machine learning models. The effectiveness of this methodology has been validated by applying it to different segmentation use cases. Comparisons are made between optimized classical kernel methods and the generated quantum-inspired models to understand their behaviors. The results show that quantum models for multiband image processing achieve accuracies similar to those of classical methods.