Multi-objective Optimization Solution Research Articles

System simulation and multi-objective optimization methodology for sustainable municipal solid waste classification management: A case study in China

Cities worldwide are actively implementing waste classification policies to develop efficient waste management systems that address the growing challenges of waste disposal and promote sustainable development. However, selecting tailored waste classification solutions is challenging due to the intricate interrelationships within the system and the complex trade-offs between different management objectives. This study presents a comprehensive methodology that simulates the entire waste classification management system by integrating the stages of waste generation, source classification, and final disposal, all linked by waste quantities and properties. A multi-objective optimization model is employed to identify optimal technical pathways that balance economic, environmental, and energy objectives. This methodology was applied in Zhangjiagang, a medium-sized city in China, revealing that economically optimal solutions focusing on landfill and composting as disposal pathways could exacerbate environmental impacts, with indicators deteriorating by 23.1 %–419.1 % except for freshwater eutrophication potential. In contrast, a multi-objective optimization solution prioritizing incineration and anaerobic digestion achieves environmental and energy benefits valued at 18.7 USD/t, with a modest increase in economic costs of 5.7 USD/t compared to the base year. The unconstrained optimal scenarios achieve net negative environmental costs and recover 2.0 × 106 MJ of energy. The study recommends moderate and dynamically adjusted kitchen waste separation rates depending on the stage of implementation. Additionally, the findings reveal that the “lock-in” effects of mismatched disposal facilities can significantly escalate costs. In conclusion, this study underscores the importance of systematic design and multi-objective optimization, offering a comprehensive methodology for cities aiming to enhance their waste classification strategies.

Research on the Optimal Trajectory Planning Method for the Dual-Attitude Adjustment Mechanism Based on an Improved Multi-Objective Salp Swarm Algorithm

In this study, an optimization method for the motion trajectory of attitude actuators was investigated in order to improve assembly efficiency in the automatic docking process of large components. The self-developed dual-attitude adjustment mechanism (2-PPPR) is used as the research object, and the structure is symmetrical. Based on the modified Denavit–Hartenberg (MDH) parameter description method, a kinematic model of the attitude mechanism is established, and its end trajectory is parametrically expressed using a five-order B-spline curve. Based on the constraints of the dynamics and kinematics of the dual-posture mechanism, the total posturing time, the degree of urgency of each joint, and the degree of difficulty of the mechanism’s posturing are selected as the optimization objectives. The Lévy flight and Cauchy variation algorithms are introduced into the salp swarm algorithm (SSA) to solve the parameters of the multi-objective trajectory optimization model. By combining the evaluation method of the multi-objective average optimal solution, the optimal trajectory of the dual-tuning mechanism and the motion trajectory of each joint are obtained. The simulation and experiment results show that the trajectory planning method proposed in this paper is effective and feasible and can ensure that the large-part dual-posture mechanism can complete the automatic docking task smoothly and efficiently.

Theoretical analysis and crashworthiness optimization of multi-cell Al-CFRP hybrid tube

In this paper, a hybrid thin-walled tube with multiple cells and multiple materials is proposed to achieve lightweight, high energy absorption efficiency, and low cost of structures. Energy absorption characteristics and crushing deformation of the hybrid structures are investigated by experimental testing and numerical analysis. Meanwhile, theoretical prediction models of the mean crushing force for hybrid structures are developed and its accuracy was verified. It is found from the sensitivity analysis that the parameters of the multi-cell structure have significant effects on energy absorption. Furthermore, the multi-objective optimization including surrogate models is performed to obtain optimal crashworthiness of the multi-cell hybrid tube. The same optimal results were obtained using the full factor response mapping optimization algorithm and the non-dominated sorting genetic algorithm with elite strategy algorithm for multi-objective optimization solution. Compared with the original multi-cell hybrid tube and multi-cell pure aluminum (Al) tube, the optimized structure showed an increase of 0.25% and 25.31% in specific energy absorption, an increase of 16% and 77.34% in average crushing force, and an increase of 3.89% and 23.08% in energy absorption efficiency, respectively.

Performance evaluation of a partially-filled porous foam cylindrical tubular receiver realizing Ni foam material reduction

The optimization of metal foam structure parameters is crucial for enhancing the heat transfer and pressure drop performance of cylindrical tube heat exchangers. In this study, thermal output performance of 16 different nickel foam-filled solar receivers with varying thicknesses and positions is numerically simulate. An optical-thermal conversion model is established using the Monte Carlo ray tracing (MCRT) method and user-defined functions. The influences of foam filling thickness and position on heat absorber, wall hot spot distribution, heat loss type, thermal efficiency, pressure drop, thermohydraulic performance, exergy loss, and outlet temperature were analyzed. Furthermore, a multi-objective optimization algorithm called non-dominated sorting genetic algorithm II (NSGA II) is used to generate the best trade-off solutions between improvement of thermal efficiency and pressure drop. The results show that enhancing the thickness of the filled foam within the CTR tube enhances thermal efficiency and elevates pressure drop. However, optimizing the length and filling position of the foam can effectively mitigate excessive pressure drop caused by longer foam, without compromising thermal efficiency. This approach results in a reduced pressure drop while maintaining optimal performance. The optimal solution of multi-objective optimization is that the 123 mm thickness foam is filled at 0 mm from the inlet of the tube, and the pressure drop and thermal efficiency of the solar receiver are 830.2Pa and 83.1 %, respectively, when the mass flow rate is 0.01 kg/s.

Optimization of magnetic coupling mechanism of dynamic wireless power transfer based on NSGA-II algorithm

Optimization of magnetic coupling mechanism is an important way to improve the performance of a dynamic wireless power transfer system. Inspired by the common radial magnetic core for circular coils, a new radial magnetic core for rectangular coils is adopt. Through simulation and experimental results comparison, which has higher coupling coefficient with the same core area. Combined with the magnetic circuit analysis, the magnetic flux leakage and conduction regions are divided into magnetic fluxes with different shapes, which magnetic resistances are calculated respectively. Based on the simulation results, parameter distributions of fluxes under different conditions are obtained. Therefore, the expressions of the coupling coefficient k of the adopt magnetic cores and coils and the design parameters of coils and cores are obtained. Taking the maximum k and the minimum rate of change of coupling coefficient with 100 mm displacement as the optimization objectives, a multi-objective optimization solution is carried out by using NSGA-II algorithm. The coil optimization scheme is obtained and verified by experiments. k and Δk are 0.442 and 6.8% respectively, and the errors are less than 5%. In the optimization process, there is no simulation model constructed. The optimization modeling combined of magnetic field segmentation method and parameter fitting has lower complexity and calculation time of optimization.

Sustainable Road Infrastructure Decision-Making: Custom NSGA-II with Repair Operators for Multi-Objective Optimization

The integration of sustainability principles into the structural design and decision-making processes for transportation infrastructure, particularly concerning reinforced concrete precast modular frames (RCPMF), is recognized as crucial for ensuring outcomes that are environmentally responsible, economically feasible, and socially beneficial. In this study, this challenge is addressed, with the significance of sustainable development in modern engineering practices being underscored. A novel approach, which is a combination of multi-objective optimization (MOO) with multi-criteria decision-making (MCDM) techniques, is proposed, tailored specifically for the design and selection of RCPMF. The effectiveness of three repair operators—statistical-based, random, and proximity-based—in optimizing economic, environmental, and social objectives is evaluated. Precise evaluation of objective functions is facilitated by a customized Non-dominated Sorting Genetic Algorithm II (NSGA-II) algorithm, complemented by a detailed life cycle analysis (LCA). The utilization of simple additive weighting (SAW) and fair un choix adéquat (FUCA) methods for the scoring and ranking of the MOO solutions has revealed that notable excellence in meeting the RCPMF design requirements is exhibited by the statistical-based repair operator, which offers solutions with lower impacts across all dimensions and demonstrates minimal variability. MCDM techniques produced similar rankings, with slight score variations and a significant correlation of 0.9816, showcasing their consistent evaluation capacity despite distinct operational methodologies.

Multi-Objective Optimization of Low-Alloy Hot-Rolled Strip Cooling Process Based on Gray Correlation Analysis

The residual stress in low-alloy hot-rolled strips seriously affects the use and processing of products. Reducing residual stress is important for improving the product quality of hot-rolled strips. In this paper, the changes in grain size and residual stress of hot-rolled strips under different cooling processes were investigated via thermal simulation experiments and electron backscatter diffraction. It was found that the optimum cooling process solution for single-objective optimization of grain size was a final rolling temperature of 875 °C, a laminar cooling speed of 50 °C/s, and a coiling temperature of 550 °C. When single-objective optimization of residual stress was carried out, the optimal cooling process scheme was 900 °C for final rolling temperature, 20 °C/s for laminar cooling speed, and 625 °C for coiling temperature. The significance of the effect of cooling processes on grain size and residual stress was analyzed based on the extreme deviation of the effect of each cooling process on grain size and residual stress in orthogonal experiments. The results show that the coiling temperature was the most influential factor on grain size and residual stress among the cooling process parameters. The difference was that grain size increased with increasing coiling temperature, and residual stress decreased with increasing coiling temperature. Using both grain size and residual stress as evaluation indicators, a multi-objective optimization of the cooling process for hot-rolled strips was carried out via the gray correlation analysis method. The optimized solution was 875 °C final rolling temperature, 30 °C/s laminar cooling speed, and 625 °C coiling temperature. At this time, the grain size was 4.8 μm, and the KAM (Kernel Average Misorientation) was 0.40°. The grain size under the actual production process scheme was 4.4 μm with a KAM of 0.78°. Compared to the actual production process solution, the multi-objective optimization solution showed little change in grain size, with only a 9% increase and a 49% reduction in KAM. The optimization scheme in this paper could significantly reduce the level of residual stresses while ensuring the fine grain size of hot-rolled strips, thus improving the overall quality of hot-rolled strips.

Numerical study and multi-optimization of heat transfer performance in counter flow minichannel heat sink with slots on ribs using NSGA-Ⅱ

High temperature and nonuniformity temperature cause thermal failure and thermal deformation for high-heat flux electronic devices. To address this issue, a counter flow minichannel heat sink with slots on ribs is designed. A 3D conjugated heat transfer model based on computational fluid dynamics (CFD) was developed to investigate effect of slots number (i) and growth rate of slot width (j) on average Nusselt number (Nu), local temperature distribution and temperature standard deviation (σ). The results show that design parameters i and j significantly influence heat transfer capability and temperature uniformity. However, there is no clear pattern between design parameters (i and j) and heat transfer performance (Nu and σ) to guide design. Furthermore, a non-dominated sequencing genetic algorithm (NSGA-Ⅱ) multi-objective optimization coupled Artificial Neural Network (ANN) is conducted with the consideration of maximum Nusselt number Nu and minimum temperature standard deviation (σ) as objectives. Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method is employed to determine the best solution from the Pareto front, i.e., multi-objective optimization solutions set. The optimized solution obtains 2.1 times increase in average Nusselt number and 0.36 times decrease in temperature standard deviation compared with conventional counter flow minichannel heat sink. This work serves as a guideline for designing minichannel heat sinks for thermal management of electronic components.

Optimizing daylighting in lecture halls within hot-arid climates through modification of glazing systems with light-shelves: A parametric design approach

This study focused on enhancing the balance between sufficient daylighting and heat mitigation in lecture halls, set within the challenging hot-arid climate of Saudi Arabia, through the optimization of window designs and the incorporation of light-shelves. To achieve this, a combination of in-detail Parametric Sensitivity Analyses (PSA) was performed in the first stage to find the best configuration of windows, while a multi-objective optimization (MOO) strategy was applied in the second stage considering multiple objectives in each stage. Before running both analyses, the simulation model was validated by field measurement. The optimization results show that the optimal window configurations obtained through PSA and MOO offer significant potential for improving daylighting performance, energy efficiency and comfort. In terms of the overall best solutions from PSA and MOO, the PSA demonstrated substantial improvements, with Useful Daylight Illuminance (UDI) rising by 46.31% to 55.36%, glare decreasing by 52.17% to 82.61% and Energy Use Intensity (EUI) improving by 0.89% to 2.63%. Additionally, MOO solutions yielded even more significant enhancements, increasing UDI by 43.58% to 58.37%, reducing glare by 95% to 100% and enhancing EUI by 1.81% to 3.88%. This has resulted in a more evenly distributed and efficient daylighting throughout the space.

Study on the Basic Form of Reservoir Operation Rule Curves for Water Supply and Power Generation

Reservoir operation rule curves are crucial for managing water supply and power generation in reservoirs. As the number of objectives and management requirements increase, there is a growing demand for optimized operation rule curves. The objective of this study is to explore the most effective forms of reservoir operation rule curves, focusing on the case of the Nierji Reservoir and considering the dual objectives of water supply and power generation. The parameter–simulation–optimization framework, specifically employing the NSGA-II algorithm, was used to analyze and compare two basic forms of operation rule curves: the shared type and independent type. The impact of these curves on water supply potential and multi-objective optimization results with various water demand scenarios was assessed. The analysis revealed that the choice of operation rule curve form can influence the maximum water supply potential of the reservoir to some extent. The independent type operation rule curve was significantly more effective in enhancing the water supply potential for industrial and domestic users, resulting in a notable increase of 3.5 × 108 m3. Additionally, it also proved beneficial for environmental users, with an increase of 1 × 108 m3. Conversely, the shared type operation rule curve demonstrated similar functionality to the independent type curve with fewer decision variables, particularly when the water demand was relatively low. In scenarios with high water demand, the independent type curve outperformed the shared type curve by generating 6549 superior, non-dominated solutions for multi-objective optimization, specifically focused on maximizing reservoir operation benefits. In conclusion, selecting the appropriate form of reservoir operation rule curve is crucial to balance different reservoir functional objectives and achieve optimal results. Further research could focus on quantifying the specific benefits and trade-offs associated with each type of curve in order to provide more robust evidence for the advantages of a complex reservoir system.

An interactive possibilistic programming approach for sustainable supplier selection and inventory management problem in the physical internet

PurposeThis paper aims to introduce a novel structure for the physical internet (PI)–enabled sustainable supplier selection and inventory management problem under uncertain environments.Design/methodology/approachTo address hybrid uncertainty both in the objective function and constraints, a novel interactive hybrid multi-objective optimization solution approach combining Me-based fuzzy possibilistic programming and interval programming approaches is tailored.FindingsVarious numerical experiments are introduced to validate the feasibility of the established model and the proposed solution method.Originality/valueDue to its interconnectedness, the PI has the opportunity to support firms in addressing sustainability challenges and reducing initial impact. The sustainable supplier selection and inventory management have become critical operational challenges in PI-enabled supply chain problems. This is the first attempt on this issue, which uses the presented novel interactive possibilistic programming method.

Multi-objective optimization of regional integrated energy system matrix modeling considering exergy analysis and user satisfaction

Integrated energy system achieves the collaborative optimization of different energy forms such as electricity/gas/heat/cold, and it is an important strategic development direction in the international energy field in the future. However, with the development of smart grid technology and the increase of renewable energy penetration, it needs more flexible load to adapt to the changing energy supply–demand relationship. Considering the impact of user-side load fluctuations on the system, this paper establishes a two-tier closed-loop system model formed by users and the market. The upper-layer is based on the user’s perspective. A comprehensive user satisfaction model is established by the radar chart area method and consumer preference theory. The lower-layer is based on the market perspective. It proposes an improved layering matrix Energy Hub model and conducts the regional integrated energy system exergy efficiency evaluation study. Then, taking economy, carbon emission and exergy efficiency as objective functions, a multi-objective optimization model is constructed. Non-dominated Sorting Genetic Algorithms-II is applied to solve the model, and the output parameters is a Pareto front surface. A combination weighting TOPSIS method is used to select a compromise solution of multi-objective optimization from the optimal solution set. Compared with the primary energy utilization efficiency as the objective function, the exergy efficiency reduces the system cost by 8.64%. Finally, the multi-objective optimization solution based on different user satisfaction is obtained. Through the analysis of the examples, compared with the original load, the introduction of the upper-layer user satisfaction model reduces the system cost by 2.8%. The reasonable setting of user satisfaction and load adjustment amount can improve the economy of system, which verifies the effectiveness of the two-tier closed-loop system based on user satisfaction model.

Finding ϵ-Locally Optimal Solutions for Multi-Objective Multimodal Optimization

Finding ϵ-Locally Optimal Solutions for Multi-Objective Multimodal Optimization

An optimal power flow solution for a power system integrated with renewable generation

<abstract> <p>Integrating Green Renewable Energy Sources (GRES) as substitutes for fossil fuel-based energy sources is essential for reducing harmful emissions. The GRES are intermittent and their integration into the conventional IEEE 30 bus configuration increases the complexity and nonlinearity of the system. The Grey Wolf optimizer (GWO) has excellent exploration capability but needs exploitation capability to enhance its convergence speed. Adding particle swarm optimization (PSO) with excellent convergence capability to GWO leads to the development of a novel algorithm, namely a Grey Wolf particle swarm optimization (GWPSO) algorithm with excellent exploration and exploitation capabilities. This study utilizes the advantages of the GWPSO algorithm to solve the optimal power flow (OPF) problem for adaptive IEEE 30 bus systems, including thermal, solar photovoltaic (SP), wind turbine (WT), and small hydropower (SHP) sources. Weibull, Lognormal, and Gumbel probability density functions (PDFs) are employed to forecast the output power of WT, SP, and SHP power sources after evaluating 8000 Monte Carlo possibilities, respectively. The multi-objective green economic optimal solution consisted of 11 control variables to reduce the cost, power losses, and harmful emissions. The proposed method to address the OPF problem is validated using an adaptive IEEE bus system. The proposed GWPSO algorithm is evaluated by comparing it with PSO and GWO optimization algorithms in terms of achieving an optimal green economic solution for the adaptive IEEE 30 bus system. This evaluation is conducted within the confines of the same test system using identical system constraints and control variables. The integration of a small SHP with WT and SP sources, along with the proposed GWPSO algorithm, led to a yearly cost reduction ranging from <bold>$\$$19,368</bold> to <bold>$\$$30,081</bold>. Simulation findings endorsed that the proposed GWPSO algorithm executes fruitfully compared to alternative algorithms regarding a consistent convergence curve and robustness, proving its potential as a viable choice for achieving cost-effective solutions in power systems incorporating GRES.</p> </abstract>

An Active Multi-Beam Antenna Design Method and Its Application for the Future 6G Satellite Network

The active multi-beam antenna can be widely used in the 6G integrated satellite network project, the national satellite Internet project, and other major satellite projects. It determines the key performance indicators of the satellite such as the service coverage area and the communication capacity of all the beams in the forward link and the return link. The active multi-beam antenna is the core technology of satellite payload and has been developed rapidly in recent years. Based on the antenna performance requirements such as the number of giant-scale beams, super-large coverage area, high gain, and high carrier-to-interference ratio (C/I), this paper proposes a new active multi-beam antenna design method, which integrates the multi-objective coordination and multi-feed amplitude and phase weighted optimization algorithms. A balanced optimal solution that meets the performance requirements can be obtained by constraining each other with different objective functions. In the optimization process, the surrogate model of convolutional autoencoder based on artificial intelligence technology is proposed for multi-objective optimization solution, which efficiently completes the search of optimal beam excitation coefficients. This paper takes the demand for very-high-throughput communication satellites serving the Asia-Pacific region as an example and applies the design method to the multi-beam antenna design of the satellite. The simulation verification of the antenna is completed, achieving good performance of 976 beams. All results meet the performance requirements, supporting the implementation of 1 Tbps communication capacity for the entire satellite, and verifying the correctness of the design method.

Optimization of micro-perforated panel absorber backed with parallel-arranged cavities of different depths

Micro-perforated panel absorber (MPA) is one of the most effective materials in noise control. For the MPA backed with parallel-arranged cavities of different depths (PCD-MPA), the panel thickness, perforation diameter, perforation rate, and cavity depth are essential parameters for absorption performance. In this study, a Genetic Algorithm (GA)-based optimization design method for PCD-MPA is proposed to achieve specific performance objectives. Firstly, a prediction model for absorption performance of PCD-MPA is proposed. Secondly, a GA optimization method suitable for PCD-MPA is established, taking into account interactive parameters and generating multi-objective and high-quality optimal solutions through iterative adjustments. Finally, an acoustic standard sample was fabricated for experimental validation and compared with analytical results. The findings demonstrate that the proposed optimization method can achieve nearly perfect absorption with a well-selected combination of parameters that meet specific absorption requirements.

Mathematical model of routing problem for hazardous biomedical waste: A multi-objective particle swarm optimization solution approach

Purpose: This model aims at solving a Green Heterogeneous and Stochastic Capacitated Vehicle Routing Problem that takes into account the risks and environmental hazards.
 Research Methodology: Regarding an NP-hard and complex problem, and after confirming the accuracy of the problem-solving in smaller dimensions by GAMS software, the problem is solved by the metaheuristic algorithm of multi-objective particle swarm optimization (MOPSO) and its coding in MATLAB software.
 Results: The results urge that using random sampling and probability distribution, non-deterministic parameters turned into deterministic ones, and high-quality solutions were obtained.
 Limitation: The proposed method is a routing problem and has been applied for the Green Heterogeneous and Stochastic Capacitated Vehicle Routing Problem. Future researchers may work on real data sets and hazardous biomedical waste data.
 Contribution: Based on the results presented, the model derived in this paper can support decisions such as routing, prioritization, time to reach each node, etc. so that the costs of routing, system reliability, environmental issues, and penalties for violation of the priority and maximum time elapsed for vehicles are taken into account.

A multi-objective optimization solution for distributed generation energy management in microgrids with hybrid energy sources and battery storage system

This manuscript proposes an intelligent Golden Jackal Optimization (GJO) for distributed-generation energy management (EM) issues in battery storage systems (BSSs) and hybrid energy sources (HESs). The objectives of the proposed method are to minimize the operating cost, and solve the microgrid (MG) energy management problem. Numerous constraints, including power balance, generation capacity, consumer loads, and the charging-discharging and dynamic performance of energy storage units, have an impact on microgrid energy management system. The proposed approach is run in the MATLAB platform and is compared to existing approaches. Also, the simulation result concludes that the proposed approach has lower costs than the existing methods. The proposed approach provides 96 % high efficiency, and 2×106 $ lower cost compared with other existing Particle Swarm Algorithm (PSO), Artificial Bee Colony (ABC), and Tabu Search (TS) methods.

Optimization of the Tribological Performance and Service Life of Calcium Sulfonate Complex—Polyurea Grease Based on Unreplicated Saturated Factorial Design

In order to further extend the service life of calcium sulfonate complex–polyurea grease (CSCPG) while ensuring its tribological performance, this article starts with the production of raw materials and the preparation process of the grease and explores the factors that significantly affect the tribological performance and service life of CSCPG based on unreplicated saturated factorial design (USFD). The Kriging prediction model is used along with the optimization objectives of friction coefficient and service life, and nondominated sorting genetic algorithm II (NSGA-II) was used for a multi-objective optimization solution. The tribological and service life tests were conducted before and after optimization. The results show that the viscosity of the base oil and the content of the nano-solid friction reducers have a significant impact on the tribological properties of CSCPG. The content of polyurea thickeners and antioxidants, as well as the thickening reaction temperature, have a significant impact on the service life of CSCPG. When the friction coefficient and service life are optimized as objectives and are compared to the initial group, the friction coefficient of CSCPG could be reduced by 5.3%, and the service life could be extended by 3.8%. The Kriging prediction model based on USFD has high accuracy and can be used to guide the preparation and performance optimization of CSCPG.

Load-independent multi-objective sensor placement method for localization and reconstruction of external excitations under interval uncertainties

The direct measurement of external excitations under operation conditions remains challenging in many engineering applications. This study proposes a method of optimal sensor placement for the best localization and reconstruction of external excitations when considering multi-source uncertainties. The excitation inversion is achieved in the framework of minimizing the discrepancy between the forward and inversed modal loads. To accurately obtain the modal force in the time-domain excitation reconstruction algorithm, three combined indicators of the interested modal matrix are defined, which integrate measures of effectiveness, stability, orthogonality, and robustness of inverse modal transformation. The optimal sensor placement is determined by multi-objective particle swarm optimization and Pareto optimal solution. In addition, to alleviate the computational burden of loading localization, a surrogate model based on an interval kriging interpolation model and sequential sampling strategies is established for the forward modal forces. And a time-dependent interval error of modal forces inspired by the theory of second-order narrow bounds in system reliability is developed to guide the inversion process. Notably, the developed sensor selection and excitation inversion methods are independent of the type, location, and time evolution of external excitations. Eventually, two numerical examples and an experimental case are discussed to demonstrate the validity and feasibility of the developed approach.