Multi-objective Optimization Model Research Articles

Automated optimization for steel mold of precast components based on multi-objective evolutionary algorithm

Designing an economical and reliable steel mold for precast concrete (PC) components can accelerate the promotion of prefabricated buildings. This paper presents an automated design framework for a steel mold of PC components to generate manufacturing information, including dimensions and location of each plate. The profile and dimensions of PC components are extracted from the shop drawings, and a finite element model of the steel mold is established and initialized. A multi-objective optimization model considering material usage and manufacturing convenience is developed to determine the geometry and quantity of plates in steel molds. The non-dominated sorting genetic algorithm II (NSGA-II) is applied to obtain a Pareto solution set for steel mold design. The production information in DXF format can be automatically generated by parsing Industry Foundation Classes (IFC) model. The cost-effectivity and practicality of the proposed framework were verified via the design case for a real PC stair mold.

LSMOF-AD: Three-Stage Optimization Approach with Adaptive Differential for Large-Scale Container Scheduling

Container technology has gained a widespread application in cloud computing environments due to its low resource overhead and high flexibility. However, as the number of containers grows, it becomes increasingly challenging to achieve the rapid and coordinated optimization of multiple objectives for container scheduling, while maintaining system stability and security. This paper aims to overcome these challenges and provides the optimal allocation for a large number of containers. First, a large-scale multi-objective container scheduling optimization model is constructed, which involves the task completion time, resource cost, and load balancing. Second, a novel optimization algorithm called LSMOF-AD (large-scale multi-objective optimization framework with muti-stage and adaptive differential strategies) is proposed to effectively handle large-scale container scheduling problems. The experimental results show that the proposed algorithm has a better performance in multiple benchmark problems compared to other advanced algorithms and can effectively reduce the task processing delay, while achieving a high resource utilization and load balancing compared to other scheduling strategies.

Optimal dispatching of regional power grid considering vehicle network interaction.

When large-scale electric vehicles are connected to the grid for unordered charging, it will seriously affect the stability and security of the power system. To solve this problem, this paper proposes a regional power network optimization scheduling method considering vehicle network interaction. Initially, based on the user behavior characteristics and charging and discharging characteristics of electric vehicles, a charging and discharging behavior model of electric vehicles was established. Based on the Monte Carlo sampling algorithm, the scheduling upper and lower limits of each scheduling cycle of electric vehicles were described, and the scheduling potential of each scheduling cycle of electric vehicles was obtained. Then, the electricity price is then used as an incentive parameter to guide EV users to charge during periods of low electricity prices and participate in discharge during periods of peak electricity prices. Aiming at the highest economic efficiency, the best consumption effect of new energy and the smoothest demand-side power curve of regional power grid, a three-objective optimal dispatching model was established. In the later stage, uncertainty factors are taken into consideration by introducing the concept of interval numbers, and an interval multi-objective optimization dispatching model is established. The two dispatching models are solved by NSGA-II algorithm and improved NSGA-II algorithm, and the Pareto solution set is obtained. Finally, based on the analytic Hierarchy Process (AHP), the optimal scheduling scheme is determined. The Monte Carlo sampling method is used to simulate the user side charging demand, and the effectiveness of this method is verified. In addition, the results of the interval multi-objective optimization model and the deterministic multi-objective optimization model are compared, and it is proved that the solution results of the interval multi-objective model are more adaptive, practical and robust to the uncertain factors.

Cold chain distribution network design with heterogeneous fleet under carbon reduction policy application effect

ABSTRACT To integrate cold chain distribution network design problem with heterogeneous fleet, a multi-objective optimisation model is proposed to simultaneously optimise distribution routes, the quantity of internal combustion and electric refrigerated trucks (ERTs), and the number and location of distribution centres and charging stations with time window constraints, where the cold chain distribution operating cost and the customer dissatisfaction cost are minimised. A hybrid solution methodology, combining a multi-objective artificial fish swarm algorithm with a label-based optimisation algorithm, is presented to address this model. Extensive computational experiments verify the effectiveness of the proposed method. Moreover, a sensitivity analysis, focusing on the range of ERTs, the purchase subsidy for ERTs, the carbon tax and the carbon emission quota, informs the development of diverse carbon reduction policies. A comparative analysis of the implementation effect of carbon reduction policies can provide optimisation decision support for promoting sustainability in the electrification of cold chain distribution.

Global Optimization and Quantitative Assessment of Large-Scale Renewables-Based Hydrogen System Considering Various Transportation Modes and Multi-Field Hydrogen Loads

In the past, hydrogen was mostly produced from fossil fuels, causing a certain degree of energy and environmental problems. With the development of low-carbon energy systems, renewable energy hydrogen production technology has developed rapidly and become one of the focuses of research in recent years. However, the existing work is still limited by small-scale hydrogen production systems, and there is a lack of comprehensive research on the whole production-storage-transportation-utilization hydrogen system (PSTUH2S), especially on the modeling of different hydrogen transportation modes and various hydrogen loads in different fields. To make up for these deficiencies, the specific physical and mathematical models of the PSTUH2S are firstly described in this paper, with a full account of large-scale water-electrolytic hydrogen production from renewable power curtailment and grid power, various hydrogen storage and transportation modes, and multi-field hydrogen consumption paths. Furthermore, to achieve the maximum economic, energy, and environmental benefits from the PSTUH2S, a multi-objective nonlinear optimization model is also presented herein and then solved by the hybrid method combining the nonlinear processing method, the CPLEX solver and the piecewise time series production simulation method. Lastly, case studies are conducted against the background of a region in northwest China, where hydrogen consumption capacity in various years is accurately assessed and the potential advantages of the PSTUH2S are demonstrated. As the simulation results show, the power curtailment of renewable energy generation can be reduced by 3.61/11.87/14.72 billion kW·h in 2025/2030/2035, respectively, thus contributing to a 4.98%~10.09% increase in the renewable energy consumption rate and millions of tons of carbon emission reduction in these years. In terms of the total equivalent economic benefits, the proposed method is able to bring about a cost saving of USD 190.44 million, USD 634.66 million, and USD 865.87 million for 2025, 2030, and 2035, respectively.

Multi-objective optimization of bias current coefficient based on NSGA- III for active magnetic bearing with redundant electromagnetic actuators

PurposeThe multi-objective optimization configuration strategy is proposed due to the configuration of EMAs in fault-tolerant control of active magnetic bearing with redundant electromagnetic actuators involving high-dimensional, nonlinear, conflicting goals.Design/methodology/approachA multi-objective optimization model for bias current coefficients is established based on the nonlinear model of active magnetic bearings with redundant electromagnetic actuators. Based on the non-dominated sorting genetic algorithm III, a numerical method is used to obtain feasible and non-inferior sets for the bias current coefficient.Findings(1) The conflicting relationship among the three optimization objectives was analyzed for various failure modes of EAMs. (2) For different EMAs' failure modes, the multi-objective optimization configuration strategy can simultaneously achieve the optimal or sub-optimal effective EMF, flux margins, and stability of EMF. Moreover, the characteristics of the optimal Pareto front are consistent with the physical properties of the AMB. (3) Compared with the feasible configuration of C0, the non-inferior configurations can significantly improve the performance of AMB, and the advantages of the multi-objective optimization configuration strategy become more prominent as the asymmetry of the residual supporting structure intensifies.Originality/valuei) Considering the variation of the rotor displacement during the support reconstruction, a decision-making model that can accurately characterize the dynamic performance of AMB is presented. (ii) The interaction law between AMB and rotor under different failure modes of EMAs is analyzed, and the configuration principles for redundant EMAs are proposed. (iii) Based on the dynamic characteristics of AMB during the support reconstruction, effective EMF, energy consumption, and the Pearson correlation coefficient between the desired EMFs and the decoupled control currents are used as objective functions. iv. The NSGA-III is combined with the decision-making model to address the multi-objective optimization configuration problem of C0.

Energy, exergy, exergoeconomic, economic, and environmental analyses and multi-objective optimization of a novel combined cooling and power system with dual-pressure Kalina cycle-absorption refrigeration

A novel combined cooling and power system, the dual-pressure Kalina cycle-absorption refrigeration (DPKC-AR-CCP), is proposed for the cascade utilization of waste heat from high-temperature flue gas and jacket water from internal combustion engines. A pneumatic booster pump unit is employed to increase the inlet pressure of the low-pressure expander, with heat generated during the compression process utilized for preheating the ammonia/water mixture, and exhaust gas from the low-pressure expander is combined with driving gas for absorption refrigeration. By integrating the first and second laws of thermodynamics with the SPECO Methodology, models for energy, exergy, exergoeconomy, economy, and environment of the DPKC-AR-CCP system are developed. A multi-objective optimization model is formulated with maximum exergy efficiency, minimum total exergy cost rate, and maximum annual equivalent carbon dioxide (CO2) emission reduction as objective functions. MATLAB software (combined with REFPROP software) is utilized for simulation and multi-objective optimization. The performance of each component under specific conditions is assessed, and a comparison with the simple absorption refrigeration/Kalina cogeneration system (AR/KC) highlights the advantages of the DPKC-AR-CCP system; an investigation into the effects of different operating parameters on performance is conducted. The results indicate that, under identical operational parameters, the DPKC-AR-CCP system exhibits a substantial increase in net output power (230.2 %), cooling capacity (98.7 %), and exergy efficiency (148.8 %) compared to the simple AR/KC system; payback period is reduced by 64.8 %; and annual equivalent CO2 emissions are decreased by 202.2 %. However, there is an increase in the total exergy cost rate by 90.5 %. The optimal concentration of ammonia/water is determined to be 0.816/0.184, with a heat exchanger outlet temperature of 341.07 K, driving pressure of 0.654 MPa, and low-pressure expander inlet pressure of 5.064 MPa. Correspondingly, the optimal exergy efficiency is 87.2 %, the annual reduction in CO2 emissions is 5.53 × 107 kg, and the total exergy cost rate is 288.48$/h.

A Placement Method of the 5G Edge Nodes Based on the Hotspot Distribution of Mobile Users

Due to the emergence of various new applications, such as short videos and online games, higher requirements of their computing and storage capacity are demanded of mobile networks. The traditional cloud computing paradigm has the shortcomings of large latency and high bandwidth demand of the core network. Therefore, how to mine the hotspot distribution of these applications and reasonably configure 5G edge nodes to reduce latency and core network bandwidth are facing great challenges. To address these issues, we designed a placement method for the 5G edge nodes based on mobile hotspots. In this method, we first cluster all locations from the user trajectories to obtain the cluster areas. Further, we extract the features, such as the number of users and duration time in all cluster areas, and extract the hotspots from all cluster areas based on the features of each cluster. Then, we introduce the base station’s high load utilization rate and the core network’s bandwidth reduction rate as the optimization parameters to construct the mathematical model of multi-objective optimization. Finally, we formalize the model into a 0–1 integer programming problem and design a greedy algorithm to solve this model. We also complete a series of experiments to evaluate our proposed methods using the GeoLife dataset. The experimental results show that the high load utilization rate can be increased up to 7.69%, and the bandwidth reduction rate of the core network can be improved up to 6.34%.

A Placement Method of the 5G Edge Nodes Based on the Hotspot Distribution of Mobile Users

Due to the emergence of various new applications, such as short videos and online games, higher requirements of their computing and storage capacity are demanded of mobile networks. The traditional cloud computing paradigm has the shortcomings of large latency and high bandwidth demand of the core network. Therefore, how to mine the hotspot distribution of these applications and reasonably configure 5G edge nodes to reduce latency and core network bandwidth are facing great challenges. To address these issues, we designed a placement method for the 5G edge nodes based on mobile hotspots. In this method, we first cluster all locations from the user trajectories to obtain the cluster areas. Further, we extract the features, such as the number of users and duration time in all cluster areas, and extract the hotspots from all cluster areas based on the features of each cluster. Then, we introduce the base station’s high load utilization rate and the core network’s bandwidth reduction rate as the optimization parameters to construct the mathematical model of multi-objective optimization. Finally, we formalize the model into a 0–1 integer programming problem and design a greedy algorithm to solve this model. We also complete a series of experiments to evaluate our proposed methods using the GeoLife dataset. The experimental results show that the high load utilization rate can be increased up to 7.69%, and the bandwidth reduction rate of the core network can be improved up to 6.34%.

Multiple unmanned ship coverage and exploration in complex sea areas

This study addresses the complexities of maritime area information collection, particularly in challenging sea environments, by introducing a multi-agent control model for regional information gathering. Focusing on three key areas—regional coverage, collaborative exploration, and agent obstacle avoidance—we aim to establish a multi-unmanned ship coverage detection system. For regional coverage, a multi-objective optimization model considering effective area coverage and time efficiency is proposed, utilizing a heuristic simulated annealing algorithm for optimal allocation and path planning, achieving a 99.67% effective coverage rate in simulations. Collaborative exploration is tackled through a comprehensive optimization model, solved using an improved greedy strategy, resulting in a 100% static target detection and correct detection index. Agent obstacle avoidance is enhanced by a collision avoidance model and a distributed underlying collision avoidance algorithm, ensuring autonomous obstacle avoidance without communication or scheduling. Simulations confirm zero collaborative failures. This research offers practical solutions for multi-agent exploration and coverage in unknown sea areas, balancing workload and time efficiency while considering ship dynamics constraints.

Multiobjective optimization guided by image quality index for limited-angle CT image reconstruction.

Due to the incomplete projection data collected by limited-angle computed tomography (CT), severe artifacts are present in the reconstructed image. Classical regularization methods such as total variation (TV) minimization, ℓ0 minimization, are unable to suppress artifacts at the edges perfectly. Most existing regularization methods are single-objective optimization approaches, stemming from scalarization methods for multiobjective optimization problems (MOP). To further suppress the artifacts and effectively preserve the edge structures of the reconstructed image. This study presents a multiobjective optimization model incorporates both data fidelity term and ℓ0-norm of the image gradient as objective functions. It employs an iterative approach different from traditional scalarization methods, using the maximization of structural similarity (SSIM) values to guide optimization rather than minimizing the objective function.The iterative method involves two steps, firstly, simultaneous algebraic reconstruction technique (SART) optimizes the data fidelity term using SSIM and the Simulated Annealing (SA) algorithm for guidance. The degradation solution is accepted in the form of probability, and guided image filtering (GIF) is introduced to further preserve the image edge when the degradation solution is rejected. Secondly, the result from the first step is integrated into the second objective function as a constraint, we use ℓ0 minimization to optimize ℓ0-norm of the image gradient, and the SSIM, SA algorithm and GIF are introduced to guide optimization process by improving SSIM value like the first step. With visual inspection, the peak signal-to-noise ratio (PSNR), root mean square error (RMSE), and SSIM values indicate that our approach outperforms other traditional methods. The experiments demonstrate the effectiveness of our method and its superiority over other classical methods in artifact suppression and edge detail restoration.

A multi-objective trajectory planning approach for vibration suppression of a series–parallel hybrid flexible welding manipulator

Although the series–parallel hybrid welding manipulator has advantages over the traditional series manipulator, its must run continuously and smoothly according to a specific trajectory at the rated velocity. Considering a flexible manipulator with a complex series–parallel hybrid structure as the research object, this paper proposes a multi-objective approach to indirectly suppress the vibration. In view of the complex flexible mechanism with a multi-kinematic pair arrangement and a multi-motor drive, a system comprehensive elastodynamics model is established, and the effectiveness of the model is verified through the hammer test method. To achieve synchronous and coordinated planning of the end position and attitude, the spatial arc welding trajectory is planned on the basis of the synchronous planning controller and the dual-S velocity profile interpolation function. Then, a multi-objective optimization model considering the travel time, energy consumption and terminal amplitude fluctuations is proposed, the trajectory planning problem is transformed into a parameter optimization problem in the interpolation function, and then the elite non-dominated sorting genetic algorithm (NSGA-II) is employed to determine the optimal trajectory. The optimal trajectory thus obtained effectively suppresses the vibration of the system. Finally, compared with the classic fifth-degree polynomial trajectory planning method, the system vibration suppression effect is significant, which verifies the effectiveness and feasibility of the proposed method and provides a theoretical basis for further realizing real-time control.

Multi-objective topology optimization simulation study based on motor

Abstract OptiStruct software is applied for structural optimization of the motor structure to solve the problem of resonance in a specific operating frequency range and reduce the motor operating noise. Comparing the experimental and simulated modal frequencies, a modified multi-objective topology optimization model is proposed to ensure the validity of the model, and a new optimization function is constructed to optimize the multi-objective topology of the motor housing. The results show that the geometric reconfiguration of the motor housing concerning the optimized model force transmission path achieves the effectiveness of reducing resonance noise. The multi-objective topology optimization model provides a feasible way to solve the motor-specific frequency resonance and operation noise problems.

A novel integrated optimization method of micrositing and cable routing for offshore wind farms

In traditional wind farm planning, the design of wind turbine locations and cable layouts is usually undertaken sequentially. However, this approach may potentially result in suboptimal solutions. While the increased spacing between wind turbines enhances power output by reducing wake losses, it also imposes a negative impact by raising cable costs. Addressing this challenge, we propose a novel multi-objective optimization model that simultaneously considers the micrositing of wind turbines and cable routing. A joint framework of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and the Mixed-Integer Linear Programming (MILP) is established to optimize the layout of wind turbine locations, points of connection, and cable paths. The results indicate that, compared to the traditional sequential optimization, our integrated optimization exhibits significant economic advantages since improved the balance between micro siting and cable routing. By strategically sacrificing a portion of power generation to reduce cable costs, the overall investment profitability can be remarkably improved, with a maximum gain equivalent to 10.02 % of the cable costs.

Energy hub management for integrated energy systems: A multi-objective optimization control strategy based on distributed output and energy conversion characteristics

To improve the comprehensive utilization efficiency of energy, a multi-objective optimization control strategy applied to the energy hub (EH) within the system is proposed to address the electrical and thermal load distribution of the integrated energy system (IES) and the low-carbon economic operation. First, a model of the electrical and thermal energy outputs is established based on the characteristics of the IES network and the multidimensional "load parameter" evolution law. Moreover, a distributed control strategy is proposed that utilizes the information interaction between neighboring EHs to accurately share the electrical and thermal loads, which reduces the communication burden of the system while avoiding overloads. Furthermore, to coordinate the optimal operation of the devices within the hub, based on the energy conversion characteristics of the EH, a multi-objective optimization model is proposed that considers low-carbon and economic aspects to realize efficient energy use. Simulation results show that the proposed strategy effectively improves the robustness of the system while realizing proportional load distribution and low-carbon economic operation. Under the same load, when focusing on system economics, the operating cost is 2.182/¥ lower than when focusing on low-carbon systems, but carbon emission is 1.6753/kg CO2 higher.

Free vibration analysis and multi-objective robust optimization of three-dimensional pyramidal truss core sandwich plates with interval uncertain parameters

In this study, the free vibration analysis and multi-objective robust optimization of three-dimensional pyramidal truss core sandwich plates with interval uncertain parameters are fulfilled. The numerical model for free vibration of the plate is derived by combining the three-dimensional elasticity theory and Rayleigh-Ritz method, and the validity of the model is illustrated by numerical results. On this basis, considering various uncertainties within the plate, a new uncertainty-propagation analysis method is constructed by integrating the numerical model, interval-analysis model, kriging model and optimization. The one-dimensional and multi-source uncertainty-propagation analysis of the fundamental frequency is finished using this method, and the accuracy is proved by comparing with Monte Carlo simulation results. Meanwhile, to minimize the effects of uncertainties on the performance of plates at the design stage, an objective multi-objective robust optimization model with the objectives of maximizing the fundamental frequency and minimizing the robustness factor is established. Finally, an improved pelican optimization algorithm is proposed by introducing the improved opposition-based learning strategy, tracking strategy with step control and convergence strategy. And the Pareto front and corresponding design schemes applicable to different working environments are obtained without repeating the optimization.

Optimizing Urban Park Locations with Addressing Environmental Justice in Park Access and Utilization by Using Dynamic Demographic Features Derived from Mobile Phone Data

Urban parks play a critical role in improving the quality of life of local residents and creating sustainable and resilient urban environments. As urban parks are essential public services, it is important that they be accessible, sufficient, and of high quality so all city residents can access and benefit from them to advocate environmental justice. To make park services more equitable, it is crucial to evaluate the distribution and quality of existing urban parks and consider potential locations for new urban parks, in terms of both park access and utilization. By adopting a new type of dynamic population data based on mobile phone-based tracking, we assessed urban park access by vulnerable populations, particularly economically marginalized older adults, and employed dynamic demographic features and neighboring environmental features of existing urban parks and park candidate sites to estimate park utilization. Ultimately, we developed an advanced multi-objective location optimization model considering park access, utilization, and construction cost. The three objectives of maximizing utilization of the candidate sites when urban parks are established, maximizing the number of vulnerable people who can be covered by the urban parks, and minimizing the total establishment cost for the urban parks were optimized in locating urban parks among candidate sites. The proposed methodology was validated with an application to Ulsan Metropolitan City in the Republic of Korea.

A multi-objective operation optimization model for the electro-thermal integrated energy systems considering power to gas and multi-type demand response

A multi-objective operation optimization model for the electro-thermal integrated energy systems considering power to gas and multi-type demand response

Multi-objective Optimization Analysis of Construction Management Site Layout Based on Improved Genetic Algorithm

In construction management, the rationality of on-site layout is crucial for project progress, cost, and safety. In order to improve the rationality of on-site layout, a multi-objective optimization model combining ant colony algorithm and Pareto optimal solution was constructed based on genetic algorithm, and this model was applied to practical engineering cases. The results show that in terms of computational time, the genetic algorithm takes an average of 1702.0s, while the improved algorithm takes an average of 421.0s, which is 1281s less and 85.9% more than before the improvement. The performance of the improved algorithm is the best, and the optimal solution can be obtained through multiple iterations. The improved algorithm has improved the efficiency of on-site layout optimization, and possesses practical application value for the layout of construction management sites. It offers a certain reference for the reasonable setting of construction management sites.

Mechanical and thermal property analysis and optimization design of hybrid lattice structure based on triply periodic minimal surfaces

Hybrid lattice structures have significant potential in engineering applications owing to their outstanding mechanical and thermal properties. In this paper, an innovative design strategy for hybrid lattice structures is proposed involving the integration of primitive surfaces with truss lattice configurations. This approach allows for the full exploitation of the mechanical and thermal properties inherent in each component, leading to the development of new hybrid lattice structures. Firstly, the quasi-static compression and fluid-solid coupling heat conduction analysis of the hybrid lattice structure is carried out by numerical simulation. Then the effects of surface wall thickness, and truss diameter on the mechanical and thermal properties of the lattice structure are discussed. Additionally, the mechanical performance of the array hybrid lattice structure is evaluated through quasi-static compression experiments. These experimental results are compared with those from the numerical simulations, validating the accuracy of the simulation model. Finally, a multi-objective optimization model targeting specific elastic modulus and heat dissipation performance (indicated by Nusselt number) is established, in response to the demand of aviation and aerospace industries for lightweight, load-bearing, and heat-dissipating multi-functional integrated lattice design. The non-dominated sorting genetic algorithm is utilized to solve the optimal model, and the distribution of feasible solutions and Pareto front are obtained. The results show that the interpenetrating hybrid lattice structure has a greater improvement in mechanical and thermal properties than the primitive surfaces. This research holds significant implications for the design of multi-performance hybrid lattice structures.