Multi-objective Optimization Model Research Articles

Replacement Strategy for Aging Nuclear Power Instrumentation and Control Equipment Based on Life Margin

The instrumentation and control (I&C) equipment in nuclear power plants gradually ages and becomes obsolete with increased operation time. Its performance deteriorates, and the probability of its failure also increases gradually. The failure of I&C equipment may directly lead to the degradation of the control or protection functions, reducing the reliability and safety margin required by the design. This will hurt the safety and stable operation of nuclear power plants. Therefore, an aging I&C management/replacement strategy is necessary to control and minimize this problem. In this regard, this paper establishes a module lifetime evaluation model described by a composite probability density function for modules composed of multiple components. On this basis, we have developed a multi-objective aging replacement optimization model aimed at high reliability, economy, and feasibility, and propose an equipment aging replacement optimization calculation method based on the linear-weighted discrete state transition algorithm. The procedure is verified by the application of data from nuclear power engineering. The results show that the proposed aging replacement strategy and method can significantly reduce computational difficulty, improve equipment reliability, and lower aging replacement costs.

Design optimization of vibration amplitude reduction based on virtual prototype and machine learning

The traditional design optimization of vibration amplitude reduction mainly has the disadvantages of low modeling and prediction accuracy as well as low optimization efficiency. Therefore, this paper presents a design optimization method for vibration amplitude reduction based on virtual prototyping and machine learning, which combines the high accuracy of numerical calculations with the efficiency of machine learning, overcoming the shortcomings of traditional methods. Firstly, sample points are collected through the design of experiments and virtual prototype simulation. Then, based on the sampled data, a prediction model for the relationship between the design parameters and the amplitude of the product is established using Genetic Algorithm-Support Vector Regression (GA-SVR). On the basis of the GA-SVR prediction model, a multi-objective optimization model of product is established, and Multiple Objectives Particle Swarm Optimization -entropy weight- Technique for Order Preference by Similarity to Ideal Solution (MOPSO-entropy weight-TOPSIS) is used to solve for the optimal design parameters. Finally, the washing machine suspension system is used as an example to verify the effectiveness of the model. The results show that, compared with the original design scheme, the design scheme obtained by the model can reduce the amplitude of the washing machine suspension system by 12.68%, and reduce the total weight of the counterweight by 7.35%. This method is conducive to the intelligent and efficient design optimization of vibration amplitude reduction, and is of great significance to product life cycle design.

A multi-objective Fuzzy Robust Optimization model for open-pit mine planning under uncertainty

A multi-objective Fuzzy Robust Optimization model for open-pit mine planning under uncertainty

Multi-objective optimization of building envelope of rural housing in the severely cold region of China based on energy consumption, thermal comfort and cost-effectiveness

The performance of rural residential buildings has a great energy-saving potential due to the great spontaneity and ignorance of the rural residential construction. To solve this problem, this paper provides a multi-objective optimization model based on Chebyshev inequality. The model takes energy consumption, indoor thermal comfort, project cost as optimization objectives and considers operating conditions in winter and summer. This study taking a rural residential house as an example and analysed the influence of its envelope parameters on building performance using EnergyPlus. The parameters of the building envelope were optimized, which verified the superiority of the method. Optimization results showed that the heating energy consumption was reduced by 29%, and the air conditioning energy consumption was reduced by 6%. This study analysed the relationship between the building energy consumption, indoor thermal comfort and the cost by multi-objective optimization method, to provide a new research idea for rural residential research. Optimal design solutions were generated under various weighting coefficients. The model aimed to meet the climatic and economic conditions of northern China and similar regions. Therefore, based on these optimization results, decision-makers can choose optimal design and construction solutions according to their own will.

Design and Control of Radial Electromagnetic Bearing for Flywheel Battery

Abstract High rotational speed is the key to increasing energy storage capacity of flywheel battery. Non-contact bearing ensures reliable operation of high-speed flywheel battery. To design an electromagnetic bearing that meets the requirement of high-speed flywheel battery, firstly, a multi-parameters and multi-objectives optimization model for radial electromagnetic bearing is established. The optimization aims at the smallest spatial volume while ensuring electromagnetic force. A 16.58% reduction of the spatial volume is obtained. Furthermore, the influence of bias current and control current on the electromagnetic force and rotor displacement of radial magnetic bearing are analyzed. The bias current and control current that meet the requirements of current stiffness and displacement stiffness are determined. Based on this, a differential control model for radial magnetic bearing is constructed. Then, a controller based on adaptive discrete sliding mode control is designed. Finally, performance test and data analysis are completed. Results indicate that the designed radial electromagnetic bearing is able to achieve low response error, low system jitter and high robust under different test conditions. Compared with traditional sliding mode control method, under stochastic excitation, the maximum and average tracking errors of bearing displacement are reduced by 62.31% and 42.74%. The energy consumption is decreased by 49.32%.

Green Transformation of Industrial Structure of Jiaxing City Driven by Synergizing the Reduction of Pollution and Carbon Emissions

The "Implementation Plan for Reduction of Pollution and Carbon Emissions" emphasizes the need to explore mechanisms for pollution and carbon emission reductions, strengthen efforts for synergistic reduction, and accelerate the green and low-carbon development in different types of cities. Thus, this study established a multi-objective optimization model for the industrial structure of the city based on green total factor productivity （GTFP）, which was applied to Jiaxing, an emerging coastal city. The results indicated an overall 8% increase in GTFP across various industries in Jiaxing from 2016 to 2019. The top three economic sectors - chemical products； electricity and thermal production and supply； and communication equipment, computers, and other electronic devices-ranked 12th, 15th, and 5th in GTFP, respectively. Conversely, industries with the top three GTFP rankings-other manufacturing products, specialized equipment, food, and tobacco-ranked 19th, 12th, and 14th in GDP, respectively. Given the mismatch between economic output and environmental benefits among different industries, deepening industrial structure adjustment and promoting synergistic pollution reduction and carbon reduction are imperative. The chemical industry （decreasing by 2%） and the electricity and thermal production and supply industry （decreasing by 1%） crucially need industrial transformation. This can be achieved by setting higher environmental thresholds, reducing the growth rate of fixed asset investment, and curbing the final consumption of these industries to enhance the efficiency of existing facilities. Meanwhile, the service industry （increasing by 5%） should be rapidly developed, while other industries can continue to grow at their current pace. The city-level industrial structure optimization model established in this study can effectively support future decision-making for the dual control of total carbon emissions and intensity, foster new-quality productivity, and promote carbon peaking and green low-carbon high-quality development in different cities.

Optimal Allocation of Urban Water Resources Based on Multi-Objective Nutcracker Optimization Algorithm

The imbalance between water supply and demand (WSD) has been growing noticeable as a result of the economy’s fast expansion which can be effectively alleviated using optimal allocation of water resources. An urban water resources allocation (WRA) model based on the innovative Multi-Objective Nutcracker Optimization Algorithm (MONOA) is proposed in this study. Taking into account economic, social and ecological benefits, a comprehensive multi-objective optimization (MOO) model is established. By introducing the opposite learning strategy, non-dominated sorting approach and crowding distance mechanism to a recently reported intelligent optimization algorithm called the Nutcracker Optimization Algorithm (NOA), the novel nature-inspired metaheuristic algorithm MONOA is proposed to solve the multi-objective optimization model. The MONOA is evaluated on ten benchmark test functions, and it exhibits superior distribution and convergence by comparing with some highly cited algorithms. The proposed model is applied to Handan, China, in order to obtain a reasonable water allocation scheme in the planning year. The simulation results reveal that the economic benefit is in the range CNY [1.36, 1.44] × 1011, water shortage is in the range [0.66, 0.98] × 108 m3 and COD emission is in the range [3.70, 3.91] × 104 t in all the obtained Pareto solutions. The water resources management departments might create customized water allocation plans by balancing different goals and taking preferences into account. Moreover, the proposed method is a general approach that can be applied to many other cities. Hence, it is of great significance to the sustainable development and utilization of urban water resources.

Multi-Objective Model for Residential Energy Management in Context of Individual Self-Consumption

The European Union (EU) strongly urged its member states to take action to establish energy communities. Domestic consumers now have the opportunity to organize themselves to play an active and decisive role in the electricity supply chain and make a solid contribution to an ecological footprint. This work proposes residential energy management, considering renewable resources production (photovoltaic panels) and energy storage systems for individual self-consumption approach and boosting future participation in energy community interactions. A multi-objective optimization model using Biased Random Key Genetic Algorithm (BRKGA) was adapted and implemented to minimize electricity consumption costs and maximize self-consumption usage. An energy storage system with 10kW was considered to optimize electricity management. Two scenarios were proposed, one considering a residential prosumer with a photovoltaic (PV) power installed value of 3.56kWp and the other with 4.20kWp. The results showed a reduction in electricity consumption between 7%-8% and an increase in self-consumption supported by a battery energy storage system of nearly 50%. Data from an actual system were used to obtain realistic scenarios.

Bilateral Data Asset Matching in Digital Innovation Ecosystems: A Regret Theory Approach

This study addresses the bilateral matching of data assets with expected levels in digital innovation ecosystems, incorporating regret-avoidance behavior. First, given the potential hesitation between two parties throughout the matching process, expressing preference information using probability hesitant fuzzy sets is reasonable. Second, the Lance scoring function best captures the gap in expectation and satisfaction between the matching parties. Based on regret theory, we develop a matching strategy that considers both parties’ utilities and satisfaction levels. We construct an optimization model to determine criteria weights using a novel Lance distance metric. Subsequently, a multi-objective optimization model is formulated to maximize satisfaction while ensuring stability in the supply–demand matching process. A numerical example underscores the suggested method's effectiveness and shows its practical applicability in data asset matching scenarios. This study advances the field by integrating psychological factors and sophisticated fuzzy set theory into the decision-making process for allocating data assets in digital ecosystems.

Research on carbon emission accounting and low carbon operation methods for urban water supply systems

Under the environment of global warming, how to promote the innovative and efficient development of water supply system is the key to sustainable urban development. There is still room for improvement in the study of carbon emissions in urban water supply systems and there is a lack of appropriate rationale for low-carbon operation and management of waterworks. A water-energy-carbon (WEC) framework is established in Fuzhou to account for the carbon emissions from 2013 to 2022 and the level of coupling and coordination development is evaluated in this study. A multi-objective optimization model and a system dynamics (SD) model were developed to optimize the low-carbon operation of the water supply system and predict future climate impacts. We also provide a quantitative and qualitative evaluation method for low-carbon operation. The results show that carbon emissions generated by electricity consumption and chemical use in the water supply system contribute greatly, and the coupling coordination level is high. Under different scenarios, the optimal solutions of carbon emissions, carcinogenic risk factors and unit energy economy range from 1.63 to 3.82 kt CO2-eq, 4.4 × 10−5 to 2.7 × 10−4, and 0.26 to 0.73 RMB/MJ. In the SD model, the water supply system can achieve 17 % and 20 % carbon reduction in 2025 and 2030. Its carbon reduction direction is mainly reflected in public service water use. This study provides a reference for optimizing the operation of water supply systems.

Mining Area Production Safety Optimization Based on Multi-objective Particle Swarm Optimization Model

Safe production in metal mines is an important task to ensure the safety of workers and the integrity of production equipment. From the perspective of optimization, this paper establishes an early warning model and a multi-objective particle swarm optimization algorithm model by analyzing the relevant production data affecting the indicator system of mine safety production, and then solves the model through the multi-objective particle swarm optimization algorithm to give an optimization scheme for maximizing the safety production of the relevant mines. For the safety production problems in metal mines, four indicator systems are proposed, namely, production ecological environment safety, production personnel safety standards, production equipment safety and production information security, and then the relevant production data of the four indicator systems are analyzed and the early warning model is established. Based on the relevant production data of the four indicator systems, the mathematical relationship between the production data affecting each indicator system is constructed. Then, a multi-objective particle swarm optimization algorithm is established to construct the relationship between the maximum safe production of the mine and each indicator system, and the index of the maximum safe production production data of the mine is obtained. The feasibility of the mine safety production optimization scheme is given.

Optimization of the seru production system with demand fluctuation: A Mean-CVaR model

The increasingly diverse and non-deterministic customer demands have given rise to the seru production system. This study aims to address the multi-objective seru formation problem under demand fluctuations, with the mean and the CVaR or standard deviation of total completion time minimized. In particular, two multi-objective stochastic optimization models, i.e., Mean-CVaR and Mean-Std, are constructed to determine the number of seru, the allocation scheme of workers and batches. Then, the Local Search Fast Non-dominated Sorting Genetic Algorithm (NSGA-II-LS) is designed to solve the proposed models. Numerical experiments validate the method’s effectiveness by demonstrating that, compared to the Mean-Std model, the Mean-CVaR model enhances the seru system’s capability and stability in coping with stochastic demand fluctuations through an average reduction of 4% in expected value and an average reduction of 36% in standard deviation. The effects of confidence level, the product batch number, and the standard deviation on the results have been analyzed. The results show that the Mean-CVaR method helps achieve a more stable seru system in terms of reducing standard deviation. Moreover, product batch division is beneficial to the CVaR of total processing time but detrimental to the mean processing time.

A robust multi-objective optimization model for grid-scale design of sustainable cropping patterns: A case study

In the face of growing population, water scarcity, and increasing food demand, there is a pressing need to shift towards optimized, resource-efficient, climate-resilient, and sustainable agricultural practices. In light of that, designing a sustainable cropping pattern that considers the procedure of resource allocation based on land capabilities and crop features is vital for ensuring long-term food production and safeguarding the delicate balance of ecosystems. Motivated by this imperative, this study proposes a comprehensive framework that integrates Geographic Information System (GIS), System Dynamics (SD), and optimization to address the sustainable design of cropping patterns. The framework assesses grid-scale land suitability, models dynamic water resource interactions, and optimizes resource allocation based on crop calendar considerations. For the first time, a dynamic crop inventory is integrated into the cropping pattern optimization process, addressing food security concerns in a comprehensive manner. In order to evaluate the effect of uncertainties on the designed system, a robust optimization model is developed based on convex sets. The results demonstrate the advantages of the robust model in situations with uncertainty. Despite a 5% reduction in profit compared to the deterministic solution, the robust design achieves a 25% decrease in irrigation, highlighting the cost of ensuring sustainability. The deterministic approach prioritizes crops based on their economic value, whereas the robust solution considers the volume of irrigation required for a sustainable design. The managerial implications emphasize the importance of prioritizing water-efficient and climate-resilient agricultural practices to guarantee long-term food security.

A bi-level multi-objective optimization approach for carbon policy formulation towards food waste resource treatment from environmental, energy and economic perspectives

Excessive food waste contributes to the greenhouse effect. Although low-carbon technology has the potential to reduce carbon emissions and generate renewable energy, the high costs may deter the adoption. To promote low-carbon food waste treatment, local authorities could develop carbon reduction policies targeted at the food waste treatment industry, which could lead to a game between local authorities and food waste treatment enterprises. Hence, a bi-level multi-objective optimization model is put forward to balance the decisions of local authorities and those of food waste treatment enterprises. Enterprises can choose which carbon reduction policies to implement, and local authorities determine the intensity of specific policies. After testing this model on Shenzhen’s food waste treatment system, the model’s validity and feasibility were confirmed. It was discovered that by identifying suitable carbon reduction policies, the model can help enterprises achieve more than 50% carbon reduction. Moreover, different enterprises may choose different carbon reduction policies to implement. Optimal food waste disposal arrangements need flexible adjustments. The constructed model can serve as a decision-making tool for city managers to reduce carbon emissions in food waste treatment.

Optimisation of hexagon-shaped floating units using the response surface method

ABSTRACT A hexagon-shaped floating unit (HS-FU) was proposed, and the optimisation was performed with respect to the geometric dimension based on the response surface method (RSM). A multi-objective optimisation model considering the weight and heave response was established to achieve a satisfactory balance between economy and safety, constrained by the roll, initial metacentric height, freeboard, and collision issue. The results show that, opposite to the heave response, the structural weight is positively proportional to the side lengths of the water tank and living area; the stability can be improved by increasing the water tank height and the living area side length. The combination of weighting factors with a value of 0.4 for weight and 0.6 for heave was selected as one of the optimal designs. Compared with the initial design, the optimal design shows better performance, with the heave response decreasing by 22.51% and the initial metacentric height increasing by 28.30%.

Path Analysis of Recycling of Urban Solid Waste Based on Multi-Objective Optimization Model

With the acceleration of urbanization and rapid economic development, the problem of urban solid waste (SW) has become increasingly prominent. Traditional SW disposal treatment methods, mainly landfill and incineration, pose issues of environmental pollution and resource waste. This study aims to analyze the recycling pathways of urban SW and establish a multi-objective optimization model to achieve comprehensive optimization of economic, environmental, and social goals. Through data collection, quantification and standardization, establishment of a multi-objective optimization problem model, NSGA-II algorithm solution, and simulation and result analysis, this paper seeks to provide scientific decision support and technical guidance for urban SW management. Taking Wuhan as an example, this study analyzes the treatment methods of six different types of SW and seeks the optimal treatment pathways through the multi-objective optimization model. The results show that the multi-objective optimization model can effectively balance economic costs, environmental costs, and utilization efficiency in SW treatment and utilization, providing new ideas for achieving urban sustainable development and resource recycling.

A multi-objective optimization model for cropland design considering profit, biodiversity, and ecosystem services

More sustainable agricultural methods are needed to alleviate the decreases in biodiversity and ecosystem services that have occurred because of industrial agriculture. One such method is the inclusion of alternative crops into croplands that can support biodiversity, reduce erosion and chemical runoff, and sequester carbon in the soil. However, the question of where such crops should be planted to balance competing economic and environmental objectives remains open. To this end, we develop a mixed-integer quadratically constrained program to optimize the layout of a cropland considering economic, biodiversity, greenhouse gas emissions, and water quality objectives. We include spatially varying fertilization as a decision variable in addition to crop establishment location. We further include the effect of core area and edges between different crops on biodiversity. To demonstrate the applicability of the model, we apply it to an example field, showing how the optimal cropland design changes as a decision-maker prioritizes different objectives and as edges have different impacts on biodiversity.

Heat Transfer Simulation and Structural Optimization of Spiral Fin-and-Tube Heat Exchanger

The spiral fin-and-tube heat exchanger is a widely used heat transfer device in heating and cooling applications, and its performance is influenced by multiple structural parameters, including the pitch, thickness, and height of the fins, the diameter and thickness of the base tube, and the transverse and longitudinal tube spacings. This study comprehensively explores how these factors affect the heat transfer performance of the spiral fin-and-tube heat exchanger and aims to determine its optimal configuration of structural parameters. First, orthogonal experiments are arranged based on these factors to conduct the corresponding finite element numerical simulations and to determine the effects of these factors on the heat transfer and resistance performance of the spiral fin-and-tube heat exchanger. Subsequently, support vector regression (SVR) is introduced to predict the heat transfer factor and the resistance factor, with the aim of benefiting the construction of a multi-objective optimization model for optimizing the two factors simultaneously. Then, a comprehensive performance indicator is used to transform the multi-optimization problem to a single optimization problem, and the genetic algorithm is adopted to solve an optimal configuration of the heat exchanger structural parameters. Ultimately, the finite element numerical simulation is utilized to validate the accuracy of the optimization. Case studies are conducted on a specific spiral fin-and-tube heat exchanger. After the optimization, the heat transfer factor is improved by 44.44%, and the resistance factor is increased by 14.19%. However, the comprehensive performance indicator is increased by 38.79%.

Study on Propeller Positioning and Trajectory Control of Underwater Robots Based on Bayesian Inference and Multi-Objective Optimization

In ocean exploration, the positioning and trajectory optimization of underwater vehicles are critical research areas that directly impact the success of ocean resource exploration, environmental monitoring, and search and rescue missions. However, the complex and uncertain marine environment poses significant challenges. This study first determines the three-dimensional spatial position of the underwater vehicle, calculates the vehicle's speed in the marine environment through mechanistic analysis, traversing the initial position, velocity, acceleration, angular velocity and angular acceleration of the submersible, fits the motion trajectory using a random walk, and uses Bayesian inference to estimate maximum probability positions of submersibles and select the optimal initial deployment point. To better cope with real-world scenarios involving ocean currents and the diverse motion modes of underwater vehicles, this study further used an improved genetic algorithm to divide the motion trajectory of the submersible into a series of discrete path points, and constructed a multi-objective optimization model with the shortest search and rescue time and the maximum probability of successful search and rescue as the objective function to realize the precise positioning of the lost underwater vehicle.

Interactive multiobjective evolutionary optimization model for dam management support

Dam management optimization is a complex multiobjective problem, whose current solutions struggle to spread in normal practice. Here, an interactive evolutionary multiobjective optimization method is proposed, which involves stakeholders by using multi-criteria decision analysis to embed a parsimonious elicitation of their preferences and expertise in the optimization process. Specifically: (i) one or more decision-makers are asked to rank a set of representative dam management strategies in order of preference; (ii) the ranking is used to build a preference model; and (iii) the preference model is used in an evolutionary multiobjective optimization algorithm to converge to the part of the Pareto front most preferred by the decision-makers. The inclusion of a user-friendly interaction in the optimization methodology makes the process more understandable for stakeholders and policy-makers, and it allows to address some of the key obstacles to the practical implementation of multiobjective dam management optimization, even when a large number of objectives is considered. The proposed methodology is applied to a case study (Lake Pozzillo, Sicily, Italy) with five objectives, where flood control is in contrast with irrigation water demand and hydropower production. Five simple and easy to implement optimal strategies are obtained by interacting with five decision-makers. The results are compared to the management practice currently used, indicating that the proposed approach can satisfy water demands while greatly improving flood attenuation.