Dual-objective Optimization Model Research Articles

Improved NSGA2 algorithm for recovery of interrupted departure flights

To solve the problem of airline flight disruption caused by emergencies, the disrupted departing flights are restored, a dual-objective optimization model is constructed to minimize the total delay cost of airlines and minimize the total delay time of passengers. An adaptive non-dominated sorting genetic algorithm based on dominance intensity is designed, and three improved operations are proposed: a fast dominance sorting method, a new congestion distance and an adaptive elite retention strategy. The proposed algorithm is verified by the operation data of an airline at Fuzhou Airport. The experimental results show that compared with the traditional plan-first-serve method, the solution obtained by the proposed algorithm is greatly optimized. Compared with the constraint method, the solution time is generally lower than the constraint method and the solution result is close to the optimal result obtained by the constraint method. Compared with multi-objective optimization algorithms such as NSGA2 and MOEAD, the proposed algorithm shows better performance and can solve the problem effectively and efficiently, providing a basis for airlines to achieve optimized solutions.

An inter-provincial coordinate model under Renewable Portfolio Standards policy based on tradable green certificate options trading

The current territorial management model under Renewable Portfolio Standards in China cannot fully develop renewable energy due to the mismatch between high energy-consumption regions and renewable energy-rich regions. Therefore, we proposed an inter-provincial coordinate model based on tradable green certificate options trading (CMTOT), which includes four components: (1) A dual-objective optimization model is established to minimize the RPS target realization cost and maximize the pollutant emission reduction based on Blacke-Scholes option pricing model. (2) The identification of buyer and seller provinces of tradable green certificate (TGC) options based on the above model. (3) An optimization model in buyers' or sellers' cooperation alliance is established. (4) The cost allocation models by Shapley value and Nucleolus are established to ensure the fairness in different scenarios. A case study of Tianjin, Shandong, Shanxi and Inner Mongolia shows that the CMTOT can effectively reduce the RPS target realization cost by 16.79 % and increase the pollutant emission reduction by 4.32 %. A sensitivity analysis confirms the robustness of the CMTOT. The results show that the CMTOT can both fully develop renewable energy and effectively reduce pollutant emission. Therefore, we proposed some policy recommendations to support the implementation of the CMTOT.

Optimization of solar heating systems for office buildings in solar enriched areas with Time-zoned heating strategy

Time-zoned heating combined with solar heating system can improve building energy conservation, but the combined system design method is significantly different from conventional systems. Therefore, this study proposes a general overall optimization design method for solar heating system with Time-zoned heating mode from the overall consideration of system operational performance and building heating effectiveness. In addition, a Dual-objective optimization model that balances economic and heating performance has been proposed. Take an administrative office building with Time-zoned heating mode in Lhasa as an example, this study provides a suggested range of design parameters based on the analysis of the effects of different ratio of heat storage tanks volume to solar collector area (Vt/Ac). The optimization of solar collector area, heat storage tank volume, auxiliary heat source power, are achieved by employing levelized cost of heat (LCOH) and non-guaranteed heating hours as the optimization objectives. The study results indicate that the optimal Vt/Ac ratios are 0.02–0.04 m3/m2 for better system operational performance and 0.02–0.08 m3/m2 for better building heating effectiveness. Non-guaranteed heating hours occur mostly during the initial 1–2 h of heating start-up on weekdays. Meanwhile, when the non-heating guarantee hours are limited to 3500–4000 h, the solar heating system design scheme obtained from the optimized model has a certain degree of economic viability. Reducing the cost of solar collectors and heat storage tanks can help obtain more competitive solar heating system design solutions. The research results can provide new method and reference for designing solar heating system with Time-zoned heating mode.

Group-Based Federated Knowledge Distillation Intrusion Detection

Intrusion detection based on federated learning allows the sharing of more high-quality attack samples to improve the intrusion detection performance of local models while preserving the privacy of local data. Most research on federated learning intrusion detection requires local models to be homogeneous. However, in practical scenarios, local models often include both homogeneous and heterogeneous models due to differences in hardware capabilities and business requirements among nodes. Additionally, there is still room for improvement in the accuracy of recognizing novel attacks in existing researches. To address the challenges mentioned above, we propose a Group-based Federated Knowledge Distillation Intrusion Detection approach. First, through a step-by-step grouping method, we achieve the grouping effect of intra-group homogeneity and inter-group heterogeneity, laying the foundation for reducing the aggregation difficulty in intra-group homogenous aggregation and inter-group heterogeneous aggregation. Second, in intra-group homogenous aggregation, a dual-objective optimization model is employed to quantify the learning quality of local models. Weight coefficients are assigned based on the learning quality to perform weighted aggregation. Lastly, in inter-group heterogeneous aggregation, the group leader model’s learning quality is used to classify and aggregate local soft labels, generating global soft labels. Group leader models utilize global soft labels for knowledge distillation to acquire knowledge from heterogeneous models. Experimental results on NSL-KDD and UNSW-NB datasets demonstrate the superiority of our proposed method over other algorithms.

Multi-Modal Urban Traffic Transfer Schedule Timetable Bi-Objective Optimization: Model, Algorithm, Comparison, and Case Study

The optimization of the connection between urban rail transit and the bus is an essential issue that benefits passenger travel and the urban structure and has social benefits, which can be realized by reasonably adjusting the bus departure schedule. This study is necessary because the development status quo of China’s urban transportation network planning is unreasonable, travel efficiency is not high, and operating costs are high. This paper sets up the decision variables of bus departure time and departure interval at each station, establishes a dual-objective optimization model with the minimum schedule change and the minimum transfer time, and studies the application of the augmented Chebyshev algorithm in the dual-objective optimization model. Secondly, based on the Shenzhen metro and public transportation integrated circuit card data, the case analysis uses the generalized Chebyshev algorithm and the non-dominated sorting genetic algorithm, respectively. The optimization results show that using the improved augmented and generalized Chebyshev algorithm in the bus schedule alteration time within a reasonable range can maximize the total transfer time, which compared with the original scheme is shortened by 68.06%. In contrast, genetic algorithms will make the complete bus schedule alteration prominent, and the whole transfer time is substantially increased. The results show that the improved augmented generalized Chebyshev algorithm is more suitable for solving the dual-objective rail transit connection problem.

The Impact of Dual Objective Optimization Model Combining Non-Dominated Genetic Algorithm on Rural Industrial Ecological Economy

The Impact of Dual Objective Optimization Model Combining Non-Dominated Genetic Algorithm on Rural Industrial Ecological Economy

Incorporating Socio-Economic Factors in Maximizing Two-Dimensional Demand Coverage and Minimizing Distance to Uncovered Demand: A Dual-Objective MCLP Approach for Fire Station Location Selection

In this study, we employ a dual-objective optimization model, utilizing the Implicit Modified Coverage Location Problem (MCLP-Implicit) approach, to determine an appropriate fire station allocation. Our objectives encompass maximizing coverage in areas with a heightened projected demand, based on socioeconomic predictors of building fires, and concurrently minimizing the distance of uncovered demand zones to the closest fire station. The challenges of this model reflect the criticality of strategic placement, aiming for not just swift response times but also the complete coverage of a region with priority given to subregions with a pronounced potential for incidents. The applicability of the proposed approach is demonstrated through a case study in south-east Queensland. Our findings indicate that there is a tangible justification for adopting this model. The broader coverage and wider spread of locations it produces can greatly aid in the dynamic deployment of personnel during surges caused by seasonal fluctuations or unforeseen calamities. By weaving in socioeconomic aspects into demand predictions, our model has also maintained appropriate coverage to socioeconomically disadvantaged communities in the region. Nevertheless, it is paramount to underline the recommendation that further research should account for road connectivity—a pivotal factor when pinpointing these locations in the real world. This research paves the way for enriched insights in long-term urban planning and fire response protocol crafting, especially in regions mirroring similar socioeconomic profiles or risks of natural disasters, not to mention its commercial implications to the relevant industry servicing the fire and rescue authorities.

Urban water resources management with energy constraint and carbon emission intensity under uncertainty: A dual objective optimization model

Water production, utilization and recycle in urban system associated with a large amount of energy consumption and carbon emission that has increasingly international attention under sustainable development goals. In this study, a comprehensive urban water resources system management model is proposed under water-energy-carbon nexus and multiple uncertainties. The water-energy-carbon nexus was reflected in the processes of water extraction, water production and sewage treatment, and two objectives of maximizing the system benefit and minimizing carbon emission is transformed into two linear programming problem through the dual-objective interval fractional programming. Considering the interaction among water supply/demand, pollutants discharge control, energy consumption and self-energy utilization, indirect carbon emission as the main constraints, the developed model is applied to a typical water and energy sources shortage area of Qingdao city in north China. Multiple scenarios related to energy consumption and carbon emission control are designed to search the relationship among energy consumption, carbon emission and water resources allocation schemes. The results indicated that water demand as the main driving factor of energy consumption and carbon emission in urban water system cannot be directly limited through water supply structure adjustment. Through introducing self-energy supply (sludge utilization) and energy consumption control, water resources allocation schemes and water supply structure would be changed to improve energy saving potential with an obvious decrease trend of carbon emission intensity from 0.7921 tCO2/MWh to 0.5544 tCO2/MWh. The model could effectively provide reasonable urban water system management strategies under different water allocation demands and carbon emission challenges scenarios and deal with multiple uncertainties in decision-making process.

A Two-Stage Bilateral Matching Study of Teams-Technology Talents in New R&D Institutions Based on Prospect Theory

This study considers two-stage bilateral matching of teams and scientific and technological talents in new R&D organizations and proposes a two-stage dual-objective bilateral matching method based on prospect theory. The matching of teams and scientific and technological talent in new R&D institutions is divided into two stages: elimination matching in the first stage and selection matching in the second stage. In the first stage, the evaluation index of the team to talent and the cost index of talent are constructed, the dual reference points of peer and expectation are set for evaluating talent, and the bottom-line reference points are set for talent cost. The comprehensive prospect value in the first stage is calculated based on prospect theory, and the matching in the first stage is completed based on the dual-objective optimization model with the highest evaluation value and the lowest cost value. In the second stage, using the matching results of the first stage, the team evaluates the talent again, while the talent ranks the team to obtain the satisfaction value, and completes the second stage of bilateral matching based on prospect theory and the dual-objective optimization model with the highest evaluation value and the highest satisfaction value. Finally, a case study and method comparison show that the proposed method is feasible and effective.

A Bi-objective Optimization Study Based on Elite Selection for Electric Vehicle Charging

Electric vehicle charging increases the grid-side load, and the safety and economy of grid operation are affected. First, we consider the influence of climatic factors on EV charging and simulate the daily load profile under disorderly charging using the Monte Carlo method. Then, the elite selection is proposed in the context of traditional algorithms to establish a dual-objective optimization model with the minimum peak-to-valley difference on the grid side and the minimum total cost on the customer side. Finally, a city grid is used as an example to verify that the model is effective. The results show that the establishment of a time-of-use tariff for electricity market supply and demand and the use of elite selection bi-objective optimization can reduce peak-to-valley differences and smooth out grid fluctuations, improving the safety and economy of grid operation.

The Construction and Application of Dual-Objective Optimal Speed Model of Liners in a Changing Climate: Taking Yang Ming Route as an Example

In a changing climate, ship speed optimization plays an important role in energy conservation and emission reduction. In order to establish a dual-objective optimization model of minimizing ship operating costs and reducing carbon emissions, fuel costs, berthing costs, emission costs and fixed cost during sailing cycles, the emission reduction strategies of ships using MGO in emission control areas and the AMP in ports are taken into account. The PSO algorithm is adopted to find the Pareto solution set, and the TOPSIS algorithm is used to screen the optimal compromise solution, while Yang Ming, a trans-Pacific route, is selected to verify the applicability of the model. The result shows that the optimization model can effectively reduce the operating cost during sailing cycles and control carbon emissions, which can provide references for ship operation decision-making to achieve carbon peaking and carbon neutrality.

An Enhanced NSGA-II for Solving Berth Allocation and Quay Crane Assignment Problem With Stochastic Arrival Times

The berth allocation and quay crane assignment problem (BACAP) is an important port operation planning problem. To obtain an effective and reliable schedule of berth and quay crane (QC), this study addresses the BACAP with stochastic arrival times of vessels. An efficient method combining scenario generation is presented to simulate the stochastic arrival times. After then, a mixed integer linear programming (MILP) model is established, aiming to minimize the expectation of the vessels’ total stay time in port. A multi-objective constraint-handling (MOCH) strategy is adopted to reformulate the developed model, which converts constraint violations into an objective, thus transforming the single-objective optimization model with complex constraints into a dual-objective optimization model with only easy-handling constraints. Then an enhanced non-dominated sorting genetic algorithm II (ENSGA-II) is proposed to solve the dual-objective model, in which a neighborhood search algorithm and a search bias mechanism are incorporated to strengthen the local exploitation capability. Furthermore, a repair method (RM), penalty function (PF) and the superiority of feasible solutions (SF) strategy for constraint handling are designed respectively and incorporated with genetic algorithm to solve the original single-objective optimization model. Finally, numerical experiments on instances in the literature are conducted to validate the effectiveness of the MOCH and the proposed ENSGA-II. The results show that the average total stay time of vessels is reduced when stochastic arrival times are considered. Comparison results with another two multi-objective methods and three single-objective methods combined with different constraint-handling strategies corroborate the superiority of the proposed ENSGA-II and MOCH.

Regional Renewable Energy Optimization Based on Economic Benefits and Carbon Emissions

With increasing renewable energy utilization, the industry needs an accurate tool to select and size renewable energy equipment and evaluate the corresponding renewable energy plans. This study aims to bring new insights into sustainable and energy-efficient urban planning by developing a practical method for optimizing the production of renewable energy and carbon emission in urban areas. First, we provide a detailed formulation to calculate the renewable energy demand based on total energy demand. Second, we construct a dual-objective optimization model that represents the life cycle cost and carbon emission of renewable energy systems, after which we apply the differential evolution algorithm to solve the optimization result. Finally, we conduct a case study in Qingdao, China, to demonstrate the effectiveness of this optimization model. Compared to the baseline design, the proposed model reduced annual costs and annual carbon emissions by 14.39% and 72.65%, respectively. These results revealed that dual-objective optimization is an effective method to optimize economic benefits and reduce carbon emissions. Overall, this study will assist energy planners in evaluating the impacts of urban renewable energy projects on the economy and carbon emissions during the planning stage.

Optimal planning of photovoltaic-storage fast charging station considering electric vehicle charging demand response

The charging demand response of electric vehicle(EV) users will affect the social and economic benefits of fast charging services, so it is an important factor in EV charging station planning. In this paper, a photovoltaic-storage fast charging station(PSFCS) planning method considering charging demand response is proposed. Firstly, the EV fast charging behaviors and the factors influencing the admitting ability of PSFCS to the fast charging load are discussed, and the interaction characters between them are analyzed. Based on the above analysis, a three-stage dual-objective optimization model of PSFCS planning considering charging demand response is proposed and solved by the elitist non-dominated sorting genetic algorithm(NSGA-II). Finally, the case study is formulated based on the 21 power nodes-12 traffic nodes network, simulation results show that the proposed method can effectively improve the profit of charging service providers and reduce the total charging time of EVs.

Joint Transmit Resource Management and Waveform Selection Strategy for Target Tracking in Distributed Phased Array Radar Network

In this article, a joint transmit resource management and waveform selection (JTRMWS) strategy is put forward for target tracking in distributed phased array radar network. We establish the problem of joint transmit resource and waveform optimization as a dual-objective optimization model. The key idea of the proposed JTRMWS scheme is to utilize the optimization technique to collaboratively coordinate the transmit power, dwell time, waveform bandwidth, and pulse length of each radar node in order to improve the target tracking accuracy and low probability of intercept (LPI) performance of distributed phased array radar network, subject to the illumination resource budgets and waveform library limitation. The analytical expressions for the predicted Bayesian Cramér–Rao lower bound and the probability of intercept are calculated and subsequently adopted as the metric functions to evaluate the target tracking accuracy and LPI performance, respectively. It is shown that the JTRMWS problem is a nonlinear and nonconvex optimization problem, where the above four adaptable parameters are all coupled in the objective functions and constraints. Combined with the particle swarm optimization algorithm, an efficient and fast three-stage-based solution technique is developed to deal with the resulting problem. Simulation results are provided to verify the effectiveness and superiority of the proposed JTRMWS algorithm compared with other state-of-the-art benchmarks.

Neural network joint capacity-power control strategy based on NSGAII-BP for interference suppression in LEO satellite uplinks

Due to the long distance between satellites and the earth, uplink users need considerable energy to achieve satellite communication. Therefore, the energy efficiency is very important for user terminals. In addition, multibeam Low Earth Orbit (LEO) satellite networks may cause cochannel interference between beams because of full frequency reuse. Moreover, power competition among users will easily occur if the users begin to privately increase the transmission power. Power competition limits capacity and reduces energy efficiency. To solve these problems, this paper proposes a power control strategy balancing capacity and energy efficiency. A dual-objective optimization model is established based on the maximum transmission power constraint and the minimum signal-to-interference ratio (SIR) constraint. The model is solved by a back propagation (BP) neural network based on the fast nondominated sorting genetic algorithm (NSGAII). The neural network can adjust the optimization degree of the energy efficiency and capacity by preference factors. The simulation experiment results show that, while the user minimum transmission rate increases, the EE optimization of the NSGAII-BP neural network is better than that of the traditional weighting method.

Design and Multiobjective Optimization of Green Closed-Loop Manufacturing-Recycling Network Considering Raw Material Attribute

Regarding decision planning in the electronic manufacturing industry, this paper designs a green closed-loop manufacturing-recycling network for multiperiod production planning for multiple products. The network considers the tradeoff between production costs and environmental pollution induced by production scraps. Therefore, a mixed integer programming model with a dual objective is designed to achieve environmental protection and reduce production costs through resource recovery and utilization. At the same time, the recycled materials are considered to be treated, not entirely new, which could affect the manufacturing qualified rate. Thus, material attributes are proposed to distinguish new raw materials from recycled (second-hand) ones through the closed-loop manufacturing-recycling process to enhance the manufacturing qualified rate. In order to solve the dual-objective optimization model and realize optimal decisions, an epsilon constraint is designed to generate a nonextreme solution set by changing the original feasible region. The results show its ability to obtain a more balanced solution in terms of cost and environmental factors compared with the fuzzy-weighted method. Meanwhile, the analysis proves that the dual-objective optimization model with distinguishing material attributes can improve the efficiency of the manufacturing qualified rate and achieve a win-win situation for production and environmental protection during enterprise production.

Dual-Objective Optimization of Maximum Rail Potential and Total Energy Consumption in Multitrain Subway Systems

The dc traction power system is commonly adopted in multitrain subway systems. During the operation of multiple trains, different power distribution of the system directly affects the energy consumption and power supply safety. It is urgent for multitrain subway systems to optimize the operation parameters to ensure energy-saving and safety. In this article, nondominated sorting genetic algorithm II (NSGA-II) based on kernel density estimation (KDE) is proposed for collaborative optimization of total energy consumption and the maximum rail potential in the system. First, a simulation model for the dynamic operation of multiple trains with a parallel multiconductor traction network is established to realize the dynamic flow calculation with train–network coupling. Second, a dual-objective optimization model of the maximum rail potential and the total energy consumption is formulated to realize the energy-saving and power supply safety of the system. Finally, the NSGA-II algorithm based on KDE is proposed to handle the dual-objective optimization model. According to the dynamic simulations of Guangzhou Metro Line 2, the optimization method is verified. Results show that the total energy consumption is reduced by 22.2%, and the maximum rail potential is dropped by 40.3%, which effectively realizes the energy-saving and power supply safety.

Multi-Objective Optimization of Energy Saving and Throughput in Heterogeneous Networks Using Deep Reinforcement Learning.

Wireless networking using GHz or THz spectra has encouraged mobile service providers to deploy small cells to improve link quality and cell capacity using mmWave backhaul links. As green networking for less CO2 emission is mandatory to confront global climate change, we need energy efficient network management for such denser small-cell heterogeneous networks (HetNets) that already suffer from observable power consumption. We establish a dual-objective optimization model that minimizes energy consumption by switching off unused small cells while maximizing user throughput, which is a mixed integer linear problem (MILP). Recently, the deep reinforcement learning (DRL) algorithm has been applied to many NP-hard problems of the wireless networking field, such as radio resource allocation, association and power saving, which can induce a near-optimal solution with fast inference time as an online solution. In this paper, we investigate the feasibility of the DRL algorithm for a dual-objective problem, energy efficient routing and throughput maximization, which has not been explored before. We propose a proximal policy (PPO)-based multi-objective algorithm using the actor-critic model that is realized as an optimistic linear support framework in which the PPO algorithm searches for feasible solutions iteratively. Experimental results show that our algorithm can achieve throughput and energy savings comparable to the CPLEX.

Optimization of Heat Transfer Model Based on Greedy Algorithm

Aiming at the problem of furnace temperature curve, this paper uses Fourier’s law of heat conduction and Newton’s law of cooling to establish a heat transfer model to obtain the temperature changing law of the welding area and the furnace temperature curve; uses the single-objective optimization model to establish the maximum conveyor belt passing speed and find out the optimal temperature for each temperature zone; the greedy algorithm and traversal search are used to obtain the undetermined parameters of the dual-objective model through local optimization. In response to question 1, it is required to study the temperature change in the center of the welding area. First, we set the heat transfer model from heat conduction law and convection heat transfer law. The interface of the reflow furnace is two-dimensional: the conveyor belt direction is the x-axis and the vertical direction of the reflow furnace is the y-axis. The energy balance method is used to find the relationship between the x and y-axis heat conduction to establish a differential equation, after then we use Newton Cooling Law and the temperature distribution in the reflow furnace and establish the difference equation of the welding zone temperature. This paper uses the numerical calculation to solve the differential equation sets, obtaining the statistics of the temperatures at intervals of 0.5s. The theoretical furnace temperature curve is drawn as well. The second problem requires the maximum furnace passing speed of the conveyor belt after the temperature of each zone was determined and the constraints of the process limit must be met. A single-objective optimization model was established in this paper and the maximum conveyor belt speed was taken as the objective function. Without exceeding the constraints of the problem, the maximum conveyor belt passing speed was 92.6cm/min after the computer finished the programme. For problem three, it is required to find out the suitable temperature of each zone and the belt-speed, making the area minimized which is covered by the temperature curve exceeding 217°C and under the peak point. This paper establishes a single-objective optimization model, where the objective function is the minimum coverage area; the constraints are the same as the five constraints above in question 2. We use the programming software to search the feasible solution: the temperature of the preheating zone is 167 °C, the temperature of the constant temperature zone 6 is 185.1°C, the temperature of the constant temperature zone 7 is 225°C, the temperature of the reflow zone is 259.2°C, and the passing speed of the conveyor belt is 91.2cm/min. For question four, it is required to make the furnace temperature curve as symmetrical to the peak value as possible while considering the coverage area. This paper defines the symmetry coefficient to quantify the peak symmetry. On this basis, a dual-objective optimization model is first established, and then the coverage area in the objective is converted into constraint conditions, which is transformed into a single-objective optimization problem, and the unknown parameters are finally determined. The full traversal search needs to determine 5 parameters so the calculation is time-consuming. Therefore, we first use the greedy algorithm to solve some of the local optimal parameters, and then use the full traversal search to simplify the calculation. The final result: the temperature of the preheating zone is 169.5°C, the temperature of the constant temperature zone 6 is 185°C, the temperature of the constant temperature zone 7 is 225°C, the temperature of the reflow zone is 258.7°C, the furnace passing speed of the conveyor belt is 90.4cm/min. Finally, the model established in this paper is discussed and analyzed, the model established is comprehensively evaluated, and the conclusion is made.