A Multi-Satellite Regional Imaging Mission Planning Method Based on Moom for Emergency Surveying and Mapping

This paper aims to promote rural revitalization and support the long-term development of national agriculture by optimizing planting strategies, developing organic farming according to local conditions, and rationalizing the use of cultivated land. Based on the data related to cultivated land area in North China from 2017 to 2022, a multi-objective function optimization model is constructed, which comprehensively considers constraints such as land type, crop planting area limitations, crop continuous cropping effects, and legume crop rotation requirements. On this basis, the target-MOTAD model and importance sampling-Monte Carlo simulation are introduced to establish a comprehensive objective function for maximizing revenue and minimizing risk, which is solved by a hybrid genetic algorithm. Furthermore, complementarity coefficients and substitution coefficients are introduced to establish a market response model based on principal-agent game, which is dynamicized and solved by the NSGA-II algorithm, aiming to provide scientific decision support for agricultural producers and improve their revenue.

In this paper, a multi-objective optimization model is proposed to calculate best possible size of energy storage system (ESS). The proposed model is solved utilizing weighted sum method. Positive effects of demand response program (DRP) are considered in the proposed paper. By utilizing the weighted sum method, many various solutions are obtained. Then to select the best possible solution, fuzzy satisfying approach is employed. The proposed multi-objective model includes two conflicting objective functions: 1) the first objective function is minimization of microgrid investment cost as well as operation cost; 2) the second objective function is minimization of loss of load expectation (LOLE). Microgrid includes some local units inside itself which may have some unknown outages and also due to variable and unstable output of renewable units, utilization of ESS is essential to improve stability of microgrid. Impact of DRP implementation is evaluated on microgrid related costs and the results are compared to validate the proposed technique. In order to simulate and model the proposed stochastic ESS optimal sizing problem in a microgrid, a mixed-integer program (MIP) is utilized.

Establishment of data-driven multi-objective model to optimize drilling performance

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.

In order to control the discharge of regional total pollutants in the region and improve the ability of water environment management and decision making, a multi-objective decision-making optimization model of water pollution load allocation was constructed, which took into account economy and fairness. The model takes the maximum environmental benefit and the minimum weighted comprehensive Gini coefficient as the objective function and takes into account the uncertainty and multi-objectives of the model, which is conducive to promoting economic development and ensuring the fairness of regional water pollutant discharge. A method based on Monte Carlo simulation coupled with a genetic algorithm was designed to obtain the optimal solution set through multiple simulation optimization. This model is applied to Anhui Province to solve the allocation optimization problem of total pollutant reduction in the 13th Five-Year Energy Conservation and Emission Reduction Plan. After the optimization of water pollution load distribution, the comprehensive Gini coefficients of COD and NH3-N are reduced by different ranges. The comprehensive Gini coefficient after COD optimization decreased by 2.4–4.6%, and the comprehensive Gini coefficient after NH3-N optimization decreased by 25.1–32.5%, which verified the feasibility and rationality of the model in the optimal allocation of the total discharge of regional water pollutants. The model takes into account uncertain subjective and objective factors that have an important impact on water pollutant discharge targets and decision variables, thus optimizing the total emissions of the entire regional control unit in both space and time.

Microtrips are the key in driving cycle, and the base for constructing a driving cycle. For the construction of a modal driving cycle, the study firstly used principal component analysis to reduce the dimensions of the index data of microtrips, then a multi-objective optimization model was established with the goal of minimizing the deviation between the feature of the vehicle modal driving cycle and the actual vehicle operating feature. The non-dominated sorting genetic algorithm-II (NSGA-II) was designed to save the optimization model. Based on the correlation test of actual data, the designed method can obtain highly representative solutions.

In order to address the impact of reduced transmission stability and reliability caused by volume reduction on the quality of gear transmission, this article proposes a multi-objective optimization model for nonlinear dynamic load transmission errors of double helical gears. This study aims to introduce a multi-objective design method for gear transmission, using the volume and smooth reliability of helical gears as objective functions, and establish a multi-objective optimization design mathematical model for helical cylindrical gear transmission. In order to solve this multi-objective optimization problem, we utilized the optimization toolbox in the scientific calculation software MATLAB with examples. The results show that after the joint optimization design of volume and coincidence degree, it is calculated that the volume after the joint optimization design is still 2.2624 × 107 mm3, and the coincidence degree is 5.9908. After rounding, the design result is m n = 3 , Z 1 = 31 , β = 20 ∘ , Ψ d = 1.2 {m}_{{\rm{n}}}=3,{Z}_{1}=31,\beta ={20}^{\circ },{\Psi }_{{\rm{d}}}=1.2 . The optimization design results show that the joint optimization design with the minimum volume and the maximum coincidence as the objective function can reduce the volume and improve the output stability of the helical gear.

This paper uses the city of Juneau, Alaska as an example, To study sustainable tourism management based on multi-objective optimization model. The development of tourism as a core engine of regional economic growth has brought about imbalances in economic, ecological and social systems.The city of Juneau has seen a surge in tourism based on its unique natural beauty, but it also faces shrinking glaciers and infrastructure pressures.This paper constructs a multi-objective optimization model containing maximization of economic benefits, minimization of environmental costs and social costs, introduces the number of tourists and the number of daily running cruises as decision variables, and solves them by NSGA-II algorithm to get the solutions under different development strategies. The results show that there is a trade-off relationship between economic benefits and environmental and social costs. Accordingly, economic, environmental, and social strategies are proposed, including the development of diversified tourism methods, green tourism models, and protection of residents' interests, aiming to provide sustainable tourism management programs for Juneau City and similar destinations, balancing economic growth with environmental and social responsibilities.

Energy and water efficiency measures can offer great reduction in utility costs of large existing buildings. However, decision-makers are often faced with a challenging task to identify the most cost effective measures within available budgets due to wide variety of energy and water efficiency measures. This paper presents the development of a new model that is capable of identifying optimum trade-offs between (1) minimizing upgrade cost, and (2) minimizing utility cost of existing buildings simultaneously while maintaining building operational performance. To this end, a multi-objective model is developed in three main steps: (1) formulation step where decision variables, objective function, and constraints are identified and formulated; (2) implementation step that performs the model computations and specifies the model input and output data; and (3) performance evaluation step where a case study is analyzed to evaluate the performance of the model. The primary contributions that this research adds to existing are (1) development of a new optimization model that can identify optimal building upgrades to strike a balance between upgrade cost and utility cost, and (2) implementing the model computations using epsilon-constraint method and binary linear programming to guarantee the optimality of the generated solution in short computational time. The case study results illustrated that the developed model was able to identify pareto-optimal solutions of the two optimization objectives and optimal set of upgrade measures for each of the pareto-optimal solutions for a study period of 20 years that reduced utility costs up to 39.5%.

In order to optimize the local search efficiency of multi-objective parameters of flux switching permanent motor based on traditional NSGA-II algorithm, an improved NSGA-II (iNSGA-II) algorithm is proposed, with an anti-redundant mutation operator and forward comparison operation designed for quick identification of non-dominated individuals. In the initial stage of the iNSGA-II algorithm, half of the individual populations were randomly generated, while the other half was generated according to feature distribution information. Taking the flux switching permanent motor stator/rotor gap, permanent magnets width, stator tooth width, rotor tooth width and other parameters as optimization variables, the flux switching permanent motor maximum output shaft torque and minimum torque ripple are taken as optimization objectives, thus a multi-objective optimization model is established. Real number coding was adopted for obtaining the Pareto optimal solution of flux switching permanent motor structure parameters. The results showed that the iNSGA-II algorithm is better than the traditional NSGA-II on convergence. A 1.8L TOYOTA PRIUS model was selected as the prototype vehicle. By using the optimized parameters, a joint optimization simulation model was established by calling ADVISOR’s back-office function. The simulation results showed that the entire vehicle’s 100-km acceleration time is under 8 s and the battery’s SOC value maintains at 0.5–0.7 in the entire cycle, implying that the iNSGA-II algorithm optimizes the flux switching permanent motor design and is suitable for the initial design and optimizing calculation of the flux switching permanent motor.

ABSTRACTIn this paper, a multiobjective mixed-integer piecewise nonlinear programming model (MOMIPNLP) is built to formulate the management problem of urban mining system, where the decision variables are associated with buy-back pricing, choices of sites, transportation planning, and adjustment of production capacity. Different from the existing approaches, the social negative effect, generated from structural optimization of the recycling system, is minimized in our model, as well as the total recycling profit and utility from environmental improvement are jointly maximized. For solving the problem, the MOMIPNLP model is first transformed into an ordinary mixed-integer nonlinear programming model by variable substitution such that the piecewise feature of the model is removed. Then, based on technique of orthogonal design, a hybrid heuristic algorithm is developed to find an approximate Pareto-optimal solution, where genetic algorithm is used to optimize the structure of search neighborhood, and both local branching algorithm and relaxation-induced neighborhood search algorithm are employed to cut the searching branches and reduce the number of variables in each branch. Numerical experiments indicate that this algorithm spends less CPU (central processing unit) time in solving large-scale regional urban mining management problems, especially in comparison with the similar ones available in literature. By case study and sensitivity analysis, a number of practical managerial implications are revealed from the model.Implications: Since the metal stocks in society are reliable overground mineral sources, urban mining has been paid great attention as emerging strategic resources in an era of resource shortage. By mathematical modeling and development of efficient algorithms, this paper provides decision makers with useful suggestions on the optimal design of recycling system in urban mining. For example, this paper can answer how to encourage enterprises to join the recycling activities by government’s support and subsidies, whether the existing recycling system can meet the developmental requirements or not, and what is a reasonable adjustment of production capacity.

Multi-objective optimization of rectangular microchannel heat sink based on entropy generation and hydro-thermal performance using NSGA-II algorithm

Aiming at the problem that drilling parameters are difficult to adjust in time for the driller due to the complex geological environment in underground coal mines, a drilling parameter control method based on online identification of drillability and multi-objective optimization of drilling parameters is proposed. A drillability grade identification model is established, with rotational speed and torque as input parameters, which can accurately identify the current drilling state. A multi-objective optimization model of the optimal drilling parameters is established with the mechanical specific energy and drilling speed prediction model as the objective functions, and the NSGA-II algorithm and TOPSIS algorithm are used for solutions and decision-making. A fuzzy PID controller is established. For the control of rotational speed parameters and drilling pressure parameters, the advantages and disadvantages of the fuzzy PID control method and the traditional PID control method are compared through simulation and experiments. A control method based on the drillability identification model and the multi-objective optimization model is established. According to the different drillability grades, the drilling parameters are adjusted in time to ensure the normal drilling state. By constantly approaching the optimal parameters through the drilling parameters, the drilling efficiency is improved. Through experimental verification, this control method effectively prevents the occurrence of drilling speed reduction and intermittent sticking and can adjust the drilling parameters to continuously optimize.

This research is intended to develop a suspension parameter optimization approach based on a virtual prototype surrogate model of rail vehicles considering the coupling effects of suspension parameters. In order to analyze the effects on the dynamic indexes, which were affected by the suspension parameters, a virtual prototype model of a rail vehicle was established. The indexes of lateral ride quality and motion stability were obtained under different combinations of suspension parameters by design of experiment and simulation of virtual prototype. For constructing objective function of multi-objective optimization model for suspension parameters, the suspension parameters that have significant effects on ride quality and motion stability simultaneously were taken as the design variables, and thereafter Kriging models of lateral ride quality index, derailment coefficient, and reduction ratio of wheel load were obtained. On this basis, the multi-objective optimization model of suspension parameters was established, in which the objective function was combined with the three Kriging models. Then, the Pareto optimal solution set and concrete value of suspension parameters were sought using the NSGA-II algorithm. The dynamic simulation results indicated that both ride quality and motion stability of the rail vehicle had been improved after the multi-objective optimization of suspension parameters.

In this paper, a multi-objective model is proposed to address the dynamic cellular manufacturing (DCM) formation problem. This model considers four conflicting objectives: relocation cost, machine utilization, material handling cost and maintenance cost. The model also considers the situation that some machines could be shared by more than one cell at the same period. A genetic algorithm is applied to get the solution of this mathematical model. Three numerical examples are simulated to evaluate the validity of this model.