Multi-objective Function Optimization for Environmental Control of a Greenhouse Based on a RBF and NSGA-Ⅱ

To better meet the needs of crop growth and achieve energy savings and efficiency enhancements,constructing a reliable environmental model to optimize greenhouse decision parameters is an important problem to be solved.In this work,a radial-basis function(RBF)neural network was used to mine the potential changes of a greenhouse environment,a temperature error model was established,a multi-objective optimization function of energy consumption was constructed and the corresponding decision parameters were optimized by using a non-dominated sorting genetic algorithm with an elite strategy(NSGA-Ⅱ).The simulation results showed that RBF could clarify the nonlinear relationship among the greenhouse environment variables and decision parameters and the greenhouse temperature.The NSGA-Ⅱ could well search for the Pareto solution for the objective functions.The experimental results showed that after 40 min of combined control of sunshades and sprays,the temperature was reduced from 31℃to 25℃,and the power consumption was 0.5 MJ.Compared with tire three days of July 24,July 25 and July 26,2017,the energy consumption of the controlled production greenhouse was reduced by 37.5%,9.1%and 28.5%,respectively.

Multi-objective capacity allocation optimization method of photovoltaic EV charging station considering V2G

Large-scale electric vehicles(EVs) connected to the micro grid would cause many problems. In this paper, with the consideration of vehicle to grid(V2 G), two charging and discharging load modes of EVs were constructed. One was the disorderly charging and discharging mode based on travel habits, and the other was the orderly charging and discharging mode based on time-of-use(TOU) price;Monte Carlo method was used to verify the case. The scheme of the capacity optimization of photovoltaic charging station under two different charging and discharging modes with V2 G was proposed. The mathematical models of the objective function with the maximization of energy efficiency, the minimization of the investment and the operation cost of the charging system were established. The range of decision variables, constraints of the requirements of the power balance and the strategy of energy exchange were given. NSGA-Ⅱ and NSGA-SA algorithm were used to verify the cases, respectively. In both algorithms, by comparing with the simulation results of the two different modes, it shows that the orderly charging and discharging mode with V2 G is obviously better than the disorderly charging and discharging mode in the aspects of alleviating the pressure of power grid, reducing system investment and improving energy efficiency.

Multi-objective Optimization of Industrial Purified Terephthalic Acid Oxidation Process

Multi-objective optimization of a purified terephthalic acid (PTA) oxidation unit is carried out in this paper by using a process modei that has been proved to describe industrial process quite well. The modei is a semi-empirical structured into two series ideal continuously stirred tank reactor (CSTR) models. The optimal objectives include maximizing the yield or inlet rate and minimizing the concentration of 4-carboxy-benzaldhyde, which is the main undesirable intermediate product in the reaction process. The multi-objective optimization algorithra applied in this study is non-dominated sorting genetic algorithm Ⅱ (NSGA-Ⅱ). The performance of NSGA-Ⅱ is further illustrated by application to the title process.

Multi-objective optimization of methane production system from biomass through anaerobic digestion

This work addressed the multi-objective optimization of a biogas production system considering both environmental and economic criteria. A mixed integer non-linear programming(MINLP) model was established and solved with non-dominated sorting genetic algorithm Ⅱ, from which the Pareto fronts, the optimal technology combinations and operation conditions were obtained and analyzed. It's found that the system is feasible in both environmental and economic considerations after optimization. The most expensive processing section is decarbonization; the most expensive equipment is anaerobic digester; the most power-consuming processing section is digestion, followed by decarbonization and waste management. The positive green degree value on the process is attributed to processing section of digestion and waste management. 3:1 chicken feces and corn straw, solar energy, pressure swing adsorption and 3:1 chicken feces and rice straw, solar energy, pressure swing adsorption are turned out to be two robust technology combinations under different prices of methane and electricity by sensitivity analysis. The optimization results provide support for optimal design and operation of biogas production system considering environmental and economic objectives.

Multi-objective optimization design for leg mechanism of hydraulic-actuated quadruped robot

In order to improve the robot＇ s abilities of bearing heavy burdens and transporting in complex terrains, the multi-objective optimization design for leg mechanism of the quadruped robot with hydraulic actuated is studied in this paper. The kinematics and dynamics of the robot are ana- lyzed and the two-dimensional linear inverted pendulum model is adopted in planning the trajectories of joints. Then the mathematical model of valve-controlled asymmetric cylinder and control model of single leg are proposed respectively. In the end, NSGA-Ⅱ algorithm is used to achieve the multi^ob- jective optimization design of parameters concerning single leg mechanism and PD torque control. The results prove that the optimized leg mechanism can significantly reduce the required maximum power of hydraulic system, thus decrease its own weight and lead to the obtaining of good dynamic performance.

Models for Location Inventory Routing Problem of Cold Chain Logistics with NSGA-Ⅱ Algorithm

In this paper,a novel location inventory routing(LIR)model is proposed to solve cold chain logistics network problem under uncertain demand environment. The goal of the developed model is to optimize costs of location,inventory and transportation.Due to the complex of LIR problem( LIRP), a multi-objective genetic algorithm(GA), non-dominated sorting in genetic algorithm Ⅱ( NSGA-Ⅱ) has been introduced. Its performance is tested over a real case for the proposed problems. Results indicate that NSGA-Ⅱ provides a competitive performance than GA,which demonstrates that the proposed model and multi-objective GA are considerably efficient to solve the problem.

Optimization of Cutting Parameters for Trade-off Among Carbon Emissions, Surface Roughness, and Processing Time

As the manufacturing industry is facing increasingly serious environmental problems, because of which carbon tax policies are being implemented, choosing the optimum cutting parameters during the machining process is crucial for automobile panel dies in order to achieve synergistic minimization of the environment impact, product quality, and processing efficiency. This paper presents a processing task-based evaluation method to optimize the cutting parameters, considering the trade-off among carbon emissions, surface roughness, and processing time. Three objective models and their relationships with the cutting parameters were obtained through input–output, response surface, and theoretical analyses, respectively. Examples of cylindrical turning were applied to achieve a central composite design(CCD), and relative validation experiments were applied to evaluate the proposed method. The experiments were conducted on the CAK50135 di lathe cutting of AISI 1045 steel, and NSGA-Ⅱ was used to obtain the Pareto fronts of the three objectives. Based on the TOPSIS method, the Pareto solution set was ranked to find the optimal solution to evaluate and select the optimal cutting parameters. An S/N ratio analysis and contour plots were applied to analyze the influence of each decision variable on the optimization objective. Finally, the changing rules of a single factor for each objective were analyzed. The results demonstrate that the proposed method is effective in finding the trade-off among the three objectives and obtaining reasonable application ranges of the cutting parameters from Pareto fronts.

Multi-objective Dimensional Optimization of a 3-DOF Translational PKM Considering Transmission Properties

Multi-objective dimensional optimization of parallel kinematic manipulators(PKMs) remains a challenging and worthwhile research endeavor. This paper presents a straightforward and systematic methodology for implementing the structure optimization analysis of a 3-prismatic-universal-universal(PUU) PKM when simultaneously considering motion transmission, velocity transmission and acceleration transmission. Firstly, inspired by a planar four-bar linkage mechanism, the motion transmission index of the spatial parallel manipulator is based on transmission angle which is defined as the pressure angle amongst limbs. Then, the velocity transmission index and acceleration transmission index are derived through the corresponding kinematics model. The multi-objective dimensional optimization under specific constraints is carried out by the improved non-dominated sorting genetic algorithm(NSGA Ⅱ), resulting in a set of Pareto optimal solutions. The final chosen solution shows that the manipulator with the optimized structure parameters can provide excellent motion, velocity and acceleration transmission properties.

汽车磁流变阻尼器多目标优化分析（英文）

为了改善汽车的被动安全性能,提出将一种单杆单筒式磁流变阻尼器应用于汽车前部吸能结构中。提出以修正Bingham塑性模型(BPM模型)为理论基础,以最大阻尼力和可调范围为优化目标,运用mode FRONTIER自带的非支配排序遗传算法(NSGA-II)对所采用的磁流变阻尼器结构参数进行多目标优化分析。优化结果表明:最大阻尼力和可调范围成反比,所用的优化算法不可能使两个优化目标同时达到最优,只能在众多前沿解中选择符合条件的优化解。优化后的磁流变阻力器磁场分布更加集中合理。

Multiobjective car relocation problem in one-way carsharingsystem

In this paper, we present a multiobjective approach for solving the one-way car relocation problem.We fix three objectives that include the number of remaining rejected demands, the number of jockeys used for the relocation operations, and the total time used by these jockeys. For this sake, we propose to apply two algorithms namely NSGA-Ⅱ and an adapted memetic algorithm(MA) that we call MARPOCS which stands for memetic algorithm for the one-way carsharing system. The NSGA-Ⅱ is used as a reference to compare the performance of MARPOCS. The comparison of the approximation sets obtained by both algorithms shows that the hybrid algorithm outperforms the classical NSGA-Ⅱ and so solutions generated by the MARPOCS are much better than the solutions generated by NSGA-Ⅱ. This observation is proved by the comparison of different quality indicators’ values that are used to compare the performance of each algorithm. Results show that the MARPOCS is promising to generate very good solutions for the multiobjective car relocation problem in one-way carsharing system. It shows a good performance in exploring the search space and in finding solution with very good fitness values.

A comparative study on using meta-heuristic algorithms for road maintenance planning:Insights from field study in a developing country

Optimized road maintenance planning seeks for solutions that can minimize the life-cycle cost of a road network and concurrently maximize pavement condition. Aiming at pro- posing an optimal set of road maintenance solutions, robust meta-heuristic algorithms are used in research. Two main optimization techniques are applied including single-objective and multi-objective optimization. Genetic algorithms （GA）, particle swarm optimization （PSO）, and combination of genetic algorithm and particle swarm optimization （GAPSO） as single-objective techniques are used, while the non-domination sorting genetic algorithm II （NSGAII） and multi-objective particle swarm optimization （MOPSO） which are sufficient for solving computationally complex large-size optimization problems as multi-objective techniques are applied and compared. A real case study from the rural transportation network of Iran is employed to illustrate the sufficiency of the optimum algorithm. The formulation of the optimization model is carried out in such a way that a cost-effective maintenance strategy is reached by preserving the performance level of the road network at a desirable level. So, the objective functions are pavement performance maximization and maintenance cost minimization. It is concluded that multi-objective algorithms including non-domination sorting genetic algorithm II （NSGAII） and multi-objective particle swarm optimization performed better than the single objective algorithms due to the capability to balance between both objectives. And between multi-objective algorithms the NSGAII provides the optimum solution for the road maintenance planning.

Multilevel coverage location model of earthquake relief material storage repository considering distribution time sequence characteristics

The scientific location of earthquake emergency supply warehouses is conducive to the effective distribution of emergency relief resources and improved rescue efficiency in earthquake hazard. Comprehensively considering the regional population as well as coverage quality at the demand points, this paper aims to divide the coverage thresholds of earthquake emergency rescue and logistic supplies according to their time-series features,and to build a location model for supply warehouses according to the variety and amount of stored supplies considering their time-series features, in hope of optimizing the set covering issue of earthquake relief supply warehouses. The solution is approached with two methods: the target deviation rate minimization model and NSGA-Ⅱ algorithm. The results obtained by solving the target deviation rate minimization model can balance every target. The branch and bound algorithm can find the global optimal solution at a certain calculation scale with high calculation efficiency, but its efficiency decreases significantly when the operation scale increases. The NSGA-Ⅱ algorithm is more suitable for large-scale solution calculations with high calculation efficiency, and it can output a set of non-inferior solutions for decision makers to select from according to different preference. Taking Aba Prefecture in Sichuan Province as illustration, the feasibility of the model is validated;meanwhile, the effectiveness and benefits of the two approaches in solving the problem of multi-objective set covering of the warehouses are compared and analyzed.