Urban Oversaturated Traffic Network Control Based on Stability Preference Multi-Objective Compatible Optimization Control

2012 ◽  
Vol 151 ◽  
pp. 503-509
Author(s):  
Juan Chen ◽  
Qing Song Hu ◽  
Xiang Yang Sun

To solve the traffic congestion control problem on oversaturated network, the control problem is formulated as a conflicted multi-objective control problem., a new stability preference multi-objective compatible optimization control(SPMOCC) algorithm is proposed to solve the conflicted multi-objective control problem. In the proposed SPMOCC algorithm, NSGA-II algorithm is adjusted by proposing non-even Pareto front spread preserving strategy to obtain some special area on the Pareto front; a stability preference selection strategy is proposed to obtain stable controller. The proposed SPMOCC is used to solve the oversaturated traffic network control problem in a core area of 11 junctions under the simulation environment. It is proved that the proposed compatible optimization control algorithm can handle the oversaturated traffic network control problem effectively than the fixed time control method.

2011 ◽  
Vol 317-319 ◽  
pp. 1373-1384 ◽  
Author(s):  
Juan Chen ◽  
Chang Liang Yuan

To solve the traffic congestion control problem on oversaturated network, the total delay is classified into two parts: the feeding delay and the non-feeding delay, and the control problem is formulated as a conflicted multi-objective control problem. The simultaneous control of multiple objectives is different from single objective control in that there is no unique solution to multi-objective control problems(MOPs). Multi-objective control usually involves many conflicting and incompatible objectives, therefore, a set of optimal trade-off solutions known as the Pareto-optimal solutions is required. Based on this background, a modified compatible control algorithm(MOCC) hunting for suboptimal and feasible region as the control aim rather than precise optimal point is proposed in this paper to solve the conflicted oversaturated traffic network control problem. Since it is impossible to avoid the inaccurate system model and input disturbance, the controller of the proposed multi-objective compatible control strategy is designed based on feedback control structure. Besides, considering the difference between control problem and optimization problem, user's preference are incorporated into multi-objective compatible control algorithm to guide the search direction. The proposed preference based compatible optimization control algorithm(PMOCC) is used to solve the oversaturated traffic network control problem in a core area of eleven junctions under the simulation environment. It is proved that the proposed compatible optimization control algorithm can handle the oversaturated traffic network control problem effectively than the fixed time control method.


Author(s):  
Rui Zhu ◽  
Puyu Cao ◽  
Yang Wang ◽  
Chao Ning

Abstract Flow distortions occur at the outlet section of the intake duct owing to its shape properties, which is a component of water-jet propulsion. Since the noticeable influence of intake’s flow characteristics upon propulsive efficiency, it’s necessary to focus on intake duct redesign. In this paper, a systematic methodology for reducing flow distortions and power losses within the intake duct through a shape optimization process was obtained. In addition, the mechanism of flow distortions was also developed. The flush type inlet applied in the marine vessel with the speed of 30 knots was chosen as research project. Four characteristic parameters were set as optimization variables depending on the geometrical relationship of thirteen characteristic parameters referred to the duct longitudinal midsection, which were the ramp angle α, the radius of the upper lip R3, the radius of the lower lip R4 and the lip height h respectively. Subsequently, a sample space was built by Latin Hypercube Sampling (LHS) and the parameters were normalized in the range of 0 to 1. With the commercial software CFX, the numerical simulation was accomplished driven by SST k-ω turbulence model. Multi-objective optimization based on the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) was utilized to minimize the non-uniformity at outlet section and maximize the minimal pressure at lip simultaneously. Moreover, the Radial Basis Function (RBF) neural network was employed to approximate the functional relationship between variables and objectives, which could be applied in the NSGA-II to get the Pareto Front. The minimum non-uniformity point and the trade-off point (The point both satisfies the minimum non-uniformity and the maximum minimal pressure at lip strategically) were selected from the Pareto Front. With regard to the characteristic parameters of the trade-off point, the ramp angle, the radius of the upper lip, the radius of the lower lip and the lip height are 31.91°, 11.42 mm, 400.97 mm and 55.43 mm respectively. Meanwhile, the characteristic parameters of the minimum non-uniformity point are 30.22°, 25.59 mm, 166.65 mm and 89.90 mm respectively. Ultimately, the duct outflow characteristics of prototype and optimization are compared. In terms of the trade-off point, the minimal pressure at lip increases 66.40% to −24488.93 Pa and the non-uniformity has a drop of 4.56% to 0.1571. The non-uniformity of the minimum point is 0.1481 which is reduced by 10.02%. Through the optimization of duct shape, the secondary flow (Dean vortices) is suppressed effectively. This paper is expected to provide a better comprehension of the flow field within the intake duct of water-jet propulsion.


Algorithms ◽  
2019 ◽  
Vol 12 (10) ◽  
pp. 220 ◽  
Author(s):  
Juan Chen ◽  
Yuxuan Yu ◽  
Qi Guo

This paper proposes a model predictive control method based on dynamic multi-objective optimization algorithms (MPC_CPDMO-NSGA-II) for reducing freeway congestion and relieving environment impact simultaneously. A new dynamic multi-objective optimization algorithm based on clustering and prediction with NSGA-II (CPDMO-NSGA-II) is proposed. The proposed CPDMO-NSGA-II algorithm is used to realize on-line optimization at each control step in model predictive control. The performance indicators considered in model predictive control consists of total time spent, total travel distance, total emissions and total fuel consumption. Then TOPSIS method is adopted to select an optimal solution from Pareto front obtained from MPC_CPDMO-NSGA-II algorithm and is applied to the VISSIM environment. The control strategies are variable speed limit (VSL) and ramp metering (RM). In order to verify the performance of the proposed algorithm, the proposed algorithm is tested under the simulation environment originated from a real freeway network in Shanghai with one on-ramp. The result is compared with fixed speed limit strategy and single optimization method respectively. Simulation results show that it can effectively alleviate traffic congestion, reduce emissions and fuel consumption, as compared with fixed speed limit strategy and classical model predictive control method based on single optimization method.


2014 ◽  
Vol 945-949 ◽  
pp. 2241-2247
Author(s):  
De Gao Zhao ◽  
Qiang Li

This paper deals with application of Non-dominated Sorting Genetic Algorithm with elitism (NSGA-II) to solve multi-objective optimization problems of designing a vehicle-borne radar antenna pedestal. Five technical improvements are proposed due to the disadvantages of NSGA-II. They are as follow: (1) presenting a new method to calculate the fitness of individuals in population; (2) renewing the definition of crowding distance; (3) introducing a threshold for choosing elitist; (4) reducing some redundant sorting process; (5) developing a self-adaptive arithmetic cross and mutation probability. The modified algorithm can lead to better population diversity than the original NSGA-II. Simulation results prove rationality and validity of the modified NSGA-II. A uniformly distributed Pareto front can be obtained by using the modified NSGA-II. Finally, a multi-objective problem of designing a vehicle-borne radar antenna pedestal is settled with the modified algorithm.


2016 ◽  
Vol 23 (5) ◽  
pp. 782-793 ◽  
Author(s):  
Mansour Ataei ◽  
Ehsan Asadi ◽  
Avesta Goodarzi ◽  
Amir Khajepour ◽  
Mir Behrad Khamesee

This paper reports work on the optimization and performance evaluation of a hybrid electromagnetic suspension system equipped with a hybrid electromagnetic damper. The hybrid damper is configured to operate with hydraulic and electromagnetic components. The hydraulic component produces a large fail-safe baseline damping force, while the electromagnetic component adds energy regeneration and adaptability to the suspension. For analyzing the system, the electromagnetic component was modeled and integrated into a 2DOF quarter-car model. Three criteria were considered for evaluating the performance of the suspension system: ride comfort, road holding and regenerated power. Using the genetic algorithm multi-objective optimization (NSGA-II), the suspension design was optimized to improve the performance of the vehicle with respect to the selected criteria. The multi-objective optimization method provided a set of solutions called Pareto front in which all solutions are equally good and the selection of each one depends on conditions and needs. Among the given solutions in the Pareto front, a small number of cases, with different design purposes, were selected. The performances of the selected designs were compared with two reference systems: a conventional and a nonoptimized hybrid suspension system. The results show that the ride comfort and road holding qualities of the optimized hybrid system are improved, and the regenerated power is considerably increased.


2019 ◽  
Vol 34 (7) ◽  
pp. 708-715
Author(s):  
董晓庆 DONG Xiao-qing ◽  
程良伦 CHENG Liang-lun ◽  
陈洪财 CHEN Hong-cai ◽  
郑耿忠 ZHENG Geng-zhong ◽  
谢森林 XIE Sen-lin

Author(s):  
Zhongran Chi ◽  
Haiqing Liu ◽  
Shusheng Zang

This paper discusses the approach of cooling design optimization of a high-pressure turbine (HPT) endwall with applied 3D conjugate heat transfer (CHT) computational fluid dynamics (CFD). This study involved the optimization of the spacing of impingement jet array and the exit width of shaped holes, which are different for each cooling cavity. The optimization objectives were to reduce the wall-temperature level and to increase the aerodynamic performance. The optimization methodology consisted of an in-house parametric design and CFD mesh generation tool, a CHT CFD solver, a database of CFD results, a metamodel, and an algorithm for multi-objective optimization. The CFD tool was validated against experimental data of an endwall at CHT conditions. The metamodel, which could efficiently estimate the optimization objectives of new individuals without CFD runs, was developed and coupled with nondominated sorting genetic algorithm II (NSGA II) to accelerate the optimization process. Through the optimization search, the Pareto front of the problem was found in each iteration. The accuracy of metamodel with more iterations was improved by enriching database. But optimal designs found by the last iteration are almost identical with those of the first iteration. Through analyzing extra CFD results, it was demonstrated that the design variables in the Pareto front successfully reached the optimal values. The optimal pitches of impingement arrays could be decided accommodating the local thermal load while avoiding jet lift-off of film coolant. It was also suggested that cylindrical film holes near throat should be beneficial to both aerodynamic and cooling performances.


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