Research on Comprehensive Optimizing for Special Vehicle Handling and Riding Based on Multi-Objective Optimization Theory

2013 ◽  
Vol 467 ◽  
pp. 549-557
Author(s):  
Jun Li ◽  
Shi Hua Yuan ◽  
Ke Chen
Keyword(s):  
Solution Method ◽  
Optimization Theory ◽  
Multi Objective Optimization ◽  
Pareto Solutions ◽  
Pareto Solution ◽  
Vehicle Handling ◽  
Multi Objective ◽  
Optimization Parameters ◽  
Special Vehicle ◽  
Vehicle Dynamic Model

According to the comprehensive optimizing problem, a vehicle dynamic model based on whole vehicle parameters was established in this paper. Seven optimization targets which are typical means for the special vehicles handling and riding and the twelve optimization parameters of the suspension system were put forward through analysis. And the multi-objective optimization solving was completed by using the multi-objective optimization tool. The pareto solutions for comprehensive optimization were obtained. Through setting standard of the maximum scores of understeer degree and the minimum value of driver seat departments RMS of full domain weighted acceleration, the Pareto solution was obtained. It can provide a solution method for balance and coordination chassis performance of the special vehicle.

scholarly journals Search Acceleration of Evolutionary Multi-Objective Optimization Using an Estimated Convergence Point

Mathematics ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 129 ◽  
Author(s):  
Yan Pei ◽  
Jun Yu ◽  
Hideyuki Takagi
Keyword(s):  
Parameter Space ◽  
Statistical Tests ◽  
Distribution Characteristic ◽  
Multi Objective Optimization ◽  
Pareto Improvement ◽  
Pareto Solutions ◽  
Pareto Solution ◽  
Multi Objective ◽  
Objective Space ◽  
Convergence Point

We propose a method to accelerate evolutionary multi-objective optimization (EMO) search using an estimated convergence point. Pareto improvement from the last generation to the current generation supports information of promising Pareto solution areas in both an objective space and a parameter space. We use this information to construct a set of moving vectors and estimate a non-dominated Pareto point from these moving vectors. In this work, we attempt to use different methods for constructing moving vectors, and use the convergence point estimated by using the moving vectors to accelerate EMO search. From our evaluation results, we found that the landscape of Pareto improvement has a uni-modal distribution characteristic in an objective space, and has a multi-modal distribution characteristic in a parameter space. Our proposed method can enhance EMO search when the landscape of Pareto improvement has a uni-modal distribution characteristic in a parameter space, and by chance also does that when landscape of Pareto improvement has a multi-modal distribution characteristic in a parameter space. The proposed methods can not only obtain more Pareto solutions compared with the conventional non-dominant sorting genetic algorithm (NSGA)-II algorithm, but can also increase the diversity of Pareto solutions. This indicates that our proposed method can enhance the search capability of EMO in both Pareto dominance and solution diversity. We also found that the method of constructing moving vectors is a primary issue for the success of our proposed method. We analyze and discuss this method with several evaluation metrics and statistical tests. The proposed method has potential to enhance EMO embedding deterministic learning methods in stochastic optimization algorithms.

scholarly journals On the use of filters to facilitate the post-optimal analysis of the Pareto solutions in multi-objective optimization

2015 ◽  
Vol 74 ◽  
pp. 48-58 ◽  
Author(s):  
E. Antipova ◽  
C. Pozo ◽  
G. Guillén-Gosálbez ◽  
D. Boer ◽  
L.F. Cabeza ◽  
...  
Keyword(s):  
Multi Objective Optimization ◽  
Pareto Solutions ◽  
Multi Objective ◽  
Optimal Analysis

scholarly journals Multi-Objective Bee Swarm Optimization Algorithm with Minimum Manhattan Distance for Passive Power Filter Optimization Problems

Mathematics ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. 133
Author(s):  
Nien-Che Yang ◽  
Danish Mehmood
Keyword(s):  
Power Systems ◽  
Optimization Problems ◽  
Filter Design ◽  
Manhattan Distance ◽  
Multi Objective Optimization ◽  
Optimal Method ◽  
Pareto Solution ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Passive Power

Harmonic distortion in power systems is a significant problem, and it is thus necessary to mitigate critical harmonics. This study proposes an optimal method for designing passive power filters (PPFs) to suppress these harmonics. The design of a PPF involves multi-objective optimization. A multi-objective bee swarm optimization (MOBSO) with Pareto optimality is implemented, and an external archive is used to store the non-dominated solutions obtained. The minimum Manhattan distance strategy was used to select the most balanced solution in the Pareto solution set. A series of case studies are presented to demonstrate the efficiency and superiority of the proposed method. Therefore, the proposed method has a very promising future not only in filter design but also in solving other multi-objective optimization problems.

scholarly journals Walking with EMO

2011 ◽  
pp. 300-332
Author(s):  
Jason Teo ◽  
Lynnie D. Neri ◽  
Minh H. Nguyen ◽  
Hussein A. Abbass
Keyword(s):  
Legged Locomotion ◽  
Operational Performance ◽  
Multi Objective Optimization ◽  
Pareto Solutions ◽  
Uncertain Environments ◽  
Multi Objective ◽  
Limb Dynamics

This chapter will demonstrate the various robotics applications that can be achieved using evolutionary multi-objective optimization (EMO) techniques. The main objective of this chapter is to demonstrate practical ways of generating simple legged locomotion for simulated robots with two, four and six legs using EMO. The operational performance as well as complexities of the resulting evolved Pareto solutions that act as controllers for these robots will then be analyzed. Additionally, the operational dynamics of these evolved Pareto controllers in noisy and uncertain environments, limb dynamics and effects of using a different underlying EMO algorithm will also be discussed.

scholarly journals Study on application of MSOT method for lightweight design of automobile body structure

2020 ◽  
Vol 12 (10) ◽  
pp. 168781402096504
Author(s):  
Li Jixiong ◽  
Wang Daoyong
Keyword(s):  
Weight Reduction ◽  
Lightweight Design ◽  
The Body ◽  
Body Structure ◽  
Multi Objective Optimization ◽  
Pareto Solution ◽  
Multi Objective ◽  
Automobile Body ◽  
Collision Safety ◽  
Side Collision

In this study, the integrated MSOT (M-Multi-dimensional factor autobody model, S-Screening autobody component, O-Optimization of plate thickness, T-Testing, and validation) integration method is adopted to optimize the automobile body structure design for weight reduction. First, a multi-dimensional factor body model is established, then components of the vehicle are screened for the most important targets related to weight reduction and performance, and a multi-objective optimization is performed. Virtual experiments were carried out to validate the analysis and the MSOT method were proposed for lightweight design of the automobile body structure. A multi-dimensional performance model that considers stiffness, modality, strength, frontal offset collision, and side collision of a domestic passenger car body structure. Components affecting the weight of the vehicle were identified. Sheet metal thickness was selected as the main optimization target and a multi-objective optimization was carried out. Finally, simulations were performed on the body structure. The comprehensive performance, in terms of fatigue strength, frontal offset collision safety, and side collision safety, was verified using the optimized Pareto solution set. The results show that the established MSOT method can be used to comprehensively explore the weight reduction of the body structure, shorten the development process, and reduce development costs.

scholarly journals Solving the Asymmetry Multi-Objective Optimization Problem in PPPs under LPVR Mechanism by Bi-Level Programing

Symmetry ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 1667
Author(s):  
Feiran Liu ◽  
Jun Liu ◽  
Xuedong Yan
Keyword(s):  
Optimization Problem ◽  
Optimal Strategies ◽  
Decision Makers ◽  
Multi Objective Optimization ◽  
Pareto Solutions ◽  
Present Value ◽  
Public And Private ◽  
The Public ◽  
Multi Objective ◽  
The Cost

Optimizing the cost and benefit allocation among multiple players in a public-private partnership (PPP) project is recognized to be a multi-objective optimization problem (MOP). When the least present value of revenue (LPVR) mechanism is adopted in the competitive procurement of PPPs, the MOP presents asymmetry in objective levels, control variables and action orders. This paper characterizes this asymmetrical MOP in Stackelberg theory and builds a bi-level programing model to solve it in order to support the decision-making activities of both the public and private sectors in negotiation. An intuitive algorithm based on the non-dominated sorting genetic algorithm III (NSGA III) framework is designed to generate Pareto solutions that allow decision-makers to choose optimal strategies from their own criteria. The effectiveness of the model and algorithm is validated via a real case of a highway PPP project. The results reveal that the PPP project will be financially infeasible without the transfer of certain amounts of exterior benefits into supplementary income for the private sector. Besides, the strategy of transferring minimum exterior benefits is more beneficial to the public sector than to users.

Multi-Objective Optimization and Parameter Tuning for Turbine PID Controller

2013 ◽  
Vol 779-780 ◽  
pp. 971-976
Author(s):  
Yuan Sheng Lin ◽  
Yong Li ◽  
Fei Fei Song ◽  
Da Wei Teng
Keyword(s):  
Decision Making ◽  
Pid Controller ◽  
Optimization Technique ◽  
Disturbance Attenuation ◽  
Subsequent Stage ◽  
Multi Objective Optimization ◽  
Pareto Solution ◽  
Acceptable Solution ◽  
Multi Objective ◽  
Multi Attribute Decision Making

The tuning of PID controller parameters is the most important task in PID design process. A new tuning method is presented for PID parameters, based on multi-objective optimization technique and multi-attribute decision making method. Three performances of a PID controller, i.e. the accurate set point tracking, disturbance attenuation and robust stability are studied simultaneously. These specifications are usually competitive and any acceptable solution requires a tradeoff among them. A hybrid approaches is proposed. In the first stage, a Non-dominated Sorting Genetic Algorithm II (NSGA II) is employed to approximate the set of Pareto solution through an evolutionary optimization process. In the subsequent stage, a multi-attribute decision making (MADM) approach is adopted to rank these solutions from best to worst and to determine the best solution in a deterministic environment with a single decision maker. The ranking of Pareto solution is based on entropy weight and TOPSIS method. A turbine PID design example is conducted to illustrate the analysis process in present study. The effectiveness of this universal framework is supported by the simulation results.

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