scholarly journals Bi-Objective Optimization Method and Application of Mechanism Design Based on Pigs' Payoff Game Behavior

2012 ◽  
Vol 2012 ◽  
pp. 1-15
Author(s):  
Lu Wang ◽  
Jian-gang Wang ◽  
Rui Meng ◽  
Neng-gang Xie
Keyword(s):  
Optimization Problems ◽  
Optimization Method ◽  
Payoff Function ◽  
Strategy Space ◽  
Design Variables ◽  
Game Player ◽  
Payoff Functions ◽  
Game Players ◽  
Behavior Characteristics ◽  
The Impact

It takes two design goals as different game players and design variables are divided into strategy spaces owned by corresponding game player by calculating the impact factor and fuzzy clustering. By the analysis of behavior characteristics of two kinds of intelligent pigs, the big pig's behavior is cooperative and collective, but the small pig's behavior is noncooperative, which are endowed with corresponding game player. Two game players establish the mapping relationship between game players payoff functions and objective functions. In their own strategy space, each game player takes their payoff function as monoobjective for optimization. It gives the best strategy upon other players. All the best strategies are combined to be a game strategy set. With convergence and multiround game, the final game solution is obtained. Taking bi-objective optimization of luffing mechanism of compensative shave block, for example, the results show that the method can effectively solve bi-objective optimization problems with preferred target and the efficiency and accuracy are also well.

scholarly journals Multiobjective Game Method Based on Self-Adaptive Space Division of Design Variables and Its Application to Vehicle Suspension

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Rui Meng ◽  
Nenggang Xie ◽  
Lu Wang
Keyword(s):  
Multiobjective Optimization ◽  
Iteration Process ◽  
Optimization Method ◽  
Payoff Function ◽  
Impact Factors ◽  
Vehicle Suspension ◽  
Space Division ◽  
Design Variables ◽  
Game Players ◽  
Self Adaptive

Based on the similarity between the game theory and the multiobjective design, the bionic mapping and the space mapping are established between the multiobjective optimization model and game model. Then, the multiobjective optimization method based on self-adaptive space division of design variables is proposed. The design variables are divided into multiple strategy subspaces and are assigned to corresponding game players by calculating impact factors,K-means clustering, and correlation analysis. Strategy subspaces of game players are dynamically adjusted in the iteration process. In their own strategy subspaces, each game player takes their payoff function (the mapping of objective function) as monoobjective optimization. It gives the best strategy upon other players. And the best strategies of all players are combined into the group strategy in this game round. Triobjective optimization is carried out for vehicle suspension in this method and it is compared with the traditional game method. The results show that this method has better calculating automaticity and can effectively promote generalization of multiobjective game method and improve the computational efficiency and precision.

scholarly journals Application of evolutionary algorithms to optimize cooling channels

2019 ◽  
Vol 10 ◽  
pp. A4 ◽  
Author(s):  
Narasimha R. Nagaiah ◽  
Christopher D. Geiger
Keyword(s):  
Design Optimization ◽  
Optimization Problems ◽  
Simulation Method ◽  
Optimization Method ◽  
Composite Function ◽  
Cooling Channels ◽  
Design Variables ◽  
Evolutionary Simulation ◽  
Conventional Methods ◽  
Cooling Passage

The design and development is a complex, repetitive, and more often difficult task, as design tasks comprising of restraining and conflicting relationships among design variables with more than one design objectives. Conventional methods for solving more than one objective optimization problems is to build one composite function by scalarizing the multiple objective functions into a single objective function with one solution. But, the disadvantages of conventional methods inspired scientists and engineers to look for different methods that result in more than one design solutions, also known as Pareto optimal solutions instead of one single solution. Furthermore, these methods not only involved in the optimization of more than one objectives concurrently but also optimize the objectives which are conflicting in nature, where optimizing one or more objective affects the outcome of other objectives negatively. This study demonstrates a nature-based and bio-inspired evolutionary simulation method that addresses the disadvantages of current methods in the application of design optimization. As an example, in this research, we chose to optimize the periodic segment of the cooling passage of an industrial gas turbine blade comprising of ribs (also known as turbulators) to enhance the cooling effectiveness. The outlined design optimization method provides a set of tradeoff designs to pick from depending on designer requirements.

Optimization Method Based on a Mix Strategy

2013 ◽  
Vol 816-817 ◽  
pp. 1154-1157
Author(s):  
Xu Yin ◽  
Ai Min Ji
Keyword(s):  
Optimization Problems ◽  
Optimal Solution ◽  
Optimization Method ◽  
Local Solution ◽  
Collaborative Optimization ◽  
Design Variables ◽  
Gradient Optimization ◽  
Local Optimal Solution ◽  
Optimal Values ◽  
Low Efficiency

To solve problems that exist in optimal design such as falling into local optimal solution easily and low efficiency in collaborative optimization, a new mix strategy optimization method combined design of experiments (DOE) with gradient optimization (GO) was proposed. In order to reduce the effect on the result of optimization made by the designers decision, DOE for preliminary analysis of the function model was used, and the optimal values obtained in DOE stage was taken as the initial values of design variables in GO stage in the new optimization method. The reducer MDO problem was taken as a example to confirm the global degree, efficiency, and accuracy of the method. The results show the optimization method could not only avoid falling into local solution, but also have an obvious superiority in treating the complex collaborative optimization problems.

scholarly journals Size and Topology Optimization for Trusses with Discrete Design Variables by Improved Firefly Algorithm

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Yue Wu ◽  
Qingpeng Li ◽  
Qingjie Hu ◽  
Andrew Borgart
Keyword(s):  
Topology Optimization ◽  
Firefly Algorithm ◽  
Optimization Problems ◽  
Optimization Method ◽  
Structural Topology ◽  
Discrete Variables ◽  
And Topology ◽  
Design Variables ◽  
Improved Firefly Algorithm ◽  
Discrete Design Variables

Firefly Algorithm (FA, for short) is inspired by the social behavior of fireflies and their phenomenon of bioluminescent communication. Based on the fundamentals of FA, two improved strategies are proposed to conduct size and topology optimization for trusses with discrete design variables. Firstly, development of structural topology optimization method and the basic principle of standard FA are introduced in detail. Then, in order to apply the algorithm to optimization problems with discrete variables, the initial positions of fireflies and the position updating formula are discretized. By embedding the random-weight and enhancing the attractiveness, the performance of this algorithm is improved, and thus an Improved Firefly Algorithm (IFA, for short) is proposed. Furthermore, using size variables which are capable of including topology variables and size and topology optimization for trusses with discrete variables is formulated based on the Ground Structure Approach. The essential techniques of variable elastic modulus technology and geometric construction analysis are applied in the structural analysis process. Subsequently, an optimization method for the size and topological design of trusses based on the IFA is introduced. Finally, two numerical examples are shown to verify the feasibility and efficiency of the proposed method by comparing with different deterministic methods.

Study on Vibration Reduction Design of Steel-Composite Material Hybrid Mounting for Ship Based on Material Selection Optimization

2013 ◽  
Vol 694-697 ◽  
pp. 415-424
Author(s):  
Wei Wang ◽  
Lu Yun Chen ◽  
Yu Fang Zhang
Keyword(s):  
Power Flow ◽  
Optimization Problems ◽  
Material Selection ◽  
Vibration Reduction ◽  
Optimization Method ◽  
Optimization Approach ◽  
Structural Dynamic ◽  
Level Difference ◽  
Design Variables ◽  
Steel Composite

The material selection optimization for vibration reduction design is studied present article. By introducing the stacking sequence hypothesis of metal material, taking into account the power flow level difference and vibration level difference parameter, the mechanical parameters of the material and plies number are defined as design variables, and the mathematical model of structural dynamic optimization based on material selection optimization approach is established. Finally, a naval hybrid steel-composite mounting structure for example, by introducing genetic algorithm, the optimization problems is solved. The numerical results show that the optimization method is effective and feasible.

scholarly journals Remarks on optimization of impact damping for a non-ideal and nonlinear structural system

2020 ◽  
pp. 146134842094007
Author(s):  
Marcelo A Silva ◽  
Alexandre M Wahrhaftig ◽  
Reyolando MLRF Brasil
Keyword(s):  
Dynamic Response ◽  
Optimization Problems ◽  
Augmented Lagrangian Method ◽  
Equations Of Motion ◽  
Maximum Amplitude ◽  
Power Source ◽  
Structural System ◽  
Impact Damper ◽  
Design Variables ◽  
The Impact

It is intended, in this work, to present some research results on the optimization of an impact damper for a structural system excited by a non-ideal power source. In the model, the impact vibration absorber is, basically, a small free mass inside a box carved in the structure that undergoes undamped linear motions colliding against the walls of the box. Whenever the mass shocks against the walls of the box, an exchange of kinetic energy between the mass and the structure may be used to control the amplitude of the dynamic response of the structure. In this work, the structure is excited by a non-ideal power source, a DC electric motor installed on it, which may present the Sommerfeld effect. A non-ideal power source is one that interacts with the motion of the structure as opposed to an ideal source whose amplitude and frequency are fixed, independent of the displacements of the structure. Here, the dynamic response of the system is computed using step-by-step numerical integration of the equations of motion derived via a Lagrangian formulation. The optimization problem is defined considering as the objective function the maximum amplitude of the structure displacement, while the design variables are the weight of the free mass and the width of the carved box. Using the augmented Lagrangian method, several optimization problems are formulated, and, solving them, the best design to maximize the efficiency of the impact damper is obtained.

Vibration Isolator Design for Space Application Based on Multiobjective Optimization Method

2013 ◽  
Vol 365-366 ◽  
pp. 77-81
Author(s):  
Zhi Wei Feng ◽  
Qian Gang Tang ◽  
Qing Bin Zhang
Keyword(s):  
Multiobjective Optimization ◽  
High Frequency ◽  
Optimization Problems ◽  
Optimization Method ◽  
Resonant Peak ◽  
Space Application ◽  
Vibration Isolator ◽  
Peak Reduction ◽  
Space Applications ◽  
Design Variables

A multiobjective optimization based vibration isolator design for space application is described. It is common to use passive isolator and isolate the platform noise in space applications. The design of a passive isolator involves a trade-off between the resonant peak reduction and the high frequency attenuation. The equation of motion and transfer function model for single-stage and two-stage connector model is derived by using basic principle. The multiobjective optimization model is proposed, where the design variables are the damping coefficients and stiffness coefficients, the objective functions are the resonant peak reduction and the high frequency attenuation, and the constraints are the natural frequency of the connector. The multiobjective optimization problems for the design of the passive isolator are solved by using the multiobjective evolutionary algorithm based on decomposition (MOEA/D). The Pareto front obtained can provide multiple candidate solutions for the designer. The method is effective for the design process of the passive isolator.

scholarly journals A Novel Heat Exchanger Design Method Using a Delayed Rejection Adaptive Metropolis Hasting Algorithm

2018 ◽  
Author(s):  
Javier Bonilla
Keyword(s):  
Heat Exchanger ◽  
Probability Distribution ◽  
Optimization Methods ◽  
Optimization Method ◽  
Total Annual Cost ◽  
Tube Heat Exchanger ◽  
Design Variables ◽  
Shell And Tube ◽  
Delayed Rejection ◽  
The Impact

In this study, a shell-and-tube heat exchanger (STHX) design based on seven continuous independent design variables is proposed. Delayed Rejection Adaptive Metropolis hasting (DRAM) was utilized as a powerful tool in the Markov chain Monte Carlo (MCMC) sampling method. This Reverse Sampling (RS) method was used to find the probability distribution of design variables of the shell and tube heat exchanger. Thanks to this probability distribution, an uncertainty analysis was also performed to find the quality of these variables. In addition, a decision-making strategy based on confidence intervals of design variables and on the Total Annual Cost (TAC) provides the final selection of design variables. Results indicated high accuracies for the estimation of design variables which leads to marginally improved performance compared to commonly used optimization methods. In order to verify the capability of the proposed method, a case of study is also presented, it shows that a significant cost reduction is feasible with respect to multi-objective and single-objective optimization methods. Furthermore, the selected variables have good quality (in terms of probability distribution) and a lower TAC was also achieved. Results show that the costs of the proposed design are lower than those obtained from optimization method reported in previous studies. The algorithm was also used to determine the impact of using probability values for the design variables rather than single values to obtain the best heat transfer area and pumping power. In particular, a reduction of the TAC up to 3.5% was achieved in the case considered.

Product Optimization Incorporating Discrete Design Variables Based on Decomposition of Performance Characteristics

2009 ◽  
Vol 131 (3) ◽  
Author(s):  
Masataka Yoshimura ◽  
Yu Yoshimura ◽  
Kazuhiro Izui ◽  
Shinji Nishiwaki
Keyword(s):  
Mathematical Programming ◽  
Optimization Problems ◽  
System Optimization ◽  
Optimization Method ◽  
Hierarchical Optimization ◽  
Standard Material ◽  
Design Variables ◽  
Hierarchical Levels ◽  
Discrete Design Variables ◽  
Product Optimization

This paper proposes a system optimization method for product designs incorporating discrete design variables, in which hierarchical product optimization methodologies are constructed based on decomposition of characteristics and/or extraction of simpler characteristics from original characteristics. The method is constructed to take advantage of hierarchical optimization procedures, enabling the incorporation of discrete design variables. The proposed method can be applied to machine product designs that include discrete design variables such as material types, machining methods, standard material forms, and specifications. The optimizations begin at the lowest levels of the hierarchical optimization structure and proceed to the higher levels. Discrete design variables are efficiently selected and optimized in the form of small suboptimization problems at the lowest hierarchical levels, and optimum solutions for the entire problem are ultimately obtained using conventional mathematical programming methods. Practical optimization procedures for machine product optimization problems that include several types of discrete design variables are constructed, and applied examples are provided to demonstrate their effectiveness.

scholarly journals Optimization of Hybrid Energy Storage Systems for Vehicles with Dynamic On-Off Power Loads Using a Nested Formulation

Energies ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 2699 ◽  
Author(s):  
Jiajun Liu ◽  
Huachao Dong ◽  
Tianxu Jin ◽  
Li Liu ◽  
Babak Manouchehrinia ◽  
...  
Keyword(s):  
Global Optimization ◽  
Energy Storage ◽  
Design Optimization ◽  
Optimization Problems ◽  
Optimization Method ◽  
Energy Management Strategy ◽  
Hybrid Energy ◽  
Component Size ◽  
Hybrid Energy Storage ◽  
The Impact

In this paper, identification of an appropriate hybrid energy storage system (HESS) architecture, introduction of a comprehensive and accurate HESS model, as well as HESS design optimization using a nested, dual-level optimization formulation and suitable optimization algorithms for both levels of searches have been presented. At the bottom level, design optimization focuses on the minimization of power loss in batteries, converter, and ultracapacitors (UCs), as well as the impact of battery depth of discharge (DOD) to its operation life, using a dynamic programming (DP)-based optimal energy management strategy (EMS). At the top level, HESS optimization of component size and battery DOD is carried out to achieve the minimum life-cycle cost (LCC) of the HESS for given power profiles and performance requirements as an outer loop. The complex and challenging optimization problem is solved using an advanced Multi-Start Space Reduction (MSSR) search method developed for computation-intensive, black-box global optimization problems. An example of load-haul-dump (LHD) vehicles is employed to verify the proposed HESS design optimization method and MSSR leads to superior optimization results and dramatically reduces computation time. This research forms the foundation for the design optimization of HESS, hybridization of vehicles with dynamic on-off power loads, and applications of the advanced global optimization method.

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