Optimal Design of the Geometric Parameters of a Disk Cam Translating Follower Mechanism Using Chebychev Approximation

1992 ◽  
Author(s):  
Y. Peng ◽  
J. Zhou ◽  
Z. Dong
Keyword(s):  
Structural Design ◽  
Optimization Problem ◽  
Approximation Scheme ◽  
Optimization Methods ◽  
Maximum Pressure ◽  
Multiple Constraints ◽  
Multi Objective Optimization ◽  
Design And Optimization ◽  
Cam Follower ◽  
Chebychev Approximation

Abstract A method for the structural design of a cam-translating roller follower mechanism with the minimum volume and least maximum pressure angle is presented. The design is formulated as a two-objective optimization problem subject to multiple constraints. The hierarchical approach for handling multi-objective optimization and application of the Chebychev approximation scheme for solving the nested maximization-minimization problem are discussed. The method presents a forthright approach to the complex optimal structural design of a cam-follower mechanism, and significantly improves present cam-follower mechanism design and optimization methods. A design example is used to illustrate the approach.

scholarly journals Current State of Development of Ship Structural Design and Optimization Methods

2020 ◽  
Vol 3 (3) ◽  
pp. 171-187
Author(s):  
Marin Palaversa ◽  
Pero Prebeg ◽  
Jerolin Andrić
Keyword(s):  
Sensitivity Analysis ◽  
Support System ◽  
Structural Design ◽  
State Of The Art ◽  
Optimization Methods ◽  
Offshore Structures ◽  
Engineering Practice ◽  
Design And Optimization ◽  
Current State ◽  
Design Support System

This paper presents state-of-the-art methodologies and methods used in the rationally-based structural design of ships and offshore structures, namely design support system, structural optimization, surrogate modelling and sensitivity analysis. It demonstrates their application in structural design of a platform support vessel. It ends with a list of benefits that a structural designer may expect when the presented methods/methodologies are used. It also shows the obstacles to their full implementation in the engineering practice.

scholarly journals Applying Genetic Algorithm to Optimization Second-Order Bandpass MGMFB Filter

2020 ◽  
Vol 28 (4) ◽  
Author(s):  
Maad Mohsin Mijwil ◽  
Rana Ali Abttan
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Optimization Methods ◽  
Second Order ◽  
Active Filters ◽  
Multi Objective Optimization ◽  
Complex Situation ◽  
Human Effort ◽  
True Values ◽  
Selection Of

In this paper, we have applied the genetic algorithm to the selection of the true values for RC (resistors/capacitors) as an essential role in the development of analogue active filters. The classic method of incorporating passive elements is a complex situation and can attend to errors. In order to reduce the frequency of errors and the human effort, evolutionary optimization methods are employed to select the RC values. In this study, Genetic algorithm (GA) is proposed to optimize the second-order active filter. It must find the values of the passive elements RC to get a filter configuration that reduces the sensitivities to variations as well as reduces design errors less than a defined height value, concerning certain specifications. The optimization problem which is one of the problems that must be solved by GA is a multi-objective optimization problem (MOOP). GA was carried out taking into account two possible situations about the values that resistors and capacitors could adopt. The obtained experimental results show that GA can be used to obtain filter configurations that meet the specified standard.

An Efficient Framework Using Normalized Dominance Operator for Multi-Objective Evolutionary Algorithms

2019 ◽  
Vol 10 (1) ◽  
pp. 15-37 ◽  
Author(s):  
Muneendra Ojha ◽  
Krishna Pratap Singh ◽  
Pavan Chakraborty ◽  
Shekhar Verma
Keyword(s):  
Evolutionary Algorithms ◽  
Pareto Front ◽  
Optimization Problem ◽  
Computational Cost ◽  
Optimization Methods ◽  
Objective Test ◽  
Test Problems ◽  
Multi Objective Optimization ◽  
Pareto Dominance ◽  
Multi Objective

Multi-objective optimization algorithms using evolutionary optimization methods have shown strength in solving various problems using several techniques for producing uniformly distributed set of solutions. In this article, a framework is presented to solve the multi-objective optimization problem which implements a novel normalized dominance operator (ND) with the Pareto dominance concept. The proposed method has a lesser computational cost as compared to crowding-distance-based algorithms and better convergence. A parallel external elitist archive is used which enhances spread of solutions across the Pareto front. The proposed algorithm is applied to a number of benchmark multi-objective test problems with up to 10 objectives and compared with widely accepted aggregation-based techniques. Experiments produce a consistently good performance when applied to different recombination operators. Results have further been compared with other established methods to prove effective convergence and scalability.

A Special Multiobjective Evolutionary Algorithm for Solving Complicated Nonlinear Equations Systems

2014 ◽  
Vol 701-702 ◽  
pp. 18-23
Author(s):  
Chun An Liu
Keyword(s):  
Evolutionary Algorithm ◽  
Nonlinear Equations ◽  
Optimization Problem ◽  
Optimal Solution ◽  
Optimization Methods ◽  
Multi Objective Optimization ◽  
Information Industry ◽  
Multi Objective ◽  
Application Fields ◽  
Industry Network

It is well known that nonlinear equations systems (NESS) is a subclass of nonlinear optimization problem, it exists in many application fields, such as information industry, network design, mechanics and robotics, etc.. How to design feasible and effective optimization methods to obtain the optimal solution or satisfied precision requirement’s optimal solution for complicated NESS is very important in computation fields. In this paper, each nonlinear sub-equation of NESS is approximately regarded as a sub-objective function of multi-objective optimization problem, then the original nonlinear equations systems is transformed into a multi-objective optimization problem, and the equivalence relation of the solution between the original NESS and the transformed multi-objective optimization problem is given. In order to effectively solve the nonlinear equations systems, a self-adaptive levy mutation operation is proposed, and a multi-objective optimization evolutionary algorithm to solve the nonlinear equations systems was designed. Computer simulations demonstrate the proposed algorithm can not only increase the diversity of evolutionary population but also make the evolution population quickly to approach the optimal solution or satisfied precision requirement’s optimal solution.

Stochastic Approximation Methods in Optimal Structural Design: The Use of Intermediate Variables

1996 ◽  
Author(s):  
Kurt Marti
Keyword(s):  
Stochastic Approximation ◽  
Structural Design ◽  
Optimization Problem ◽  
Optimization Methods ◽  
Approximation Methods ◽  
Optimal Structural Design ◽  
Stochastic Optimization Problem ◽  
Reliability Based Optimization ◽  
Intermediate Variables ◽  
Intervening Variables

Abstract Reliability-based optimization methods in optimal structural design use mostly the following basic design criteria: I) Minimal weight (volume or costs) and II) high or reliability of the structure. Since, in practice, several parameters of the structure, e.g. elastic moduli, tolerances of structural dimensions, loads, are not given, fixed quantities, but random variables having a certain probability distribution P, a stochastic optimization problem will result from criteria (I), (II), which can be represented min F(x) with F(x) := Ef(ω, x). (1) x∇D Stochastic approximation methods are considered for solving (1): The gradient estimators are obtained by the response surface methodology (RSM) where especially the improvement of the estimators by using so-called “intermediate” or “intervening” variables is considered.

scholarly journals Braille Block Detection via Multi-Objective Optimization from an Egocentric Viewpoint

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2775
Author(s):  
Tsubasa Takano ◽  
Takumi Nakane ◽  
Takuya Akashi ◽  
Chao Zhang
Keyword(s):  
Optimization Problem ◽  
Line Pair ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Optimization Framework ◽  
Visually Impaired People ◽  
Comprehensive Comparison ◽  
Basic Characteristics ◽  
Support Devices

In this paper, we propose a method to detect Braille blocks from an egocentric viewpoint, which is a key part of many walking support devices for visually impaired people. Our main contribution is to cast this task as a multi-objective optimization problem and exploits both the geometric and the appearance features for detection. Specifically, two objective functions were designed under an evolutionary optimization framework with a line pair modeled as an individual (i.e., solution). Both of the objectives follow the basic characteristics of the Braille blocks, which aim to clarify the boundaries and estimate the likelihood of the Braille block surface. Our proposed method was assessed by an originally collected and annotated dataset under real scenarios. Both quantitative and qualitative experimental results show that the proposed method can detect Braille blocks under various environments. We also provide a comprehensive comparison of the detection performance with respect to different multi-objective optimization algorithms.

Auto-sharing parameters for transfer learning based on multi-objective optimization

2021 ◽  
pp. 1-13
Author(s):  
Hailin Liu ◽  
Fangqing Gu ◽  
Zixian Lin
Keyword(s):  
Transfer Learning ◽  
Optimization Problem ◽  
Data Sets ◽  
Multi Objective Optimization ◽  
Particle Swarm Optimizer ◽  
Real World Data ◽  
Data Set ◽  
Target Task ◽  
Main Research ◽  
Multi Objective

Transfer learning methods exploit similarities between different datasets to improve the performance of the target task by transferring knowledge from source tasks to the target task. “What to transfer” is a main research issue in transfer learning. The existing transfer learning method generally needs to acquire the shared parameters by integrating human knowledge. However, in many real applications, an understanding of which parameters can be shared is unknown beforehand. Transfer learning model is essentially a special multi-objective optimization problem. Consequently, this paper proposes a novel auto-sharing parameter technique for transfer learning based on multi-objective optimization and solves the optimization problem by using a multi-swarm particle swarm optimizer. Each task objective is simultaneously optimized by a sub-swarm. The current best particle from the sub-swarm of the target task is used to guide the search of particles of the source tasks and vice versa. The target task and source task are jointly solved by sharing the information of the best particle, which works as an inductive bias. Experiments are carried out to evaluate the proposed algorithm on several synthetic data sets and two real-world data sets of a school data set and a landmine data set, which show that the proposed algorithm is effective.

Review: Multi-objective optimization methods and application in energy saving

Energy ◽  
2017 ◽  
Vol 125 ◽  
pp. 681-704 ◽  
Author(s):  
Yunfei Cui ◽  
Zhiqiang Geng ◽  
Qunxiong Zhu ◽  
Yongming Han
Keyword(s):  
Energy Saving ◽  
Optimization Methods ◽  
Multi Objective Optimization ◽  
Multi Objective
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