A New Hybrid Method to Solve the Multi-objective Optimization Problem for a Composite Hat-Stiffened Panel

2018 ◽  
pp. 77-88
Author(s):  
Ahmad El Samrout ◽  
Oussama Braydi ◽  
Rafic Younes ◽  
Francois Trouchu ◽  
Pascal Lafon
Keyword(s):  
Hybrid Method ◽  
Optimization Problem ◽  
Stiffened Panel ◽  
Multi Objective Optimization ◽  
Multi Objective

A hybrid method of multi-objective particle swarm optimization and k-means clustering and its application to modal parameter estimation in the time–frequency domain

2019 ◽  
Vol 26 (9-10) ◽  
pp. 769-778
Author(s):  
Kai Yang ◽  
Kaiping Yu ◽  
Hui Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Hybrid Method ◽  
Optimization Problem ◽  
Particle Swarm ◽  
Modal Parameters ◽  
Multi Objective Optimization ◽  
Swarm Optimization ◽  
Time Frequency ◽  
Multi Objective ◽  
External Archive

Modal parameters provide an insight into the dynamical properties of structures. In the time–frequency domain–based methods, time–frequency ridges contain crucial information on the characteristics of multicomponent signals, and manually extracting time–frequency ridges is a huge burden, especially when long-time time-varying modal parameters are focused on. In this study, time–frequency ridge extraction is converted into a multi-objective optimization problem, and a new hybrid method of multi-objective particle swarm optimization and k-means clustering is proposed to solve such a multi-objective optimization problem. In the hybrid method, the particle swarm is partitioned into sub-swarms by k-means clustering, and the sub-swarms are used to search new solutions for updating a finite-sized external archive, which is used as the exclusive centroids of the k-means clustering. Simultaneously, the finite-sized external archive serves as global best positions of sub-swarms. Both simulated and experimental cases are applied to validate the hybrid method. With the aid of the hybrid method, the influence of varying temperatures on modal parameters of a column beam is experimentally analyzed in detail.

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.

Toward the Use of Pareto Performance Solutions and Pareto Robustness Solutions for Multi-Objective Robust Optimization Problems

2012 ◽  
Author(s):  
Weijun Wang ◽  
Stéphane Caro ◽  
Fouad Bennis ◽  
Oscar Brito Augusto
Keyword(s):  
Robust Optimization ◽  
Pareto Front ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Multi Objective Optimization ◽  
Robust Solutions ◽  
Multi Objective ◽  
Robust Optimization Problem ◽  
Design Solutions ◽  
Further Development

For Multi-Objective Robust Optimization Problem (MOROP), it is important to obtain design solutions that are both optimal and robust. To find these solutions, usually, the designer need to set a threshold of the variation of Performance Functions (PFs) before optimization, or add the effects of uncertainties on the original PFs to generate a new Pareto robust front. In this paper, we divide a MOROP into two Multi-Objective Optimization Problems (MOOPs). One is the original MOOP, another one is that we take the Robustness Functions (RFs), robust counterparts of the original PFs, as optimization objectives. After solving these two MOOPs separately, two sets of solutions come out, namely the Pareto Performance Solutions (PP) and the Pareto Robustness Solutions (PR). Make a further development on these two sets, we can get two types of solutions, namely the Pareto Robustness Solutions among the Pareto Performance Solutions (PR(PP)), and the Pareto Performance Solutions among the Pareto Robustness Solutions (PP(PR)). Further more, the intersection of PR(PP) and PP(PR) can represent the intersection of PR and PP well. Then the designer can choose good solutions by comparing the results of PR(PP) and PP(PR). Thanks to this method, we can find out the optimal and robust solutions without setting the threshold of the variation of PFs nor losing the initial Pareto front. Finally, an illustrative example highlights the contributions of the paper.

Multi-Objective Optimal Load Distribution Based on Sub Goals Multiplication and Division in Power Plants

2014 ◽  
Vol 494-495 ◽  
pp. 1715-1718
Author(s):  
Gui Li Yuan ◽  
Tong Yu ◽  
Juan Du
Keyword(s):  
Genetic Algorithm ◽  
Load Distribution ◽  
Power Plants ◽  
Optimization Problem ◽  
Thermal Power ◽  
Local Optimum ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Optimal Load ◽  
Optimal Load Distribution

The classic multi-objective optimization method of sub goals multiplication and division theory is applied to solve optimal load distribution problem in thermal power plants. A multi-objective optimization model is built which comprehensively reflects the economy, environmental protection and speediness. The proposed model effectively avoids the target normalization and weights determination existing in the process of changing the multi-objective optimization problem into a single objective optimization problem. Since genetic algorithm (GA) has the drawback of falling into local optimum, adaptive immune vaccines algorithm (AIVA) is applied to optimize the constructed model and the results are compared with that optimized by genetic algorithm. Simulation shows this method can complete multi-objective optimal load distribution quickly and efficiently.

scholarly journals Efficient Hybrid Method of FEA-Based RSM and PSO Algorithm for Multi-Objective Optimization Design for a Compliant Rotary Joint for Upper Limb Assistive Device

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Ngoc Le Chau ◽  
Hieu Giang Le ◽  
Thanh-Phong Dao ◽  
Minh Phung Dang ◽  
Van Anh Dang
Keyword(s):  
Hybrid Method ◽  
Optimization Design ◽  
Pso Algorithm ◽  
Algorithm Analysis ◽  
Rank Test ◽  
Multi Objective Optimization ◽  
Rotational Angle ◽  
Multi Objective ◽  
Rotary Joint ◽  
Weight Factors

This paper proposes an efficient hybrid methodology for multi-objective optimization design of a compliant rotary joint (CRJ). A combination of the Taguchi method (TM), finite element analysis (FEA), the response surface method (RSM), and particle swarm optimization (PSO) algorithm is developed to solving the optimization problem. Firstly, the TM is applied to determine the number of numerical experiments. And then, 3D models of the CRJ is built for FEA simulation, and mathematical models are formed using the RSM. Subsequently, the suitability of the regression equation is assessed. At the same time, the calculation of weight factors is identified based on the series of statistical equations. Based on the well-established equations, a minimum mass and a maximum rotational angle are simultaneously optimized through the PSO algorithm. Analysis of variance is used to analyze the contribution of design variables. The behavior of the proposed method is compared to the adaptive elitist differential evolution and cuckoo search algorithm through the Wilcoxon signed rank test and Friedman test. The results determined the weight factors of the mass and rotational angle are about 0.4983 and 0.5017, respectively. The results found that the optimum the mass and rotational angle are 0.0368 grams and 59.1928 degrees, respectively. It revealed that the maximum stress of 335 MPa can guarantee a long working time. The results showed that the proposed hybrid method outperforms compared to other evolutionary algorithms. The predicted results are close to the validation results. The proposed method is useful for related engineering fields.

Sign in / Sign up

Export Citation Format

Share Document