Multi-objective optimization of a welding process by the estimation of the Pareto optimal set

2011 ◽  
Vol 38 (7) ◽  
pp. 8045-8053 ◽  
Author(s):  
Luis M. Torres-Treviño ◽  
Felipe A. Reyes-Valdes ◽  
Victor López ◽  
Rolando Praga-Alejo
Keyword(s):  
Welding Process ◽  
Pareto Optimal ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Pareto Optimal Set ◽  
Optimal Set

scholarly journals Multi-Objective Optimization of Impedance Parameters in a Prosthesis Test Robot

2015 ◽  
Author(s):  
Poya Khalaf ◽  
Hanz Richter ◽  
Antonie J. van den Bogert ◽  
Dan Simon
Keyword(s):  
Control System ◽  
Motion Tracking ◽  
Optimization Problem ◽  
Peak Load ◽  
Pareto Optimal ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Pareto Optimal Set ◽  
Rms Error ◽  
Optimal Set

We design a control system for a prosthesis test robot that was previously developed for transfemoral prosthesis design and test. The robot’s control system aims to mimic human walking in the sagittal plane. It has been seen in previous work that trajectory control alone fails to produce human-like forces. Therefore, we utilize an impedance controller to achieve reasonable tracking of motion and force simultaneously. However, these objectives conflict. Impedance control design can therefore be viewed as a multi-objective optimization problem. We use an evolutionary multi-objective strategy called Multi-Objective Invasive Weed Optimization (MOIWO) to design the impedance controller. The multi-objective optimization problem admits a set of equally valid alternative solutions known as the Pareto optimal set. We use a pseudo weight vector approach to select a single solution from the Pareto optimal set. Simulation results show that a solution that is selected for pure motion tracking performs very accurate motion tracking (RMS error of 0.06 cm) but fails to produce the desired forces (RMS error of 70% peak load). On the other hand, a solution that is selected for pure force tracking successfully tracks the desired force (RMS error of 12.7% peak load) at the expense of motion trajectory errors (RMS error of 4.5 cm).

PCA-assisted reproduction for continuous multi-objective optimization with complicated Pareto optimal set

2021 ◽  
Vol 60 ◽  
pp. 100795
Author(s):  
Rui Wang ◽  
Nan-Jiang Dong ◽  
Dun-Wei Gong ◽  
Zhong-Bao Zhou ◽  
Shi Cheng ◽  
...  
Keyword(s):  
Assisted Reproduction ◽  
Pareto Optimal ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Pareto Optimal Set ◽  
Optimal Set

scholarly journals Pareto Optimal Multi-Objective Optimization of Antiwear Tialn/Tin/Cr Coatings

2014 ◽  
Vol 14 (1) ◽  
pp. 5-13 ◽  
Author(s):  
Ł. Szparaga ◽  
J. Ratajski
Keyword(s):  
Optimization Procedure ◽  
Hertzian Contact ◽  
Stress And Strain ◽  
Multilayer Coatings ◽  
Pareto Optimal ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Pareto Optimal Set ◽  
Optimal Values ◽  
Optimal Set

ABSTRACT The multi-objective optimization procedure of geometry of TiAlN/TiN/Cr multilayer coatings was created. The procedure was applied to the multilayer coatings subjected to constant tangential and normal loads (Hertzian contact). In physical model Cr, TiN and TiAlN layers were treated as a continuous medium, thus in mathematical description of the stress and strain states in the coatings a classical theory of stiffness was used. Decisional variables used in procedure were thicknesses of Cr, TiN and TiAlN layers and decisional criteria were functions of the stress and strain fields in the coating and substrate. Using created optimization procedure, Pareto set of optimal values of layers' thicknesses were determined. Additionally, two methods of analysis of Pareto-optimal set were introduced and discussed.

A Preference-Based Evolutionary Algorithm for Multi-Objective Optimization

2009 ◽  
Vol 17 (3) ◽  
pp. 411-436 ◽  
Author(s):  
Lothar Thiele ◽  
Kaisa Miettinen ◽  
Pekka J. Korhonen ◽  
Julian Molina
Keyword(s):  
Evolutionary Algorithm ◽  
Reference Point ◽  
Pareto Optimal ◽  
Multi Objective Optimization ◽  
Preference Information ◽  
Multi Objective ◽  
Pareto Optimal Set ◽  
Interactive Algorithm ◽  
Aspiration Levels ◽  
Optimal Set

In this paper, we discuss the idea of incorporating preference information into evolutionary multi-objective optimization and propose a preference-based evolutionary approach that can be used as an integral part of an interactive algorithm. One algorithm is proposed in the paper. At each iteration, the decision maker is asked to give preference information in terms of his or her reference point consisting of desirable aspiration levels for objective functions. The information is used in an evolutionary algorithm to generate a new population by combining the fitness function and an achievement scalarizing function. In multi-objective optimization, achievement scalarizing functions are widely used to project a given reference point into the Pareto optimal set. In our approach, the next population is thus more concentrated in the area where more preferred alternatives are assumed to lie and the whole Pareto optimal set does not have to be generated with equal accuracy. The approach is demonstrated by numerical examples.

An Orthogonal Multi-objective Evolutionary Algorithm for Multi-objective Optimization Problems with Constraints

2004 ◽  
Vol 12 (1) ◽  
pp. 77-98 ◽  
Author(s):  
Sanyou Y. Zeng ◽  
Lishan S. Kang ◽  
Lixin X. Ding
Keyword(s):  
Evolutionary Algorithm ◽  
Optimization Problems ◽  
Engineering Problem ◽  
Test Problems ◽  
Pareto Optimal ◽  
Large Set ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Pareto Optimal Set ◽  
Optimal Set

In this paper, an orthogonal multi-objective evolutionary algorithm (OMOEA) is proposed for multi-objective optimization problems (MOPs) with constraints. Firstly, these constraints are taken into account when determining Pareto dominance. As a result, a strict partial-ordered relation is obtained, and feasibility is not considered later in the selection process. Then, the orthogonal design and the statistical optimal method are generalized to MOPs, and a new type of multi-objective evolutionary algorithm (MOEA) is constructed. In this framework, an original niche evolves first, and splits into a group of sub-niches. Then every sub-niche repeats the above process. Due to the uniformity of the search, the optimality of the statistics, and the exponential increase of the splitting frequency of the niches, OMOEA uses a deterministic search without blindness or stochasticity. It can soon yield a large set of solutions which converges to the Pareto-optimal set with high precision and uniform distribution. We take six test problems designed by Deb, Zitzler et al., and an engineering problem (W) with constraints provided by Ray et al. to test the new technique. The numerical experiments show that our algorithm is superior to other MOGAS and MOEAs, such as FFGA, NSGAII, SPEA2, and so on, in terms of the precision, quantity and distribution of solutions. Notably, for the engineering problem W, it finds the Pareto-optimal set, which was previously unknown.

Multi-Objective Optimization of Heat Exchanger Design by Entropy Generation Minimization

2010 ◽  
Vol 132 (8) ◽  
Author(s):  
Jiangfeng Guo ◽  
Lin Cheng ◽  
Mingtian Xu
Keyword(s):  
Heat Exchanger ◽  
Entropy Generation ◽  
Optimization Design ◽  
Pareto Optimal ◽  
Multi Objective Optimization ◽  
Pumping Power ◽  
Multi Objective ◽  
Entropy Generation Minimization ◽  
Pareto Optimal Set ◽  
Optimal Set

In the present work, a multi-objective optimization of heat exchanger thermal design in the framework of the entropy generation minimization is presented. The objectives are to minimize the dimensionless entropy generation rates related to the heat conduction under finite temperature difference and fluid friction under finite pressure drop. Constraints are specified by the admissible pressure drop and design standards. The genetic algorithm is employed to search the Pareto optimal set of the multi-objective optimization problem. It is found that the solutions in the Pareto optimal set are trade-off between the pumping power and heat exchanger effectiveness. In some sense, the optimal solution in the Pareto optimal set achieves the largest exchanger effectiveness by consuming the least pumping power under the design requirements and standards. In comparison with the single-objective optimization design, the multi-objective optimization design leads to the significant decrease in the pumping power for achieving the same heat exchanger effectiveness and presents more flexibility in the design process.

ECONOMIC ENVIRONMENTAL DISPATCH BY A MODE TECHNIQUE

2008 ◽  
Vol 17 (03) ◽  
pp. 499-512 ◽  
Author(s):  
C. K. PANIGRAHI ◽  
R. CHAKRABARTI ◽  
P. K. CHATTOPADHYAY
Keyword(s):  
Optimization Problem ◽  
Pareto Optimal ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Pareto Optimal Set ◽  
Sample Test ◽  
Optimal Set ◽  
Mode Technique

Multi-objective differential evolution (MODE) is proposed to handle economic environmental dispatch problem, which is a multi-objective optimization problem (MOOPs) with competing and noncommensurable objectives. The proposed approach has a good performance in finding a diverse set of solutions and in converging near the true Pareto-optimal set. Numerical results for two sample test systems have been presented to demonstrate the capabilities of the proposed approach to generate well-distributed Pareto-optimal solutions of economic environmental dispatch problem in one single run. Simulation results with the proposed approach have been validated with Non-dominated Sorting Genetic Algorithm-II.

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