scholarly journals Solving dynamic multi-objective problems with a new prediction-based optimization algorithm

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0254839
Author(s):  
Qingyang Zhang ◽  
Shouyong Jiang ◽  
Shengxiang Yang ◽  
Hui Song
Keyword(s):  
Optimization Algorithm ◽  
Linear Prediction ◽  
Sampling Strategy ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Pareto Optimal Set ◽  
Estimation Of Distribution ◽  
Optimal Set ◽  
Linear Prediction Model ◽  
Prediction Strategy

This paper proposes a new dynamic multi-objective optimization algorithm by integrating a new fitting-based prediction (FBP) mechanism with regularity model-based multi-objective estimation of distribution algorithm (RM-MEDA) for multi-objective optimization in changing environments. The prediction-based reaction mechanism aims to generate high-quality population when changes occur, which includes three subpopulations for tracking the moving Pareto-optimal set effectively. The first subpopulation is created by a simple linear prediction model with two different stepsizes. The second subpopulation consists of some new sampling individuals generated by the fitting-based prediction strategy. The third subpopulation is created by employing a recent sampling strategy, generating some effective search individuals for improving population convergence and diversity. Experimental results on a set of benchmark functions with a variety of different dynamic characteristics and difficulties illustrate that the proposed algorithm has competitive effectiveness compared with some state-of-the-art algorithms.

Multi-Objective Optimization Analysis of Motor Cooling System in Articulated Dump Truck

2011 ◽  
Vol 383-390 ◽  
pp. 4715-4720
Author(s):  
Yan Zhang ◽  
Yan Hua Shen ◽  
Wen Ming Zhang
Keyword(s):  
Field Distribution ◽  
Cooling System ◽  
Dump Truck ◽  
Water Cooling ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Pareto Optimal Set ◽  
Temperature And Pressure ◽  
Water Cooling System ◽  
Optimal Set

In order to ensure the reliable and safe operation of the electric driving motor of the articulated dump truck, water cooling system is installed for each motor. For the best performance of the water cooling system, not only the heat transfer should be enhanced to maintain the motor in relatively low temperature, but also the pressure drop in the water cooling system should be reduced to save energy by reducing the power consumption of the pump. In this paper, the numerical simulation of the cooling progress is completed and the temperature and pressure field distribution are obtained. The multi-objective optimization model is established which involves the cooling system structure, temperature field distribution and pressure field distribution. To improve the computational efficiency, the surrogate model of the simulation about the cooling process is established based on the Response Surface Methodology (RSM). After the multi-objective optimization, the Pareto optimal set is obtained. The proper design point, which could make the average temperature and pressure drop of the cooling system relative desirable, is chosen from the Pareto 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).

scholarly journals A Decomposition-Based Interactive Method for Multi-Objective Evolutionary Algorithm

2012 ◽  
Author(s):  
Nguye Long ◽  
Bui Thu Lam
Keyword(s):  
Optimization Problems ◽  
Decision Makers ◽  
Optimization Process ◽  
Multi Objective Optimization ◽  
Interactive Method ◽  
Multi Objective ◽  
Pareto Optimal Set ◽  
Single Solution ◽  
Optimal Set

Multi-objectivity has existed in many real-world optimization problems. In most multi-objective cases, objectives are often conflicting, there is no single solution being optimal with regards to all objectives. These problems are called Multi-objective Optimization Problems (MOPs). To date, there have been al large number of methods for solving MOPs including evolutionary methods (namly Multi-objective Evolutionary Algorithms MOEAs). With the use of a population of solutions for searching. MOEAs are naturally suitable for approximating optimal solutions (called the Pareto Optimal Set (POS) or the efficient set). There has been a popular trend in MOEAs considering the role of Decision Makers (DMs) during the optimization process (known as the human-in-loop) for checking, analyzing the results and giving the preference to guide the optimization process. This is call the interactive method.

Collective intelligence approaches in interactive evolutionary multi-objective optimization

2020 ◽  
Vol 28 (1) ◽  
pp. 95-108 ◽  
Author(s):  
Daniel Cinalli ◽  
Luis Martí ◽  
Nayat Sanchez-Pi ◽  
Ana Cristina Bicharra Garcia
Keyword(s):  
Collective Intelligence ◽  
Real Life ◽  
Decision Makers ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Pareto Optimal Set ◽  
Trade Offs ◽  
Life Problems ◽  
Conflicting Objectives ◽  
Optimal Set

Abstract Evolutionary multi-objective optimization algorithms (EMOAs) have been successfully applied in many real-life problems. EMOAs approximate the set of trade-offs between multiple conflicting objectives, known as the Pareto optimal set. Reference point approaches can alleviate the optimization process by highlighting relevant areas of the Pareto set and support the decision makers to take the more confident evaluation. One important drawback of this approaches is that they require an in-depth knowledge of the problem being solved in order to function correctly. Collective intelligence has been put forward as an alternative to deal with situations like these. This paper extends some well-known EMOAs to incorporate collective preferences and interactive techniques. Similarly, two new preference-based multi-objective optimization performance indicators are introduced in order to analyze the results produced by the proposed algorithms in the comparative experiments carried out.

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

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.

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