Generation of gait of walking robot based on the multi-objective optimization and determination of walking motion from the Pareto optimum solution set

2017 ◽  
Vol 2017.54 (0) ◽  
pp. I013
Author(s):  
Kenshiro KATAI ◽  
Seiya ISHII ◽  
Garuda FUJII ◽  
Masayuki NAKAMURA
Keyword(s):  
Solution Set ◽  
Pareto Optimum ◽  
Multi Objective Optimization ◽  
Walking Robot ◽  
Multi Objective ◽  
Walking Motion ◽  
Pareto Optimum Solution ◽  
Optimum Solution

scholarly journals Modelling and Multi-Objective Optimization of the Sulphur Dioxide Oxidation Process

Processes ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 1072
Author(s):  
Mohammad Reza Zaker ◽  
Clémence Fauteux-Lefebvre ◽  
Jules Thibault
Keyword(s):  
Sulphuric Acid ◽  
Sulphur Dioxide ◽  
Variable Number ◽  
Inlet Temperature ◽  
Multi Objective Optimization ◽  
Absorption Column ◽  
Maximum Reaction Rate ◽  
Multi Objective ◽  
Maximum Reaction

Sulphuric acid (H2SO4) is one of the most produced chemicals in the world. The critical step of the sulphuric acid production is the oxidation of sulphur dioxide (SO2) to sulphur trioxide (SO3) which takes place in a multi catalytic bed reactor. In this study, a representative kinetic rate equation was rigorously selected to develop a mathematical model to perform the multi-objective optimization (MOO) of the reactor. The objectives of the MOO were the SO2 conversion, SO3 productivity, and catalyst weight, whereas the decisions variables were the inlet temperature and the length of each catalytic bed. MOO studies were performed for various design scenarios involving a variable number of catalytic beds and different reactor configurations. The MOO process was mainly comprised of two steps: (1) the determination of Pareto domain via the determination a large number of non-dominated solutions, and (2) the ranking of the Pareto-optimal solutions based on preferences of a decision maker. Results show that a reactor comprised of four catalytic beds with an intermediate absorption column provides higher SO2 conversion, marginally superior to four catalytic beds without an intermediate SO3 absorption column. Both scenarios are close to the ideal optimum, where the reactor temperature would be adjusted to always be at the maximum reaction rate. Results clearly highlight the compromise existing between conversion, productivity and catalyst weight.

scholarly journals One Welfare State for Europe: A Costly Utopia?

2004 ◽  
Vol 4 (2) ◽  
pp. 1850020 ◽  
Author(s):  
Peter Hennessy ◽  
Thierry Warin
Keyword(s):  
Social Program ◽  
Pareto Optimum ◽  
Gdp Growth ◽  
The European Union ◽  
Social Expenditure ◽  
Asymmetric Shocks ◽  
Pareto Optimum Solution ◽  
Future Policy ◽  
Optimum Solution ◽  
Social Expenditures

This paper addresses the question of the social policy harmonization in the European Union. In adopting a common monetary policy, Europe is faced with structural and fiscal concerns, as national growth levels differ. Another possible factor in output shocks are the levels of various social expenditures in the member countries. OECD data on the level of social program expenditures in four EU countries will be compared to fluctuations in GDP growth to identify existing relationships. Significant relationships between independent social expenditure policy and GDP growth shocks suggest structural harmonization as an improvement if Europe is to take full advantage of the common market. However, the effects of expenditure levels may be easier to identify and predict than the dynamic effects of policy change. As the effects of future policy changes are more difficult to ascertain, harmonization may not consistently appear to be a Pareto-optimum solution to asymmetric shocks.

scholarly journals Bézier Simplex Fitting: Describing Pareto Fronts of´ Simplicial Problems with Small Samples in Multi-Objective Optimization

2019 ◽  
Vol 33 ◽  
pp. 2304-2313 ◽  
Author(s):  
Ken Kobayashi ◽  
Naoki Hamada ◽  
Akiyoshi Sannai ◽  
Akinori Tanaka ◽  
Kenichi Bannai ◽  
...  
Keyword(s):  
Sample Size ◽  
Optimization Problems ◽  
Solution Set ◽  
Small Samples ◽  
High Dimensional ◽  
Large Sample Size ◽  
Multi Objective Optimization ◽  
Dimensional Solution ◽  
Multi Objective ◽  
Simplex Model

Multi-objective optimization problems require simultaneously optimizing two or more objective functions. Many studies have reported that the solution set of an M-objective optimization problem often forms an (M − 1)-dimensional topological simplex (a curved line for M = 2, a curved triangle for M = 3, a curved tetrahedron for M = 4, etc.). Since the dimensionality of the solution set increases as the number of objectives grows, an exponentially large sample size is needed to cover the solution set. To reduce the required sample size, this paper proposes a Bézier simplex model and its fitting algorithm. These techniques can exploit the simplex structure of the solution set and decompose a high-dimensional surface fitting task into a sequence of low-dimensional ones. An approximation theorem of Bézier simplices is proven. Numerical experiments with synthetic and real-world optimization problems demonstrate that the proposed method achieves an accurate approximation of high-dimensional solution sets with small samples. In practice, such an approximation will be conducted in the postoptimization process and enable a better trade-off analysis.

A Study on Multi-Objective Optimization of Abrasive Blasting Systems

2010 ◽  
Vol 126-128 ◽  
pp. 29-34 ◽  
Author(s):  
Vu Ngoc Pi ◽  
Tran Minh Duc
Keyword(s):  
Boron Carbide ◽  
Optimization Problem ◽  
Nozzle Diameter ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Nozzle Wear ◽  
The Cost ◽  
Cost Components ◽  
Abrasive Blasting

This paper introduces a study on a multi-objective optimization problem of abrasive blasting systems. The aim of the study is to find the optimum exchanged diameter of boron carbide nozzles. In the study, the effects of several parameters such as the maximum nozzle diameter, the nozzle wear and the cost components on the optimum initial nozzle diameter were taken into account. From the study, a regression model for determination of the optimum initial diameter of boron carbide nozzles was introduced.

scholarly journals Multi-performance Target Collaborative Optimization Methods for Battery Electric Vehicle

2021 ◽  
Author(s):  
Chen Yawei ◽  
Chen Qian ◽  
Liu Jurui ◽  
Hao Xixiang ◽  
Yuan Chenheng
Keyword(s):  
Power Consumption ◽  
Ride Comfort ◽  
Simulation Software ◽  
Collaborative Optimization ◽  
Pareto Optimum ◽  
Stability Factor ◽  
Multi Objective Optimization ◽  
Analysis Model ◽  
Multi Objective ◽  
Target Values

Abstract The present studies on battery electric vehicles (BEVs) has mainly focused on the single-objective or weighted multi-objective optimization based on energy management, which can not manifest the coupling relationship among the vehicle performance objectives essentially. To optimize the handling stability, ride comfort and economy of BEV, this paper built the stability dynamics analysis model, ride comfort simulation half-car model and power consumption calculation model of BEV, as well as two-point virtual random excitation model on Level B road and proposed related evaluation indexes, including vehicle handling stability factor, weighted acceleration root-mean-square (RMS) value of vertical vibration at the driver’s seat and power consumption per 100 m at a constant speed. The Pareto optimum principle–based multi-objective evolutionary algorithm (MOEA) of BEV was also designed, which was encoded with real numbers and obtained the target values of all optional schemes via MATLAB/Simulink simulation software. The merits and demerits of alternative schemes could be judged according to the Pareto dominance principle, so that alternative schemes obtained after optimization were realizable. The results of simulation experiment suggest that the proposed algorithm can perform the multi-objective optimization on BEV, and obtain a group of Pareto optimum solutions featured by high handling stability, favorable ride comfort and low energy consumption for the decision-makers.

scholarly journals Comparative Study of Knee-Based Algorithms for Many-Objective Optimization Problems

2018 ◽  
Vol 12 (1) ◽  
pp. 7-16
Author(s):  
Saad M. Alzahrani ◽  
Naruemon Wattanapongsakorn
Keyword(s):  
Optimization Problems ◽  
Benchmark Problems ◽  
Multi Objective Optimization ◽  
Trade Off ◽  
Knee Region ◽  
Multi Objective ◽  
Trade Offs ◽  
Conflicting Objectives ◽  
Small Set ◽  
Optimum Solution

Nowadays, most real-world optimization problems consist of many and often conflicting objectives to be optimized simultaneously. Although, many current Multi-Objective optimization algorithms can efficiently solve problems with 3 or less objectives, their performance deteriorates proportionally with the increasing of the objectives number. Furthermore, in many situations the decision maker (DM) is not interested in all trade-off solutions obtained but rather interested in a single optimum solution or a small set of those trade-offs. Therefore, determining an optimum solution or a small set of trade-off solutions is a difficult task. However, an interesting method for finding such solutions is identifying solutions in the Knee region. Solutions in the Knee region can be considered the best obtained solution in the obtained trade-off set especially if there is no preference or equally important objectives. In this paper, a pruning strategy was used to find solutions in the Knee region of Pareto optimal fronts for some benchmark problems obtained by NSGA-II, MOEA/D-DE and a promising new Multi-Objective optimization algorithm NSGA-III. Lastly, those knee solutions found were compared and evaluated using a generational distance performance metric, computation time and a statistical one-way ANOVA test.

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