Multi-Objective Trajectory Optimization of Free-Floating Space Manipulator Using NSGA-II

2015 ◽  
Vol 713-715 ◽  
pp. 800-804 ◽  
Author(s):  
Gang Chen ◽  
Cong Wei ◽  
Qing Xuan Jia ◽  
Han Xu Sun ◽  
Bo Yang Yu
Keyword(s):  
Trajectory Optimization ◽  
Interpolation Method ◽  
Optimal Solution ◽  
Optimization Method ◽  
Joint Space ◽  
Motion Path ◽  
Objective Functions ◽  
Nsga Ii ◽  
Space Manipulator ◽  
Multi Objective

In this paper, a kind of multi-objective trajectory optimization method based on non-dominated sorting genetic algorithm II (NSGA-II) is proposed for free-floating space manipulator. The aim is to optimize the motion path of the space manipulator with joint angle constraints and joint velocity constraints. Firstly, the kinematics and dynamics model are built. Secondly, the 3-5-3 piecewise polynomial is selected as interpolation method for trajectory planning of joint space. Thirdly, three objective functions are established to simultaneously minimize execution time, energy consumption and jerk of the joints. At last, the objective functions are combined with the NSGA-II algorithm to get the Pareto optimal solution set. The effectiveness of the mentioned method is verified by simulations.

scholarly journals NSGA-II algorithm and application for multi-objective flexible workshop scheduling

2020 ◽  
Vol 14 ◽  
pp. 174830262094246
Author(s):  
Wang Yahui ◽  
Shi Ling ◽  
Zhang Cai ◽  
Fu Liuqiang ◽  
Jin Xiangjie
Keyword(s):  
Genetic Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Optimal Solution ◽  
Scheduling Problem ◽  
Objective Functions ◽  
Nsga Ii ◽  
Industrial Processing ◽  
Multi Objective ◽  
Processing Cost

Based on the study of multi-objective flexible workshop scheduling problem and the learning of traditional genetic algorithm, a non-dominated sorting genetic algorithm is proposed to solve and optimize the scheduling model with the objective functions of processing cycle, advance/delay penalty and processing cost. In the process of optimization, non-dominated fast ranking operator and competition operator are used to select the descendant operator, which improves the computational efficiency and optimization ability of the algorithm. Non-repetitive non-dominant solutions and frontier sets are found in the iteration operation to retain the optimal results. Finally, taking a manufacturing workshop as an example, the practicability of the proposed algorithm is verified by the simulation operation of the workshop scheduling information and the comparison with other algorithms. The results show that the algorithm can obtain the optimal solution more quickly than the unimproved algorithm. The improved algorithm is faster and more effective in searching, and has certain feasibility in solving the job shop scheduling problem, which is more suitable for industrial processing and production.

scholarly journals Optimum design of 3R robot manipulador by using Improved Differential Evolution implemented in parallel computation

2020 ◽  
Vol 1 (2) ◽  
pp. 83-103
Author(s):  
Sezimária De Fátima Pereira Saramago ◽  
José Laércio Doricio ◽  
Milena Almeida Leite Brandão
Keyword(s):  
Differential Evolution ◽  
Optimization Problems ◽  
Robot Manipulator ◽  
Optimal Solution ◽  
Optimization Method ◽  
Objective Functions ◽  
Basic Algorithm ◽  
Promising Alternative ◽  
Multi Objective ◽  
De Algorithm

In recent decades the great interest in Evolutionary Algorithms (EAs) has boosted their development leading to a significant improvement in their efficiency and applicability. Thus, EAs have been applied to solve optimization problems in different areas of knowledge. A promising optimization method known as Differential Evolution (DE), which belongs to the class of AEs, has attracted the attention of researchers. The DE algorithm is simple, robust and efficient. However, by testing with classical optimization problems noticed that sometimes the results obtained with DE are not as satisfactory as expected or that in many cases the algorithm ends the search for the optimal solution prematurely. Recently, with the advancement and greater availability of computer technology, the scientific community has been thinking about the implementation of optimization algorithms in parallel in order to reduce the processing time. The main objective of this paper is to present an improvement of the Differential Evolution optimization method, proposing modifications to the basic algorithm by using shuffled complex and making it able to work with parallel computing. The proposed methodology is applied to the optimal design of an orthogonal 3R robot manipulator that takes into account the characteristics of its workspace. For this purpose, a multi-objective optimization problem is formulated to obtain the optimal geometric parameters for the robot. The maximum workspace volume, the maximum system stiffness and the optimum dexterity are considered as the multi-objective functions. The results show that the procedure represents a promising alternative for the type of problem presented above.

Multi-objective Optimization for Common and Special Components: First Step Toward Network Optimization of Regular and Non-Regular Flights

2015 ◽  
Vol 11 (02) ◽  
pp. 183-199
Author(s):  
Takahiro Jinba ◽  
Hiroto Kitagawa ◽  
Eriko Azuma ◽  
Keiji Sato ◽  
Hiroyuki Sato ◽  
...  
Keyword(s):  
Optimization Method ◽  
High Rate ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Test Bed ◽  
Nsga Ii ◽  
Multi Objective ◽  
The Common ◽  
Airline Company ◽  
Common Components

To optimize the problem composed of (i) the common components which should be optimized from the viewpoint of all objective functions and (ii) the special components which should be optimized from the viewpoint of one of the objective functions, this paper proposes a new multi-objective optimization method which optimizes not only the common components for all objective functions but also the special ones for each objective function. To investigate the effectiveness of the proposed method, this paper tested our method on the test-bed problem which is an extended version of the 0/1 knapsack problem. The intensive experiments have revealed the following implications: (i) Our method finds better solutions which have higher fitness than the conventional method (NSGA-II); (ii) our method can find the solutions that had a large norm (which corresponds to a high profit of an airline company in the flight scheduling problem) with the high rate of the common components; and (iii) since the crowding distance employed in our method contributes to keeping the diversity during the solution search, our method has high exploration capability of solutions.

Optimization Design of Helical Spring Based on Multi-Objective Genetic Algorithm

2013 ◽  
Vol 373-375 ◽  
pp. 1068-1071
Author(s):  
Kang Li Shao ◽  
Feng Wang ◽  
Yong Hai Wu
Keyword(s):  
Optimization Design ◽  
Optimal Solution ◽  
Optimization Method ◽  
Coil Spring ◽  
Design Quality ◽  
Nsga Ii ◽  
Suspension Spring ◽  
Multi Objective ◽  
Free Vibration Frequency ◽  
Light Vehicle

Suspension spring is used in the suspension system of light vehicle and medium buses widely, and its design quality related to stability and security of the vehicle. This paper take the suspension coil spring of a light vehicle as the research object, its multi-objective optimization model is established. The volume of spring and one frequency free vibration frequency are taken as optimization objective, the strength, stiffness, stability, fatigue strength and the winding ratio of the spring are taken as constraints, and use NSGA-II algorithm, obtained Pareto optimal solution set of the optimization problem. The coil spring model and optimization method used in this paper is also suitable for optimization design of other spring.

Multi Objective Design Optimization of Two Bar Truss Using NSGA II and TOPSIS

2014 ◽  
Vol 984-985 ◽  
pp. 419-424
Author(s):  
P. Sabarinath ◽  
M.R. Thansekhar ◽  
R. Saravanan
Keyword(s):  
Design Optimization ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Optimal Solutions ◽  
Objective Functions ◽  
Nsga Ii ◽  
Multi Objective ◽  
Total Displacement ◽  
Design Variables ◽  
Conflicting Objectives

Arriving optimal solutions is one of the important tasks in engineering design. Many real-world design optimization problems involve multiple conflicting objectives. The design variables are of continuous or discrete in nature. In general, for solving Multi Objective Optimization methods weight method is preferred. In this method, all the objective functions are converted into a single objective function by assigning suitable weights to each objective functions. The main drawback lies in the selection of proper weights. Recently, evolutionary algorithms are used to find the nondominated optimal solutions called as Pareto optimal front in a single run. In recent years, Non-dominated Sorting Genetic Algorithm II (NSGA-II) finds increasing applications in solving multi objective problems comprising of conflicting objectives because of low computational requirements, elitism and parameter-less sharing approach. In this work, we propose a methodology which integrates NSGA-II and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for solving a two bar truss problem. NSGA-II searches for the Pareto set where two bar truss is evaluated in terms of minimizing the weight of the truss and minimizing the total displacement of the joint under the given load. Subsequently, TOPSIS selects the best compromise solution.

scholarly journals Multi-objective optimization of solar thermal photovoltaic hybrid power generation system based on NSGA-II algorithm

2021 ◽  
Vol 336 ◽  
pp. 02022
Author(s):  
Liang Meng ◽  
Wen Zhou ◽  
Yang Li ◽  
Zhibin Liu ◽  
Yajing Liu
Keyword(s):  
Power Generation ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Solar Thermal ◽  
Multi Objective Optimization ◽  
Generation System ◽  
Nsga Ii ◽  
Multi Objective ◽  
Hybrid Power ◽  
Power Generation System

In this paper, NSGA-Ⅱ is used to realize the dual-objective optimization and three-objective optimization of the solar-thermal photovoltaic hybrid power generation system; Compared with the optimal solution set of three-objective optimization, optimization based on technical and economic evaluation indicators belongs to the category of multi-objective optimization. It can be considered that NSGA-Ⅱ is very suitable for multi-objective optimization of solar-thermal photovoltaic hybrid power generation system and other similar multi-objective optimization problems.

scholarly journals A two-stage evolutionary optimization approach for an irrigation system design

2016 ◽  
Vol 19 (1) ◽  
pp. 115-122 ◽  
Author(s):  
Milan Cisty ◽  
Zbynek Bajtek ◽  
Lubomir Celar
Keyword(s):  
Water Distribution ◽  
Irrigation System ◽  
Optimal Solution ◽  
Water Distribution Network ◽  
Search Space ◽  
Optimization Method ◽  
Global Optimum ◽  
Optimization Approach ◽  
Nsga Ii ◽  
Irrigation Network

In this work, an optimal design of a water distribution network is proposed for large irrigation networks. The proposed approach is built upon an existing optimization method (NSGA-II), but the authors are proposing its effective application in a new two-step optimization process. The aim of the paper is to demonstrate that not only is the choice of method important for obtaining good optimization results, but also how that method is applied. The proposed methodology utilizes as its most important feature the ensemble approach, in which more optimization runs cooperate and are used together. The authors assume that the main problem in finding the optimal solution for a water distribution optimization problem is the very large size of the search space in which the optimal solution should be found. In the proposed method, a reduction of the search space is suggested, so the final solution is thus easier to find and offers greater guarantees of accuracy (closeness to the global optimum). The method has been successfully tested on a large benchmark irrigation network.

Kinematic Multi-Objective Optimization of Circular-Toothed Gerotor Pumps by Genetic Algorithm

2017 ◽  
Author(s):  
Andrew J. Robison ◽  
Andrea Vacca
Keyword(s):  
Genetic Algorithm ◽  
Design Method ◽  
Third Party ◽  
Low Flow ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Generation Algorithm ◽  
Multi Objective ◽  
Pareto Curve

A gerotor gear generation algorithm has been developed that evaluates key performance objective functions to be minimized or maximized, and then an optimization algorithm is applied to determine the best design. Because of their popularity, circular-toothed gerotors are the focus of this study, and future work can extend this procedure to other gear forms. Parametric equations defining the circular-toothed gear set have been derived and implemented. Two objective functions were used in this kinematic optimization: maximize the ratio of displacement to pump radius, which is a measure of compactness, and minimize the kinematic flow ripple, which can have a negative effect on system dynamics and could be a major source of noise. Designs were constrained to ensure drivability, so the need for additional synchronization gearing is eliminated. The NSGA-II genetic algorithm was then applied to the gear generation algorithm in modeFRONTIER, a commercial software that integrates multi-objective optimization with third-party engineering software. A clear Pareto front was identified, and a multi-criteria decision-making genetic algorithm was used to select three optimal designs with varying priorities of compactness vs low flow variation. In addition, three pumps used in industry were scaled and evaluated with the gear generation algorithm for comparison. The scaled industry pumps were all close to the Pareto curve, but the optimized designs offer a slight kinematic advantage, which demonstrates the usefulness of the proposed gerotor design method.

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