Multiobjective Optimization for Dynamic Umbilical Installation Using Non-Dominated Sorting Genetic Algorithm

2011 ◽  
Author(s):  
Aijun Wang ◽  
Hezhen Yang ◽  
Huajun Li
Keyword(s):  
Genetic Algorithm ◽  
Multiobjective Optimization ◽  
Cost Effective ◽  
Optimization Strategy ◽  
Approximation Model ◽  
Dynamic Nature ◽  
Nsga Ii ◽  
Pareto Solution ◽  
Dynamic Analyses ◽  
Optimal Deployment

This paper presents a method of multiobjective optimization based on approximation model for dynamic umbilical installation. The optimization aims to find out the most cost effective size, quantity and location of buoyancy modules for umbilical installation. Due to the highly geometrically nonlinearity and highly responsive dynamic nature in deepwater, dynamic umbilical analysis is very complex and time-consuming. Approximation Model constructed by design of experiment (DOE) sampling is utilized to solve this problem. Non-linear dynamic analyses considering environmental loadings are executed on these sample points from DOE. Non-dominated Sorting Genetic Algorithm (NSGA-II) is employed to obtain the Pareto solution set through an evolutionary optimization process. The optimization results indicate this optimization strategy with approximation model is valid, and provide the optimal deployment way of buoyancy modules.

Applications of Multi-Objective Genetic Algorithm in Optimizing Steering Performances for Submersible

2013 ◽  
Vol 756-759 ◽  
pp. 3136-3140
Author(s):  
Zhuo Yi Yang ◽  
Yong Jie Pang ◽  
Shao Lian Ma
Keyword(s):  
Genetic Algorithm ◽  
Optimal Design ◽  
Approximation Model ◽  
Horizontal Movement ◽  
Nsga Ii ◽  
Hydrodynamic Coefficient ◽  
Pareto Solution ◽  
Multi Objective ◽  
Minimum Drag ◽  
Multi Objective Genetic Algorithm

Multi-objective arithmetic NSGA-II based on Pareto solution is investigated to deal with integrated optimal design of speedability and manoeuvre performances for submersible. Approximation model of resistance for serial revolving shape is constructed by hydrodynamic numerical calculations. The appraisement criterions of stability and mobility are calculated from linear equation of horizontal movement by estimating hydrodynamic coefficient of submersible. After optimization, the scattered Pareto solution of drag and turning diameter are gained, and from the solutions designer can select the reasonable one based on the actual requirement. The Pareto solution can ensure the minimum drag in this manoeuvre performance or the best manoeuvre performance in this drag value.

scholarly journals A Multiobjective Optimization Model for Continuous Allocation of Emergency Rescue Materials

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Yonghong Liu ◽  
Yucheng Li ◽  
De Huang
Keyword(s):  
Multiobjective Optimization ◽  
Optimization Model ◽  
Supply And Demand ◽  
Economic Losses ◽  
Allocation Scheme ◽  
Nsga Ii ◽  
Pareto Solution ◽  
Emergency Relief ◽  
Rescue Work ◽  
Emergency Rescue

Emergency rescue operations play a vital role in alleviating human suffering, reducing casualties, and cutting down economic losses. One key aspect in the management of these operations is the rational allocation of emergency relief materials, where the allocation is continuous, dynamic, and concurrent. This allocation should be made not only to minimize the emergency rescue losses, but also to reduce the cost of emergency rescue work. A reasonable and effective allocation scheme for emergency relief materials can be established to adapt to the continuity, dynamics, and concurrency of material distribution. In this work, we propose a multiobjective optimization model of emergency material allocation with continuous time-varying supply and demand constraints, where the objective is to minimize the losses and the economic cost incurred by the emergency rescue operations. The constrained optimization problem is handled through sequential unconstrained minimization techniques, and the multiobjective optimization is carried out by the fast nondominated sorting genetic algorithm (NSGA-II) with an elite strategy to obtain a Pareto solution set with fairness and balance of loss and cost. The loss and cost associated with the Pareto frontier are employed to find an appropriate noninferior solution and its corresponding material allocation scheme. We verify through several simulations the model feasibility and the effectiveness of the proposed method, which can provide decision support for continuous material allocation in emergency rescue operations.

scholarly journals A Multiobjective Genetic Algorithm Based on a Discrete Selection Procedure

2015 ◽  
Vol 2015 ◽  
pp. 1-17 ◽  
Author(s):  
Qiang Long ◽  
Changzhi Wu ◽  
Xiangyu Wang ◽  
Lin Jiang ◽  
Jueyou Li
Keyword(s):  
Genetic Algorithm ◽  
Multiobjective Optimization ◽  
Optimization Problems ◽  
Selection Procedure ◽  
Population Based ◽  
Pareto Solution ◽  
Multiobjective Genetic Algorithm ◽  
Trade Offs ◽  
Multiobjective Optimization Problems ◽  
Optimum Order

Multiobjective genetic algorithm (MOGA) is a direct search method for multiobjective optimization problems. It is based on the process of the genetic algorithm; the population-based property of the genetic algorithm is well applied in MOGAs. Comparing with the traditional multiobjective algorithm whose aim is to find a single Pareto solution, the MOGA intends to identify numbers of Pareto solutions. During the process of solving multiobjective optimization problems using genetic algorithm, one needs to consider the elitism and diversity of solutions. But, normally, there are some trade-offs between the elitism and diversity. For some multiobjective problems, elitism and diversity are conflicting with each other. Therefore, solutions obtained by applying MOGAs have to be balanced with respect to elitism and diversity. In this paper, we propose metrics to numerically measure the elitism and diversity of solutions, and the optimum order method is applied to identify these solutions with better elitism and diversity metrics. We test the proposed method by some well-known benchmarks and compare its numerical performance with other MOGAs; the result shows that the proposed method is efficient and robust.

Multiscale Design and Multiobjective Optimization of Orthopaedic Cellular Hip Implants

2011 ◽  
Author(s):  
Sajad Arabnejad Khanoki ◽  
Damiano Pasini
Keyword(s):  
Finite Element ◽  
Bone Resorption ◽  
Multiobjective Optimization ◽  
Relative Density ◽  
Homogeneous Medium ◽  
Unit Cell ◽  
Interface Stress ◽  
Optimization Strategy ◽  
Interface Failure ◽  
Nsga Ii

A multiscale design and multiobjective optimization procedure is developed to design a new type of graded cellular hip implant. We assume that the prosthesis design domain is occupied by a unit cell representing the building block of the implant. An optimization strategy seeks the best geometric parameters of the unit cell to minimize bone resorption and interface failure, two conflicting objective functions. Using the asymptotic homogenization method, the microstructure of the implant is replaced by a homogeneous medium with an effective constitutive tensor. This tensor is used to construct the stiffness matrix for the finite element modeling (FEM) solver that calculates the value of each objective function at each iteration. As an example, a 2D finite element model of a left implanted femur is developed. The relative density of the lattice material is the variable of the multiobjective optimization, which is solved through the non-dominated sorting genetic algorithm II (NSGA-II). The set of optimum relative density distributions is determined to minimize concurrently interface stress distribution and bone loss mass. The results show that the amount of bone resorption and the maximum value of interface stress can be reduced by over 70% and 50%, respectively, when compared to current fully dense titanium stem.

Rapid Preliminary Design of Floating Offshore Structures Using a Modified Genetic Algorithm

2007 ◽  
Author(s):  
Michael J. Perry ◽  
John E. Halkyard ◽  
C. G. Koh
Keyword(s):  
Genetic Algorithm ◽  
Cost Effective ◽  
Offshore Structures ◽  
Preliminary Design ◽  
Optimization Strategy ◽  
Empirical Estimates ◽  
Wind Speed Maximum ◽  
Design Wind Speed ◽  
Truss Spar ◽  
Weight Design

Preliminary design of floating offshore structures involves determining structural dimensions able to provide sufficient buoyancy to carry the required topside, at the lowest possible cost, while satisfying various stability, strength, installation, and response requirements. A novel optimization strategy, capable of carrying out the preliminary design of floating offshore structures, is presented in this paper. The genetic algorithm based strategy searches within prescribed parameter limits for the most cost effective design, while ensuring the design conforms to the constraints given. The design of a truss spar is used to illustrate how the strategy can be applied. The topside weight, design wind speed, maximum wave height, etc are input along with constraints such as, maximum draft at floatoff, maximum heel angle, allowable stress in the truss and limits on pitch and heave period and response. Using empirical estimates for hull weights and simplified response calculations, the strategy is then able to rapidly determine parameters such as hull diameter, hard tank depth, length of keel tank, total length and truss leg diameter such that the total cost of the structure is minimized. The strategy allows for the preliminary design phase to be completed in only a few seconds, while providing initial weight and cost estimates.

Study of the Non-Dominated Sorting Genetic Algorithm for the Optimal Structural Parameter of the Tooth-Arrangement Multi-Fingered Dextrous Hand

2013 ◽  
Vol 756-759 ◽  
pp. 4082-4089
Author(s):  
Zhan Li Li ◽  
Xiang Ting He
Keyword(s):  
Genetic Algorithm ◽  
Parameter Optimization ◽  
Experimental Results ◽  
Structural Parameter ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Pareto Solution ◽  
Multi Objective ◽  
Dextrous Hand

Firstly, the structural parameter optimization of the tooth-arrangement multi-fingered dextrous hand is studied. Secondly, as to the shortcomings that the Pareto solution of multi-objective optimization was distributed unevenly in NSGA-II, a non-dominated sorting genetic algorithm based on immune principle is proposed. Lastly, the structural parameter of the medical tooth-arrangement multi-fingered dextrous hand is optimized using the proposed algorithm. The experimental results show that this algorithm can optimize structural parameter of tooth-arrangement multi-fingered dextrous hand very well.

scholarly journals Pareto Optimization of a Half Car Passive Suspension Model Using a Novel Multiobjective Heat Transfer Search Algorithm

2017 ◽  
Vol 2017 ◽  
pp. 1-17 ◽  
Author(s):  
Vimal Savsani ◽  
Vivek Patel ◽  
Bhargav Gadhvi ◽  
Mohamed Tawhid
Keyword(s):  
Heat Transfer ◽  
Genetic Algorithm ◽  
Multiobjective Optimization ◽  
Pareto Front ◽  
Search Algorithm ◽  
Extreme Points ◽  
Search Technique ◽  
Nsga Ii ◽  
Linear Ordinary Differential Equations ◽  
Nondominated Sorting

Most of the modern multiobjective optimization algorithms are based on the search technique of genetic algorithms; however the search techniques of other recently developed metaheuristics are emerging topics among researchers. This paper proposes a novel multiobjective optimization algorithm named multiobjective heat transfer search (MOHTS) algorithm, which is based on the search technique of heat transfer search (HTS) algorithm. MOHTS employs the elitist nondominated sorting and crowding distance approach of an elitist based nondominated sorting genetic algorithm-II (NSGA-II) for obtaining different nondomination levels and to preserve the diversity among the optimal set of solutions, respectively. The capability in yielding a Pareto front as close as possible to the true Pareto front of MOHTS has been tested on the multiobjective optimization problem of the vehicle suspension design, which has a set of five second-order linear ordinary differential equations. Half car passive ride model with two different sets of five objectives is employed for optimizing the suspension parameters using MOHTS and NSGA-II. The optimization studies demonstrate that MOHTS achieves the better nondominated Pareto front with the widespread (diveresed) set of optimal solutions as compared to NSGA-II, and further the comparison of the extreme points of the obtained Pareto front reveals the dominance of MOHTS over NSGA-II, multiobjective uniform diversity genetic algorithm (MUGA), and combined PSO-GA based MOEA.

Application of NSGA-II and PGA in Network Planning of the Distributed Systems

2012 ◽  
Vol 433-440 ◽  
pp. 4888-4892
Author(s):  
Zhi Keng Li
Keyword(s):  
Genetic Algorithm ◽  
Network Planning ◽  
Pareto Optimum ◽  
Distributed Network ◽  
Genetic Operators ◽  
Optimal Model ◽  
Nsga Ii ◽  
Pareto Solution ◽  
Proposed Model ◽  
Planning Scheme

The issue of power reliability in a middle-voltage distributed network is now emerging as an international concern. Therefore, in this paper, an optimal model with two objectives, reliability and economy, for network planning of a distributed system is established. Based on the Pareto optimum theory, the Non-dominated Sorting Genetic algorithm II (NSGA-II), which is combined with the specific genetic operators in Partheno-genetic algorithm (PGA), is used to solve the proposed model. By the obtained well-proportioned Pareto solution set, the final network planning scheme can be found according to different real engineering conditions, thus different demands in engineering can be satisfied. A typical example is used to verify the proposed model and algorithm effective.

MULTIOBJECTIVE OPTIMIZATION OF PROCESS PLANT USING GENETIC ALGORITHM

2006 ◽  
Vol 06 (03) ◽  
pp. 315-327 ◽  
Author(s):  
L. GOVINDARAJAN ◽  
T. KARUNANITHI
Keyword(s):  
Genetic Algorithm ◽  
Optimal Design ◽  
Multiobjective Optimization ◽  
Large Scale ◽  
Raw Material ◽  
Fine Tuning ◽  
Design Parameters ◽  
Nsga Ii ◽  
Process Plant ◽  
Mathematical Techniques

The optimal design of large-scale process plant is difficult due to the presence of Pareto sets or nondominated solutions. Many conventional and advanced mathematical techniques had been adopted which have their own limitations in solving the complex design problem. In this paper, nondominant-sorted genetic algorithms NSGA and NSGA-II have been adopted for the optimal design of complex Williams–Otto model process plant. The plant consists of a reactor, separation system consisting of heat exchanger, decanter and distillation column. Multiobjective optimization is used to maximize the profit, i.e. the return on investment, to maintain lesser use of costlier raw material and lesser disposal of the waste byproducts. So NSGA-II is employed in this study as an effective replacement for NSGA, classical genetic algorithm, conventional and traditional methods of optimization in solving multiobjective process design problems and to achieve fine-tuning of variables in determining Pareto optimal design parameters. NSGA-II method finding global optimal front has a significant effect on the design of control system for the real time and continuous robust control of complex process plant as each target vector provides proper direction and drives the process to multiobjective optimum conditions.

scholarly journals Multiobjective Optimization Model for Profile Design of Hump Distributing Zone

2017 ◽  
Vol 2017 ◽  
pp. 1-11
Author(s):  
Hongliang Zhang ◽  
Jing Yang ◽  
Taoyuan Yang
Keyword(s):  
Multiobjective Optimization ◽  
Optimization Model ◽  
Improved Genetic Algorithm ◽  
Special Equipment ◽  
Nsga Ii ◽  
Pareto Solution ◽  
Railway Freight ◽  
Contrast Analysis ◽  
Profile Design

Railway freight trains consist of many cars heading to different destinations. Hump is the special equipment that distributes cars with different destinations to different tracks in a marshalling station. In recent years, with the development of Chinese freight car technology, the axle load has risen from 21 ton to 23 ton and will rise to 27 ton in the future. Many rolling problems appear in the hump distributing zone with the application of 23-ton axle load cars, which will be exacerbated by 27-ton axle load cars. This paper proposes a multiobjective optimization model based on the angle of the hump profile design with minimizing weighted accumulating rolling time (WART) and hump height as optimization goals and uses the improved genetic algorithm NSGA-II to determine a solution. In case study, Pareto solution set is obtained, and the contrast analysis with traditional method is made.

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