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.

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.

Optimal Design Parameters of Cab’s Isolation System for Vibratory Roller Using a Multi-Objective Genetic Algorithm

2018 ◽  
Vol 875 ◽  
pp. 105-112 ◽  
Author(s):  
Van Quynh Le ◽  
Khac Tuan Nguyen
Keyword(s):  
Genetic Algorithm ◽  
Optimal Design ◽  
Dynamic Model ◽  
Nonlinear Dynamic ◽  
Ride Comfort ◽  
Design Parameters ◽  
Nonlinear Dynamic Model ◽  
Nsga Ii ◽  
Isolation System ◽  
Multi Objective

In order to improve the vibratory roller ride comfort, a multi-objective optimization method based on the improved genetic algorithm NSGA-II is proposed to optimize the design parameters of cab’s isolation system when vehicle operates under the different conditions. To achieve this goal, 3D nonlinear dynamic model of a single drum vibratory roller was developed based on the analysis of the interaction between vibratory roller and soil. The weighted r.m.s acceleration responses of the vertical driver’s seat, pitch and roll angle of the cab are chosen as the objective functions. The optimal design parameters of cab’s isolation system are indentified based on a combination of the vehicle nonlinear dynamic model of Matlab/Simulink and the NSGA - II genetic algorithm method. The results indicate that three objective function values are reduced significantly to improve vehicle ride comfort.

EFFICIENCY INCREASE OF MULTI-OBJECTIVE GENETIC ALGORITHMS USING THE CLUSTERIZATION OF A POPULATION IN THE VARIABLE SPACE

2019 ◽  
pp. 13-20
Author(s):  
P. V. Kazakov
Keyword(s):  
Genetic Algorithm ◽  
Benchmark Problems ◽  
Additional Parameter ◽  
Nsga Ii ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Fitness Value ◽  
Efficiency Increase ◽  
Variable Space ◽  
Special Rule

The paper introduces a new manner for improving of obtained by MOGA (Multi-Objective Genetic Algorithm) solutions. It is based on the concept of dividing the population into set of clusters according to solutions similarity. In different of most MOGA the clusterization of population is implemented in the variable space, enables to enhance diversity of population and to increase the number of non-dominated solutions. The special procedures for the clustering of current population and copying the clusters in the next population were developed. The dominance principal by fitness-value is used for clustering. The number of clusters depends on additional parameter the radius of cluster’s hypersphere that is determined experimentally. By the special rule the individuals corresponded to centroids of clusters are copied in the new population. The clusters are recalculated for every population. The influence of the radius cluster to the number of non-dominated solutions variation was studied. The cluster modification should be integrated into any multi-objective genetic algorithm. By the analytical evaluation has been studied, this MOGA modification has additional computationally complexity from linear to quadratic. In experiments it was tested with the evolutionary algorithms SPEA2, NSGA-II on the special benchmark problems (DTLZ) with a various number of criteria using the set of performance indices. The used clustering in the variable space algorithms were achieved a better distribution and convergence to the true Paretofront in some cases.

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