scholarly journals Improved Genetic Algorithm Tuning Controller Design for Autonomous Hovercraft

Processes ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 66 ◽  
Author(s):  
Huu Khoa Tran ◽  
Hoang Hai Son ◽  
Phan Van Duc ◽  
Tran Thanh Trang ◽  
Hoang-Nam Nguyen
Keyword(s):  
Genetic Algorithm ◽  
Controller Design ◽  
Fitness Function ◽  
Tracking Error ◽  
Biological Evolution ◽  
Operation Time ◽  
Design Criterion ◽  
Improved Genetic Algorithm ◽  
Fuzzy Pid Control ◽  
Speed Up

By mimicking the biological evolution process, genetic algorithm (GA) methodology has the advantages of creating and updating new elite parameters for optimization processes, especially in controller design technique. In this paper, a GA improvement that can speed up convergence and save operation time by neglecting chromosome decoding step is proposed to find the optimized fuzzy-proportional-integral-derivative (fuzzy-PID) control parameters. Due to minimizing tracking error of the controller design criterion, the fitness function integral of square error (ISE) was employed to utilize the advantages of the modified GA. The proposed method was then applied to a novel autonomous hovercraft motion model to display the superiority to the standard GA.

Solving Vibration Control Problems Using Reduced Order Models for Flexible Structures: A Genetic Algorithm Approach

1997 ◽  
Author(s):  
Sourav Kundu ◽  
Kentaro Kamagata ◽  
Shigeru Sugino ◽  
Takeshi Minowa ◽  
Kazuto Seto
Keyword(s):  
Genetic Algorithm ◽  
Vibration Control ◽  
Controller Design ◽  
Fitness Function ◽  
Flexible Structures ◽  
Design Solution ◽  
Reduced Order Models ◽  
Reduced Order ◽  
Fitness Value ◽  
Genetic Algorithm Approach

Abstract A Genetic Algorithm (GA) based approach for solution of optimal control design of flexible structures is presented in this paper. The method for modeling flexible structures with distributed parameters as reduced-order models with lumped parameters, which has been developed previously, is employed. Due to some restrictions on controller design it is necessary to make a reduced-order model of the structure. Once the model is established the design of flexible structures is considered as a feedback search procedure where a new solution is assigned some fitness value for the GA and the algorithm iterates till some satisfactory design solution is achieved. We propose a pole assignment method to determine the evaluation (fitness) function to be used by the GA to find optimal damping ratios in passive elements. This paper demonstrates the first results of a genetic algorithm approach to solution of the vibration control problem for practical control applications to flexible tower-like structures.

scholarly journals Multiobjective Synthesis of Linear Arrays by Using an Improved Genetic Algorithm

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Bo Yang
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Fitness Function ◽  
Particle Swarm ◽  
Weight Vector ◽  
Side Lobe Level ◽  
Improved Genetic Algorithm ◽  
Pattern Synthesis ◽  
Swarm Optimization ◽  
Dynamic Weight

In this paper, an improved genetic algorithm with dynamic weight vector (IGA-DWV) is proposed for the pattern synthesis of a linear array. To maintain the diversity of the selected solution in each generation, the objective function space is divided by the dynamic weight vector, which is uniformly distributed on the Pareto front (PF). The individuals closer to the dynamic weight vector can be chosen to the new population. Binary- and real-coded genetic algorithms (GAs) with a mapping method are implemented for different optimization problems. To reduce the computation complexity, the repeat calculation of the fitness function in each generation is replaced by a precomputed discrete cosine transform matrix. By transforming the array pattern synthesis into a multiobjective optimization problem, the conflict among the side lobe level (SLL), directivity, and nulls can be efficiently addressed. The proposed method is compared with real number particle swarm optimization (RNPSO) and quantized particle swarm optimization (QPSO) as applied in the pattern synthesis of a linear thinned array and a digital phased array. The numerical examples show that IGA-DWV can achieve a high performance with a lower SLL and more accurate nulls.

An Improved Hybrid Genetic Algorithm and Performance Study

2012 ◽  
Vol 482-484 ◽  
pp. 95-98
Author(s):  
Wei Dong Ji ◽  
Ke Qi Wang
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Optimization Method ◽  
Convergence Speed ◽  
Test Results ◽  
Improved Genetic Algorithm ◽  
Performance Study ◽  
Speed Up ◽  
And Performance

Put forward a kind of the hybrid improved genetic algorithm of particle swarm optimization method (PSO) combine with and BFGS algorithm of, this method using PSO good global optimization ability and the overall convergence of BFGS algorithm to overcome the blemish of in the conventional algorithm slow convergence speed and precocious and local convergence and so on. Through the three typical high dimensional function test results show that this method not only improved the algorithm of the global search ability, to speed up the convergence speed, but also improve the quality of the solution and its reliability of optimization results.

The Attacking Dispersion Optimization in Multi Launch Rocket System Base-On Improved Genetic Algorithm

2014 ◽  
Vol 670-671 ◽  
pp. 1499-1502
Author(s):  
Wei Wang ◽  
Wei Dong Chen ◽  
Shu Qiang Zhang ◽  
Jiang Long Li ◽  
Ya En Xie
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Fitness Function ◽  
Reference Value ◽  
Weapon System ◽  
Improved Genetic Algorithm ◽  
Optimal Model ◽  
Overall Design ◽  
Mixed Coding ◽  
Rocket System

Firing dispersion of multi-launch rocket system is affected by launch sequence and firing interval significantly. Firing order and firing interval of the existing multi launch rocket system (MLRS) are optimized to improve the firing performance of the existing weapon system without changing the overall design of the weapon system. On one hand, based on optimization problem, the firing dispersion optimal model is established and the genetic algorithm is improved therefore, a sequence of mixed coding genetic algorithm is designed. On the other hand, simulation optimization of firing dispersion has been finished by the aid of fitness function which is based on the optimal model. Meanwhile, it testifies this algorithm’s validity and the simulation results can provide a certain reference value for engineering experiment.

QoS Routing Optimization Based on Improved Genetic Algorithm

2014 ◽  
Vol 998-999 ◽  
pp. 1169-1173
Author(s):  
Chang Lin He ◽  
Yu Fen Li ◽  
Lei Zhang
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Computational Complexity ◽  
Fitness Function ◽  
Qos Routing ◽  
Experimental Results ◽  
Improved Genetic Algorithm ◽  
Coding Schemes ◽  
Routing Optimization

A improved genetic algorithm is proposed to QoS routing optimization. By improving coding schemes, fitness function designs, selection schemes, crossover schemes and variations, the proposed method can effectively reduce computational complexity and improve coding accuracy. Simulations are carried out to compare our algorithm with the traditional genetic algorithms. Experimental results show that our algorithm converges quickly and is reliable. Hence, our method vastly outperforms the traditional algorithms.

Research and Implementation of Professional Quality of Personnel Evaluation System Based on Multi-Dimensional

2014 ◽  
Vol 687-691 ◽  
pp. 4725-4729
Author(s):  
Min Jiang ◽  
Ying Jiang
Keyword(s):  
Genetic Algorithm ◽  
Evaluation System ◽  
Fitness Function ◽  
Paper Test ◽  
Simulation Experiments ◽  
Automatic Group ◽  
Paper Making ◽  
Professional Quality ◽  
Speed Up

this paper uses a new genetic algorithm (New Genetic Algorithm, NGA) to implement the automatic group volume function, solving the problem that the paper is not fully considered scores distribution of knowledge points in the test paper (test dimension) in the system using traditional genetic algorithm (Genetic Algorithm, GA) to implement automatic group volume, putting forward and redefining the fitness function to speed up the convergence. Simulation experiments show that the NGA algorithm is not only efficient, but can generate a valid paper, making the paper score can on the multi-dimensional of the test as far as possible to achieve uniform distribution.

Study of Multi-Project Conflicts by IGA Based on the Improvement of Fitness Function

2013 ◽  
Vol 756-759 ◽  
pp. 2768-2773
Author(s):  
Zhi Feng Lv ◽  
Xiang Dong Ma
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Selection Methods ◽  
Improved Genetic Algorithm ◽  
Resource Conflicts ◽  
Simple Genetic Algorithm ◽  
The Family ◽  
Family Orientation ◽  
Standard Genetic Algorithm ◽  
Mutation And Selection

In the multi-project resource conflicts exist in the application of standard genetic algorithm fitness function exist "premature" problem, Genetic algorithm can not find the convergence of these issue. Based on the above issues ,an improved genetic algorithm (IGA) are appropriate, From the fitness function, mutation and selection methods to improve two aspects are described, the Improved genetic algorithm for simple genetic algorithm has the advantage of generations of each evolution, offspring parent always retains the best individual to the "high-fitness model for the ancestors of the family orientation" search out better samples, and verified through experiments the effectiveness of the algorithm

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