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

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.

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.

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.

Implementation and Evaluation of the Image Segmentation Algorithm

2013 ◽  
Vol 411-414 ◽  
pp. 1314-1317
Author(s):  
Li Jun Chen ◽  
Yong Jie Ma
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Maximum Entropy ◽  
Entropy Method ◽  
Segmentation Algorithm ◽  
Segmentation Method ◽  
Improved Genetic Algorithm ◽  
And Performance ◽  
Standard Genetic Algorithm ◽  
Better Than

In order to achieve better image segmentation and evaluate the segmentation algorithm, a segmentation method based on 2-D maximum entropy and improved genetic algorithm is proposed in this paper, and the ultimate measurement accuracy criterion is adopted to evaluate the performance of the algorithm. The experimental results and the evaluation results show that segmentation results and performance of the proposed algorithm are both better than the segmentation method based on 2-D maximum entropy method and the standard genetic algorithm. The segmentation of the proposed algorithm is complete and spends less time; it is an effective method for image segmentation.

Improved Genetic Algorithm on Minimum Spanning Tree of Length Constraint Problem

2015 ◽  
Vol 713-715 ◽  
pp. 1737-1740
Author(s):  
Ying Ying Duan ◽  
Kang Zhou ◽  
Wen Bo Dong ◽  
Kai Shao
Keyword(s):  
Genetic Algorithm ◽  
Spanning Tree ◽  
Minimum Spanning Tree ◽  
Fitness Function ◽  
Initial Population ◽  
Improved Genetic Algorithm ◽  
Length Constraint ◽  
C Programming ◽  
Mutation Operation ◽  
Constraint Problem

The first minimum spanning tree of length constraint problem (MSTLCP) is put forward, which can not be solved by traditional algorithms. In order to solve MSTLCP, improved genetic algorithm is put forward based on the idea of global and feasible searching. In the improved genetic algorithm, chromosome is generated to use binary-encoding, and more reasonable fitness function of improved genetic algorithm is designed according to the characteristics of spanning tree and its cotree; in order to ensure the feasibility of chromosome, more succinct check function is introduced to three kinds of genetic operations of improved genetic algorithm (generation of initial population, parental crossover operation and mutation operation); three kinds of methods are used to expand searching scope of algorithm and to ensure optimality of solution, which are as follows: the strategy of preserving superior individuals is adopted, mutation operation is improved in order to enhance the randomness of the operation, crossover rate and mutation rate are further optimized. The validity and correctness of improved genetic algorithm solving MSTLCP are explained by a simulate experiment where improved genetic algorithm is implemented using C programming language. And experimental results are analyzed: selection of population size and iteration times determines the efficiency and precision of the simulate experiment.

An Improved Genetic Algorithm Based on Cellular Automata

2013 ◽  
Vol 340 ◽  
pp. 727-731
Author(s):  
Hong Tang ◽  
Yun Sheng Ge ◽  
Xiao Hai Pan ◽  
Shu Feng We
Keyword(s):  
Genetic Algorithm ◽  
Cellular Automata ◽  
Search Time ◽  
Target Function ◽  
Convergence Speed ◽  
Optimal Result ◽  
Improved Genetic Algorithm ◽  
Simple Genetic Algorithm

In order to overcome the drawbacks of Simple Genetic Algorithm such as cannot get the most optimal result, low convergence speed et al. Cellular Simple Genetic Algorithm-a new genetic algorithm based on Cellular Automata-is presented in this paper. Compared with the Simple Genetic Algorithm, the experiment results show the Cellular Simple Genetic Algorithm has remarkable advantages in following aspects: reducing the search-time and improving the precise of target function.

Fuzzy-Robust Design Optimization of Dimension Tolerance Using the Improved Genetic Algorithm

2011 ◽  
Vol 55-57 ◽  
pp. 1502-1505
Author(s):  
Bo Zhong
Keyword(s):  
Genetic Algorithm ◽  
Design Optimization ◽  
Robust Design ◽  
Treatment Method ◽  
Robust Design Optimization ◽  
Continuous Variables ◽  
Discrete Variables ◽  
Improved Genetic Algorithm ◽  
Design Problems ◽  
Standard Genetic Algorithm

The standard genetic algorithm is improved by introducing the engineering treatment method of design vector in order to solve the optimization problem with mixed-discrete variables. A program of improved genetic algorithm has been designed. It can be used to solve the optimal design problems with continuous variables, discrete variables or mixed-discrete variables. For a dimension chain, the fuzzy-robust design of dimension tolerance is discussed and a model of fuzzy-robust design optimization is established. The solution of established model is achieved by using the improved genetic algorithm and the robustness of the dimension tolerance has been improved. The example shows that the proposed method is effective in engineering design.

Maximization Network Throughput Based on Improved Genetic Algorithm and Network Coding for Optical Multicast Networks

2017 ◽  
Vol 39 (1) ◽  
Author(s):  
Chengying Wei ◽  
Cuilian Xiong ◽  
Huanlin Liu
Keyword(s):  
Genetic Algorithm ◽  
Network Coding ◽  
Wavelength Division Multiplexing ◽  
Multicast Routing ◽  
Fitness Function ◽  
Wdm Networks ◽  
Network Throughput ◽  
Improved Genetic Algorithm ◽  
Wavelength Division ◽  
Optical Multicast

AbstractMaximal multicast stream algorithm based on network coding (NC) can improve the network’s throughput for wavelength-division multiplexing (WDM) networks, which however is far less than the network’s maximal throughput in terms of theory. And the existing multicast stream algorithms do not give the information distribution pattern and routing in the meantime. In the paper, an improved genetic algorithm is brought forward to maximize the optical multicast throughput by NC and to determine the multicast stream distribution by hybrid chromosomes construction for multicast with single source and multiple destinations. The proposed hybrid chromosomes are constructed by the binary chromosomes and integer chromosomes, while the binary chromosomes represent optical multicast routing and the integer chromosomes indicate the multicast stream distribution. A fitness function is designed to guarantee that each destination can receive the maximum number of decoding multicast streams. The simulation results showed that the proposed method is far superior over the typical maximal multicast stream algorithms based on NC in terms of network throughput in WDM networks.

scholarly journals A new genetic algorithm specifically based on mutation and selection

2007 ◽  
Vol 39 (01) ◽  
pp. 141-161 ◽  
Author(s):  
L. Rigal ◽  
L. Truffet
Keyword(s):  
Genetic Algorithm ◽  
Selection Pressure ◽  
Large Deviation ◽  
Fitness Function ◽  
Critical Value ◽  
Gradient Algorithm ◽  
Selection Algorithm ◽  
Local Maxima ◽  
Selection Operator ◽  
Mutation And Selection

In this paper we propose a new genetic algorithm specifically based on mutation and selection in order to maximize a fitness function. This mutation-selection algorithm behaves as a gradient algorithm which converges to local maxima. In order to obtain convergence to global maxima we propose a new algorithm which is built by randomly perturbing the selection operator of the gradient-like algorithm. The perturbation is controlled by only one parameter: that which allows the selection pressure to be governed. We use the Markov model of the perturbed algorithm to prove its convergence to global maxima. The arguments used in the proofs are based on Freidlin and Wentzell's (1984) theory and large deviation techniques also applied in simulated annealing. Our main results are that (i) when the population size is greater than a critical value, the control of the selection pressure ensures the convergence to the global maxima of the fitness function, and (ii) the convergence also occurs when the population is the smallest possible, i.e. 1.

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