scholarly journals The Improvement of Quantum Genetic Algorithm and Its Application on Function Optimization

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Huaixiao Wang ◽  
Jianyong Liu ◽  
Jun Zhi ◽  
Chengqun Fu
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problems ◽  
Evolutionary Process ◽  
Optimal Solution ◽  
Function Optimization ◽  
The Self ◽  
Improved Method ◽  
Simulation Test ◽  
Quantum Genetic Algorithm ◽  
Mutation Operation

To accelerate the evolutionary process and increase the probability to find the optimal solution, the following methods are proposed to improve the conventional quantum genetic algorithm: an improved method to determine the rotating angle, the self-adaptive rotating angle strategy, adding the quantum mutation operation and quantum disaster operation. The efficiency and accuracy to search the optimal solution of the algorithm are greatly improved. Simulation test shows that the improved quantum genetic algorithm is more effective than the conventional quantum genetic algorithm to solve some optimization problems.

scholarly journals Optimization of Network Coding Resources Based on Improved Quantum Genetic Algorithm

Photonics ◽  
2021 ◽  
Vol 8 (11) ◽  
pp. 502
Author(s):  
Tianyang Liu ◽  
Qiang Sun ◽  
Huachun Zhou ◽  
Qi Wei
Keyword(s):  
Genetic Algorithm ◽  
Network Coding ◽  
Topological Structure ◽  
Rotation Angle ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Resource Optimization ◽  
Quantum Genetic Algorithm ◽  
Adjustment Mechanism ◽  
Adaptive Adjustment

The problem of network coding resource optimization with a known topological structure is NP-hard. Traditional quantum genetic algorithms have the disadvantages of slow convergence and difficulty in finding the optimal solution when dealing with this problem. To overcome these disadvantages, this paper proposes an adaptive quantum genetic algorithm based on the cooperative mutation of gene number and fitness (GNF-QGA). This GNF-QGA adopts the rotation angle adaptive adjustment mechanism. To avoid excessive illegal individuals, an illegal solution adjustment mechanism is added to the GNF-QGA. A solid demonstration was provided that the proposed algorithm has a fast convergence speed and good optimization capability when solving network coding resource optimization problems.

scholarly journals Hybrid Genetic Algorithm and Simulated Annealing for Function Optimization

2017 ◽  
Vol 1 (2) ◽  
pp. 82 ◽  
Author(s):  
Tirana Noor Fatyanosa ◽  
Andreas Nugroho Sihananto ◽  
Gusti Ahmad Fanshuri Alfarisy ◽  
M Shochibul Burhan ◽  
Wayan Firdaus Mahmudy
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Optimization Problems ◽  
Linear Optimization ◽  
Hybrid Genetic Algorithm ◽  
Optimal Solution ◽  
Function Optimization ◽  
Non Linear ◽  
Speed Up ◽  
Linear Problems

The optimization problems on real-world usually have non-linear characteristics. Solving non-linear problems is time-consuming, thus heuristic approaches usually are being used to speed up the solution’s searching. Among of the heuristic-based algorithms, Genetic Algorithm (GA) and Simulated Annealing (SA) are two among most popular. The GA is powerful to get a nearly optimal solution on the broad searching area while SA is useful to looking for a solution in the narrow searching area. This study is comparing performance between GA, SA, and three types of Hybrid GA-SA to solve some non-linear optimization cases. The study shows that Hybrid GA-SA can enhance GA and SA to provide a better result

The Application of Genetic Algorithm on Optimization Problem for Mayonnaise Compositions

2011 ◽  
Vol 480-481 ◽  
pp. 219-224
Author(s):  
Zhi Yang Luo ◽  
Hong Xia Zhao ◽  
Xin Yuan ◽  
Yuan Li
Keyword(s):  
Genetic Algorithm ◽  
Population Size ◽  
Mutation Rate ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Optimization Methods ◽  
Function Optimization ◽  
Solve Optimization Problem ◽  
Iteration Number

For some function optimization problems of non-linear, multi-model and multi-objective, they are difficult to solve by other optimization methods, however, genetic algorithm is easy to find good results, so a kind of optimization problem for mayonnaise compositions based on genetic algorithm is introduced. This termination condition is selected according to the iteration number of maximum generation, the optimal solution of last generation in the evolution is the final result with genetic algorithm to solve optimization problem. The population size is 20, crossover rate is 0.7, and mutation rate is 0.04. Via the evolution of 100 generations, the optimization solution is gotten, which has certain guiding significance for the production.

scholarly journals An Improved Teaching-Learning-Based Optimization with the Social Character of PSO for Global Optimization

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Feng Zou ◽  
Debao Chen ◽  
Jiangtao Wang
Keyword(s):  
Optimization Problems ◽  
Global Solutions ◽  
Function Optimization ◽  
Improved Method ◽  
Social Character ◽  
The Social ◽  
Teaching Learning Based Optimization ◽  
The Mean ◽  
The Individual ◽  
Teaching Learning

An improved teaching-learning-based optimization with combining of the social character of PSO (TLBO-PSO), which is considering the teacher’s behavior influence on the students and the mean grade of the class, is proposed in the paper to find the global solutions of function optimization problems. In this method, the teacher phase of TLBO is modified; the new position of the individual is determined by the old position, the mean position, and the best position of current generation. The method overcomes disadvantage that the evolution of the original TLBO might stop when the mean position of students equals the position of the teacher. To decrease the computation cost of the algorithm, the process of removing the duplicate individual in original TLBO is not adopted in the improved algorithm. Moreover, the probability of local convergence of the improved method is decreased by the mutation operator. The effectiveness of the proposed method is tested on some benchmark functions, and the results are competitive with respect to some other methods.

Niche Genetic Algorithm Based on Sexual Reproduction and Multimodal Function Optimization Problem

2013 ◽  
pp. 8-16
Author(s):  
Yulong Tian ◽  
Tao Gao ◽  
Weifang Zhai ◽  
Yaying Hu ◽  
Xinfeng Li
Keyword(s):  
Genetic Algorithm ◽  
Sexual Reproduction ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Evolutionary Optimization ◽  
Function Optimization ◽  
Premature Convergence ◽  
Multimodal Functions ◽  
Multimodal Function Optimization ◽  
Multimodal Function

In this paper, a genetic algorithm with sexual reproduction and niche selection technology is proposed. Simple genetic algorithm has been successfully applied to many evolutionary optimization problems. But there is a problem of premature convergence for complex multimodal functions. To solve it, the frame and realization of niche genetic algorithm based on sexual reproduction are presented. Age and sexual structures are given to the individuals referring the sexual reproduction and “niche” phenomena, importing the niche selection technology. During age and sexual operators, different evolutionary parameters are given to the individuals with different age and sexual structures. As a result, this genetic algorithm can combat premature convergence and keep the diversity of population. The testing for Rastrigin function and Shubert function proves that the niche genetic algorithm based on sexual reproduction is effective.

Genetic Algorithm and Other Meta-Heuristics

2005 ◽  
pp. 144-173 ◽  
Author(s):  
Bernard K.S. Cheung
Keyword(s):  
Genetic Algorithm ◽  
Large Scale ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Schema Theory ◽  
Heuristic Methods ◽  
Optimal Solutions ◽  
Corporate Globalization ◽  
Global Optimal ◽  
Mutation And Selection

Genetic algorithms have been applied in solving various types of large-scale, NP-hard optimization problems. Many researchers have been investigating its global convergence properties using Schema Theory, Markov Chain, etc. A more realistic approach, however, is to estimate the probability of success in finding the global optimal solution within a prescribed number of generations under some function landscapes. Further investigation reveals that its inherent weaknesses that affect its performance can be remedied, while its efficiency can be significantly enhanced through the design of an adaptive scheme that integrates the crossover, mutation and selection operations. The advance of Information Technology and the extensive corporate globalization create great challenges for the solution of modern supply chain models that become more and more complex and size formidable. Meta-heuristic methods have to be employed to obtain near optimal solutions. Recently, a genetic algorithm has been reported to solve these problems satisfactorily and there are reasons for this.

Quantum genetic algorithm to evolve controllers for self-reconfigurable modular robots

2020 ◽  
Vol 17 (3) ◽  
pp. 427-435
Author(s):  
Mohamed Khalil Mezghiche ◽  
Noureddine Djedi
Keyword(s):  
Genetic Algorithms ◽  
Degrees Of Freedom ◽  
Optimization Problems ◽  
The Self ◽  
Modular Robots ◽  
Modular Robot ◽  
Adaptive Locomotion ◽  
Content Type ◽  
Quantum Genetic Algorithm ◽  
Neural Controllers

Purpose The purpose of this study is to explore using real-observation quantum genetic algorithms (RQGAs) to evolve neural controllers that are capable of controlling a self-reconfigurable modular robot in an adaptive locomotion task. Design/methodology/approach Quantum-inspired genetic algorithms (QGAs) have shown their superiority against conventional genetic algorithms in numerous challenging applications in recent years. The authors have experimented with several QGAs variants and real-observation QGA achieved the best results in solving numerical optimization problems. The modular robot used in this study is a hybrid simulated robot; each module has two degrees of freedom and four connecting faces. The modular robot also possesses self-reconfiguration and self-mobile capabilities. Findings The authors have conducted several experiments using different robot configurations ranging from a single module configuration to test the self-mobile property to several disconnected modules configuration to examine self-reconfiguration, as well as snake, quadruped and rolling track configurations. The results demonstrate that the robot was able to perform self-reconfiguration and produce stable gaits in all test scenarios. Originality/value The artificial neural controllers evolved using the real-observation QGA were able to control the self-reconfigurable modular robot in the adaptive locomotion task efficiently.

The Optimization of Precision Milling Parameters for Titanium Based on Genetic Algorithm

2012 ◽  
Vol 472-475 ◽  
pp. 1078-1081
Author(s):  
Wen Sheng Zhang ◽  
Hong Xiang Wang ◽  
Hong Du ◽  
Tao Xu
Keyword(s):  
Genetic Algorithm ◽  
Production Efficiency ◽  
Optimal Solution ◽  
Minimum Cost ◽  
Cutting Parameters ◽  
Function Optimization ◽  
Cnc Machining ◽  
Production Costs ◽  
Milling Parameters ◽  
Low Efficiency

The genetic algorithm converges faster compared with the traditional optimization algorithm, the global optimal solution can be quickly obtained and it is very effective for multi-peak function optimization. A milling process parameter optimization model is established for titanium based on genetic algorithm in this paper, the relevant constraints is considered and the optional titanium milling parameters is achieved based on the targets of maximum production efficiency and minimum cost, utilizing MATLAB optimization software to program, the best combination of cutting parameters is got finally. Experimental results show that the cutting efficiency and production costs are significantly improved with the optimized cutting parameters, so that the defects of low efficiency in CNC machining resulting in relying on experienced cutting parameters is overcome.

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