A niche quantum genetic algorithm used in multi-peak function optimization

2010 ◽  
Author(s):  
Hong-mei Ni ◽  
Wei-gang Wang
Keyword(s):  
Genetic Algorithm ◽  
Function Optimization ◽  
Quantum Genetic Algorithm ◽  
Peak Function

Research on Extreme Points Optimizing of Nonlinear Multi-Peak Function Based on Genetic Algorithm

2010 ◽  
Vol 121-122 ◽  
pp. 304-308
Author(s):  
Lu Gang Yang
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Extreme Points ◽  
Function Optimization ◽  
Experimental Result ◽  
Genetic Optimization ◽  
Binary Coding ◽  
Coding Method ◽  
Peak Function ◽  
Better Than

In the application of Genetic Algorithm (GA) to solve the function optimization problem, different encoding methods have different effect on performance of GA. Aiming at the global optimization problem of a class of nonlinear multi-peak function, the paper utilized binary coding and floating coding methods for genetic optimization and analyzed their performance. The experimental result of four kinds of typical nonlinear multi-peak function showed that under the precondition of given genetic operator, the optimizing performance of floating coding method to optimize nonlinear multi-peak function with isolated extreme points is less that the binary coding. The tuning ability of floating coding is stronger. As to the ordinary multi-peak function, the search affect is better than binary coding.

A Novel Quantum Genetic Algorithm for Continuous Function Optimization

2013 ◽  
Vol 816-817 ◽  
pp. 907-914
Author(s):  
Hao Li ◽  
Shi Yong Li
Keyword(s):  
Genetic Algorithm ◽  
Continuous Function ◽  
Global Convergence ◽  
Local Search ◽  
Rotation Angle ◽  
Binary Solution ◽  
Function Optimization ◽  
Quantum Genetic Algorithm ◽  
Function Extremum ◽  
Angle Of Rotation

In this paper, a novel quantum genetic algorithm is proposed. This algorithm compares the probability expectation of the quantum chromosome with the best binary solution to determine rotation angle of rotation gate. Different individual in population evolve with different rate to complete local search and global search simultaneously. Hε gate is used to prevent the algorithm from premature convergence. After analyzing the algorithm and its global convergence, applying this approach to the optimization of function extremum, and comparing with the simple genetic algorithm and the quantum genetic algorithm, the simulation result illustrates that the algorithm has the characteristic of quick convergence speed and high solution precision.

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.

Optimization of Wireless Body Area Networks Model in Clonal Selection Quantum Genetic Algorithm

2010 ◽  
Vol 44-47 ◽  
pp. 3125-3129
Author(s):  
Huan Wang ◽  
Li Li Guo
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithm ◽  
Clonal Selection ◽  
Body Area Networks ◽  
Wireless Body Area Networks ◽  
Function Optimization ◽  
Body Area ◽  
Quantum Genetic Algorithm ◽  
Adaptive Choice

The Wireless Body Area Networks models is not formal with the different facilities. The adaptive choice of the models is very important for each special user. In order to improve the model, the authors optimize the model of Wireless Body Area Networks with the quantum genetic algorithm based on clonal selection. The clonal selection quantum (CSQ) has shown the results of experiment by quantum genetic algorithm and immune clonal selection, and the rate and precision of quantum evolutionary algorithm have been better. It gives encouraging data results when we use the algorithm to the function optimization of Wireless Body Area Networks Model with the introduction of simulation.

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