International Conference on Natural Computation (ICNC)

A novel quantum genetic algorithm and its application

Zhang Sheng, Jiang Wanlu

Published May 1, 2012• 25 citations

Abstract

Aiming at the disadvantages of the fixed rotation angle in quantum genetic algorithm (QGA), in this paper, the particle swarm optimization (PSO) was used to update the angle of quantum rotation gate in stead of the old one that locates the angle of quantum rotation gate by a designed query table. According to the experimental results, the novel quantum genetic algorithm (NQGA) with new updating method has got a higher global search capability than common QGA. The NQGA was used to the load distribution optimization of tandem cold rolling. The comparison data show that the NQGA is an effective method for load distribution optimization of tandem cold rolling.

DOI: 10.1109/icnc.2012.6234602

Related papers

A Novel Quantum Genetic Algorithm and Its Application

Gexiang Zhang

This paper proposes a novel quantum genetic algorithm (NQGA),whose core is that the strategies of updating quantum gate using qubit phase comparison approach and adjusting search grid adaptively are introduced.NQGA is characterized by rapid convergence,good global search capability and short computing time.Test results of complex functions and application example demonstrate NQGA is superior to conventional genetic algorithms in quality and efficiency.

Dianzi xuebaoJan 1, 200449 citations

A Novel Quantum Genetic Algorithm Based on Particle Swarm Optimization Method and Its Application

Ying Ding

This paper proposes a novel quantum genetic algorithm(QGA)——PSQGA,which uses particle swarm optimization method instead of quantum gate to update the state of quantum bit.It has the advantages of particle swarm optimization and quantum genetic algorithm.A variance function is introduced to estimate the stability of the algorithm.Though the experiments of four continuous functions and combination optimization problems,as well as its application to image sparse decomposition.Compared with the improved algorithm which involved quantum gate(IQGA),the ability of finding the best solution and the stability of PSQGA are greatly improved.PSQGA has better convergent property and ability of searching more extensive space.It is fit for the solution of complex optimization problems.

Dianzi xuebaoJan 1, 200614 citations

Chaos updating rotated gates quantum-inspired genetic algorithm

Hui Chen, Zhang Jiashu, Chao Zhang

From the recent research on combinational optimization of the knapsack problem, the quantum-inspired genetic algorithm (QGA) was proved to be better than conventional genetic algorithms. To accelerate the convergence speed of the QGA, the paper proposes research issues on QGA such as Q-gate. A novel Q-gate updating algorithm called chaos updating rotated gates quantum-inspired genetic algorithm (CQGA) is proposed. An analysis of the two main characters of quantum computing and chaos is also presented. This algorithm demonstrates the convergence of the quantum genetic algorithm (QGA). Several experiments are carried out on a class of numerical and combinatorial optimization problems. The results show the updated QGA makes QGA more powerful than the previous QGA in convergence speed.

International Conference on Communications, Circuits and SystemsOct 26, 200431 citations

Progress, Application and Prospect of Quantum Genetic Algorithm

Liu Shuguang, Lin Ba

The quantum genetic algorithm (QGA) is derived from the integration of quantum computation and genetic algorithm. It is characterized with advantages like strong global optimization ability, fast convergence speed and small population size. In this paper, the principle, method and basic flow of quantum genetic algorithm (QGA) are introduced, and the research progress of QGA in recent years is reviewed, including the coding extension of theoretical basis, the innovation of operators, the rotation angle of quantum gates, the optimization of complex high-dimensional functions, and hybrid algorithms, and then he application status of QGA is also discussed. Finally, the future development direction of QGA is put forward.

IEEE International Conference on Electronics and Communication Engineering (ICECE)Dec 1, 20182 citations

Quantum genetic algorithm based on phase comparison

Hao Li

Aiming at the issue that quantum genetic algorithm is not suitable for continuons function optimization,an improved quantum genetic algorithm is proposed.This algorithm directly compares the quantum chromosomes with the current best solution to determine the rotating angle of the rotating gate.Every individual in population evolves with different rates to complete local search and global search simultaneously.Mutation operation is used to prevent the premature convergence.After analyzing the algorithm and its global convergence,this algorithm is applied to solve the function extremum and to optimize the PID controller parameters,and it is compared with the standard genetic algorithm and the quantum genetic algorithm.Experimental results illustrate that the proposed algorithm has better performance.

Systems engineering and electronicsJan 1, 20102 citations

An Improved Quantum Genetic Algorithm of Quantum Revolving Gate

Yang Guo-weia

Aiming at the problems that the Quantum Genetic Algorithm(QGA) easily falls into local extremum,an improved QGA algorithm of quantum revolving gate is proposed.In order to achieve the goal of optimization solving,quantum bit probability amplitude is applied to the chromosome encoding,and quantum rotation gate is used to chromosome updating.The theoretical derivation proves that the proposed algorithm converges with probability 1,and the algorithm converges strongly to 1?e.Comparing with double QGA,simulation results show that the algorithm increases complexity,the average time extension,but convergence times increase,convergence times of the function 1 increases from 3 to 7,and convergence times of the function 2 increases from 8 to 9.

Jisuanji gongchengJan 1, 20135 citations