Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Advanced materials research

A Novel Quantum Genetic Algorithm Based on Potential

Xiao Lei Li, Fei Meng

Published Jun 1, 2012• 0 citations
Publisher

Abstract

Considering different quantum bit having different effective intensity in chromosome evolution, a novel quantum genetic algorithm based on potential is proposed. It makes the magnitude of rotation angle depending on the potential of a quantum bit. It generates the orientation of rotation angle according to the total potential of quantum bit in the chromosome. The character of quantum entangled interference based on potential is introduced. And convergence analysis and rationality analysis are implemented. Experimental test shows that, it can obtain better convergence rate and have less runtime on smaller population size and shorter chromosome length.

DOI
10.4028/www.scientific.net/amr.532-533.1434

Related papers

A Novel Quantum Genetic Algorithm for Continuous Function Optimization

Hao Li, Shi Yong Li

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.

Advanced materials research•Sep 23, 2013•0 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 electronics•Jan 1, 2010•2 citations

Evolutionary algorithm based on evaluating quantum chromosomes

Quan Hui-yun

A novel quantum-inspired evolutionary algorithm is proposed in this paper.The rotation angle is calculated through current best solution of its group and the binary individual by evaluating a quantum chromosome,and adjusts with evolution.Compared with some other quantum inspired genetic algorithms the novel algorithm shows the outstanding performance on function optimization and Knapsack problems even if the population size is 1.

Computer Engineering and Applications Journal•Jan 1, 2007•1 citation

Quantum genetic algorithm with application in fuzzy controller parameter optimization

Yuan Li-ying

A quantum genetic algorithm based on real number encoding and gradient of fitness function is proposed in this paper.Chromosomes comprised of quantum bits are updated by quantum rotating gates,and are mutated by quantum non-gate.A convenient method to choose the direction of rotation angle of quantum rotation gate is proposed.The computation formula of the rotation angle value is constructed by the gradients of fitness function.In this method,two probability amplitude of each quantum bit are regarded as two coordinate genes;each chromosome contains two chains of genes;and each chain of genes represents an optimization result.This method can accelerate convergence process and increase successful probability with the same number of chromosomes.Its application to fuzzy controller parameters optimization illustrates that the approach is superior to common quantum genetic algorithm in searching ability.

Systems engineering and electronics•Jan 1, 2007•6 citations

Quantum genetic algorithm based on multi-chain coding scheme

He Jian

In order to improve the efficiency of the quantum genetic algorithm,this paper proposed a quantum genetic algorithm based on a expanded multi-chain coding scheme.The algorithm took qubit as chromosome.Each chromosome generated multiple and parallel gene chains which were mapping to multiple optimized solutions by separating qubit into multiple and parallel genes.The expanded genes chains expanded the searching space effectively and increased evolutionary rate for quantum genetic algorithm.It introduced the dynamic adjusting rotation angle mechanism to quantum rotation gate to guide individual evolution and used quantum not-gate to prevent algorithm occurring premature convergence.The method further improved searching efficiency.In the simulation experiment,analysed the influence for the algorithm with different variation probability([0,0.1,…,0.9,1])and used different code schemes to optimize extremal function.The simulation experiment result shows that it can obviously improve the efficiency of quantum genetic algorithm by adding gene chain,and the quantum genetic algorithm based on a expanded multi-chain coding scheme is efficient.

Jisuanji yingyong yanjiu•Jan 1, 2012•2 citations

Quantum Genetic Algorithm Based on Angle Coding of 3D

Decai Huang

In order to make full use of the quantum characteristics of the quantum state in the algorithm,and improve the search efficiency,reduce storage space,a new quantum genetic algorithm called 3D-AQGA was proposed.The algorithm describes quantum bit as a pair of angles in 3D spherical coordinate,makes full use of the quantum space motion characteristics,and introduces a kind of adaptive scheme to calculate the rotation angle size and direction which not only makes the process of chromosome's update and variation simplified,but also improves quantum characteristics,storage properties and time performance of the algorithm greatly.The simulation results show that the efficiency of the algorithm and the search ability are superior to the simple genetic algorithm and common quantum genetic algorithm.

•Jan 1, 2012•1 citation
Considering different quantum bit having different effective intensity in chromosome evolution, a novel quantum genetic algorithm based on potential is proposed. It makes the magnitude of rotation angle depending on the potential of a quantum bit. It generates the orientation of rotation angle according to the total potential of quantum bit in the chromosome. The character of quantum entangled interference based on potential is introduced. And convergence analysis and rationality analysis are implemented. Experimental test shows that, it can obtain better convergence rate and have less runtime on smaller population size and shorter chromosome length.
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.
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.
A quantum genetic algorithm based on real number encoding and gradient of fitness function is proposed in this paper.Chromosomes comprised of quantum bits are updated by quantum rotating gates,and are mutated by quantum non-gate.A convenient method to choose the direction of rotation angle of quantum rotation gate is proposed.The computation formula of the rotation angle value is constructed by the gradients of fitness function.In this method,two probability amplitude of each quantum bit are regarded as two coordinate genes;each chromosome contains two chains of genes;and each chain of genes represents an optimization result.This method can accelerate convergence process and increase successful probability with the same number of chromosomes.Its application to fuzzy controller parameters optimization illustrates that the approach is superior to common quantum genetic algorithm in searching ability.
In order to improve the efficiency of the quantum genetic algorithm,this paper proposed a quantum genetic algorithm based on a expanded multi-chain coding scheme.The algorithm took qubit as chromosome.Each chromosome generated multiple and parallel gene chains which were mapping to multiple optimized solutions by separating qubit into multiple and parallel genes.The expanded genes chains expanded the searching space effectively and increased evolutionary rate for quantum genetic algorithm.It introduced the dynamic adjusting rotation angle mechanism to quantum rotation gate to guide individual evolution and used quantum not-gate to prevent algorithm occurring premature convergence.The method further improved searching efficiency.In the simulation experiment,analysed the influence for the algorithm with different variation probability([0,0.1,…,0.9,1])and used different code schemes to optimize extremal function.The simulation experiment result shows that it can obviously improve the efficiency of quantum genetic algorithm by adding gene chain,and the quantum genetic algorithm based on a expanded multi-chain coding scheme is efficient.
In order to make full use of the quantum characteristics of the quantum state in the algorithm,and improve the search efficiency,reduce storage space,a new quantum genetic algorithm called 3D-AQGA was proposed.The algorithm describes quantum bit as a pair of angles in 3D spherical coordinate,makes full use of the quantum space motion characteristics,and introduces a kind of adaptive scheme to calculate the rotation angle size and direction which not only makes the process of chromosome's update and variation simplified,but also improves quantum characteristics,storage properties and time performance of the algorithm greatly.The simulation results show that the efficiency of the algorithm and the search ability are superior to the simple genetic algorithm and common quantum genetic algorithm.