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

Quantum clone genetic algorithm based multi-user detection

Lihua Zhang, Liping Zhang, Haiyan Peng

Published Jun 21, 2011• 4 citations
OpenAlex

Abstract

Multi-user detection based on quantum optimization algorithm has aroused intense interests recently. Quantum clone genetic algorithm has many shortcomings such as: low efficiency, poor population diversity, slow convergence speed, easy to trap in local minimums, blindness in global optimal searching direction and so on. An improved quantum clone genetic algorithm (IQCGA) is proposed in this paper. The algorithm have many merits: (1) Adopting nicking probability division initialization strategy, the population diversity is enhanced; (2) Introducing quantum whole interference crossover, the information is spread in the whole population, so it helps to avoid trapping in local minimums, and accelerates convergence speed; (3) Making use of adaptation strategy in quantum rotate gate updating, the searching speed for optimal solution is accelerated; (4) In order to avoid premature and evolution stagnation, the superior individual whole crossover quantum catastrophe strategy is adopted, which helps the population to search the objective solution in different directions. Simulation results prove that the multiuser detection based on given algorithm has lower bit error, rapider converge speed, better resisting MAI ability and better Near-far Resistance ability than other multi-user detections based on quantum clone genetic algorithm and classical genetic algorithms obviously.

Related papers

An improved quantum clone genetic algorithm

Lihua Zhang, Liping Zhang, Haiyan Peng

Intelligent optimization algorithm based on quantum behavior is the hot spot of intelligent computation. Quantum clone genetic algorithm has many shortcomings such as: low efficiency, poor population diversity, slow convergence speed, easy to trap in local minimums, blindness in global optimal searching direction and so on. An improved quantum clone genetic algorithm (IQCGA) is proposed in this paper. The algorithm have many merits: (1) Adopting the nicking probability division initialization strategy, population diversity is enhanced; (2) Introducing the quantum whole interference crossover, the information is spread in the whole population, so it helps to avoid trapping in local minimums, and accelerates convergence speed; (3) Making use of adaptation strategy in quantum rotate gate updating, searching speed for optimal solution is accelerated; (4) In order to avoid premature and evolution stagnation, the superior individual whole crossover quantum catastrophe strategy is adopted, which helps the population to search the objective solution in different directions. IQCGA is composted of initialization stage and iteration evolution stage. Furthermore, theory analysis is given to prove the convergence. Finally, Simulation results prove that the algorithms can maintain the population diversity, reduce calculation complexity, accelerate searching speed for optimal solution, and avoid trapping in local minimums. So it has the better searching optimal ability and convergence speed.

IEEE International Conference on Communication Software and Networks•May 1, 2011•0 citations

An improved multiple populations quantum genetic algorithm

Xu-Feng Niu, Wenping Ma

Abstract With the in-depth study of quantum genetic algorithm (QGA), the defect of premature convergence limits its development more and more. In order to further improve QGA, we propose an improved QGA based on multi population (IQGA). In the process of population initialization in IQGA, we generate multiple populations to avoid a single population falling into a local optimal value. We also abandon the fixed angle used by the traditional quantum revolving gate, and design a strategy to adjust the angle adaptively according to the difference from the optimal solution. In addition, we introduce a population catastrophe strategy to deal with the premature convergence of each population. The populations are connected based on migration operation, and the optimal solution of each population obtained by each iteration is collected in the elite group. A series of simulation experiments show that our improvement strategies of IQGA are effective.

Laser Physics Letters•Jul 28, 2022•7 citations

Improved quantum genetic algorithm and its application

YU Rui-qin

New methods are joined into the quantum genetic algorithm to solve the defects of poor local search ability and more iterative times.The algorithm is the core of a new quantum rotation gate adjustment strategy,which updates the population,so it has better diversity than the classical genetic algorithm,rapid convergence and good global search capacity characterize the performance of the quantum genetic algorithm.According to the test of typical complex function,the results show that the algorithm to the quality of opti-mization and efficiency are superior to traditional genetic algorithm and general quantum genetic algorithm.

Jisuanji gongcheng yu sheji•Jan 1, 2009•3 citations

Multi-Universe Parallel Quantum Genetic Algorithm

Shi Liang

This paper first proposes a novel Multi-Universe Parallel Quantum Genetic Algorithm (MPQGA) and proves its global convergence in theory.In the algorithm,all individuals are divided into some independent sub-colonies,called universes,according to their definite topological structure.Individuals in a universe are represented by multi-state gene qubits.In the individual's updating,the general quantum rotation gate strategy and dynamic adjusting rotation angle mechanism are applied to accelerate convergence.Quantum NOT gate is used to realize quantum mutation to avoid premature convergence.Each universe evolving independently enlarges the search space.Information among the universes is exchanged by adopting the best emigration and the quantum crossover operation for the improvement of search efficiency.Then it puts forward a new Blind Source Separation (BSS) method based on the combination of MPQGA and Independent Component Analysis (ICA).The simulation results show that the efficiency of the new BSS method is obviously higher than that of the Conventional Genetic Algorithm (CGA) and the Quantum Genetic Algorithm (QGA).

Dianzi xuebao•Jan 1, 2004•5 citations

Improvement of Quantum Genetic Algorithm and Its Application

Ge ZHANG

An improved quantum genetic algorithm(IQGA) was proposed to overcome the shortcoming of the quantum genetic algorithm(QGA),i.e. local optimization, when it is used for the optimization of continuous functions with many extreme values. In IQGA, the strategies of updating quantum gate using the best solution obtained and population catastrophe were adopted. The test results for two typical functions show that the convergence speed of IQGA is faster than that of QGA, and IQGA can converge in a global solution space, overcoming the shortcoming of QGA. The application results indicate that IQGA is better than QGA and other genetic algorithms.

Journal of Southwest Jiaotong University•Jan 1, 2003•12 citations

A novel genetic algorithm based on the quantum chromosome

Shuyuan Yang, Fang Liu

The genetic algorithm is an efficient optimization tool for its independence of problems, intrinsic parallelism and inherent learning capacity; however, it has some disadvantages such as slow convergence and prematurity. In this paper, a novel algorithm, called the quantum genetic algorithm—QGA, is proposed based on the combination of the quantum theory with the evolutionary theory. By adopting the qubit chromosome as a representation, QGA can represent a linear superposition of solutions due to its probabilistic representation. The random observation simulating the quantum collapse can bring diverse individuals, and the evolution of quantum chromosome can also pilot the evolution. So it has better diversity than the classical genetic algorithm. Rapid convergence and good global search capacity characterize the performance of QGA. The paper not only proves the global convergence of the QGA, but also some simulation experiments are shown to prove its superiority over other algorithms.

Journal of Xidian University•Jan 1, 2004•13 citations
Multi-user detection based on quantum optimization algorithm has aroused intense interests recently. Quantum clone genetic algorithm has many shortcomings such as: low efficiency, poor population diversity, slow convergence speed, easy to trap in local minimums, blindness in global optimal searching direction and so on. An improved quantum clone genetic algorithm (IQCGA) is proposed in this paper. The algorithm have many merits: (1) Adopting nicking probability division initialization strategy, the population diversity is enhanced; (2) Introducing quantum whole interference crossover, the information is spread in the whole population, so it helps to avoid trapping in local minimums, and accelerates convergence speed; (3) Making use of adaptation strategy in quantum rotate gate updating, the searching speed for optimal solution is accelerated; (4) In order to avoid premature and evolution stagnation, the superior individual whole crossover quantum catastrophe strategy is adopted, which helps the population to search the objective solution in different directions. Simulation results prove that the multiuser detection based on given algorithm has lower bit error, rapider converge speed, better resisting MAI ability and better Near-far Resistance ability than other multi-user detections based on quantum clone genetic algorithm and classical genetic algorithms obviously.
Intelligent optimization algorithm based on quantum behavior is the hot spot of intelligent computation. Quantum clone genetic algorithm has many shortcomings such as: low efficiency, poor population diversity, slow convergence speed, easy to trap in local minimums, blindness in global optimal searching direction and so on. An improved quantum clone genetic algorithm (IQCGA) is proposed in this paper. The algorithm have many merits: (1) Adopting the nicking probability division initialization strategy, population diversity is enhanced; (2) Introducing the quantum whole interference crossover, the information is spread in the whole population, so it helps to avoid trapping in local minimums, and accelerates convergence speed; (3) Making use of adaptation strategy in quantum rotate gate updating, searching speed for optimal solution is accelerated; (4) In order to avoid premature and evolution stagnation, the superior individual whole crossover quantum catastrophe strategy is adopted, which helps the population to search the objective solution in different directions. IQCGA is composted of initialization stage and iteration evolution stage. Furthermore, theory analysis is given to prove the convergence. Finally, Simulation results prove that the algorithms can maintain the population diversity, reduce calculation complexity, accelerate searching speed for optimal solution, and avoid trapping in local minimums. So it has the better searching optimal ability and convergence speed.
Abstract With the in-depth study of quantum genetic algorithm (QGA), the defect of premature convergence limits its development more and more. In order to further improve QGA, we propose an improved QGA based on multi population (IQGA). In the process of population initialization in IQGA, we generate multiple populations to avoid a single population falling into a local optimal value. We also abandon the fixed angle used by the traditional quantum revolving gate, and design a strategy to adjust the angle adaptively according to the difference from the optimal solution. In addition, we introduce a population catastrophe strategy to deal with the premature convergence of each population. The populations are connected based on migration operation, and the optimal solution of each population obtained by each iteration is collected in the elite group. A series of simulation experiments show that our improvement strategies of IQGA are effective.
New methods are joined into the quantum genetic algorithm to solve the defects of poor local search ability and more iterative times.The algorithm is the core of a new quantum rotation gate adjustment strategy,which updates the population,so it has better diversity than the classical genetic algorithm,rapid convergence and good global search capacity characterize the performance of the quantum genetic algorithm.According to the test of typical complex function,the results show that the algorithm to the quality of opti-mization and efficiency are superior to traditional genetic algorithm and general quantum genetic algorithm.
This paper first proposes a novel Multi-Universe Parallel Quantum Genetic Algorithm (MPQGA) and proves its global convergence in theory.In the algorithm,all individuals are divided into some independent sub-colonies,called universes,according to their definite topological structure.Individuals in a universe are represented by multi-state gene qubits.In the individual's updating,the general quantum rotation gate strategy and dynamic adjusting rotation angle mechanism are applied to accelerate convergence.Quantum NOT gate is used to realize quantum mutation to avoid premature convergence.Each universe evolving independently enlarges the search space.Information among the universes is exchanged by adopting the best emigration and the quantum crossover operation for the improvement of search efficiency.Then it puts forward a new Blind Source Separation (BSS) method based on the combination of MPQGA and Independent Component Analysis (ICA).The simulation results show that the efficiency of the new BSS method is obviously higher than that of the Conventional Genetic Algorithm (CGA) and the Quantum Genetic Algorithm (QGA).
An improved quantum genetic algorithm(IQGA) was proposed to overcome the shortcoming of the quantum genetic algorithm(QGA),i.e. local optimization, when it is used for the optimization of continuous functions with many extreme values. In IQGA, the strategies of updating quantum gate using the best solution obtained and population catastrophe were adopted. The test results for two typical functions show that the convergence speed of IQGA is faster than that of QGA, and IQGA can converge in a global solution space, overcoming the shortcoming of QGA. The application results indicate that IQGA is better than QGA and other genetic algorithms.
The genetic algorithm is an efficient optimization tool for its independence of problems, intrinsic parallelism and inherent learning capacity; however, it has some disadvantages such as slow convergence and prematurity. In this paper, a novel algorithm, called the quantum genetic algorithm—QGA, is proposed based on the combination of the quantum theory with the evolutionary theory. By adopting the qubit chromosome as a representation, QGA can represent a linear superposition of solutions due to its probabilistic representation. The random observation simulating the quantum collapse can bring diverse individuals, and the evolution of quantum chromosome can also pilot the evolution. So it has better diversity than the classical genetic algorithm. Rapid convergence and good global search capacity characterize the performance of QGA. The paper not only proves the global convergence of the QGA, but also some simulation experiments are shown to prove its superiority over other algorithms.