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

Chinese Physics Letters

Improved Quantum Evolutionary Computation Based on Particle Swarm Optimization and Two-Crossovers

Duan Hai-Bin, Xing Zhi-Hui

Published Dec 1, 2009• 3 citations
Publisher

Abstract

A quantum evolutionary computation (QEC) algorithm with particle swarm optimization (PSO) and two-crossovers is proposed to overcome identified limitations. PSO is adopted to update the Q-bit automatically, and two-crossovers are applied to improve the convergence quality in the basic QEC model. This hybrid strategy can effectively employ both the ability to jump out of the local minima and the capacity of searching the global optimum. The performance of the proposed approach is compared with basic QEC on the standard unconstrained scalable benchmark problem that numerous hard combinatorial optimization problems can be formulated. The experimental results show that the proposed method outperforms the basic QEC quite significantly.

DOI
10.1088/0256-307x/26/12/120304

Related papers

Hybrid Quantum Evolutionary Algorithms Based on Particle Swarm Theory

Yang Yu, Yafei Tian, Zhifeng Yin

Inspired by the idea of hybrid optimization algorithms, this paper proposes two hybrid quantum evolutionary algorithms (QEA) based on combining QEA with particle swarm optimization (PSO) to improve the performance of QEA . The main idea of the first method called PSEQEA is to embed the evolutionary equation of PSO in the evolutionary operator of QEA; while the main idea of the second method called PSSQEA is to replace the evolutionary operator of QEA using the evolutionary equation of PSO which is redefined the meanings of the original evolutionary equations. The experiment results of the knapsack problem, the function optimization problems and multiuser detection problem show that the both of the proposed methods not only have simpler algorithm structure, but also perform better than conventional QEA and PSO in terms of ability of global optimum

IEEE Conference on Industrial Electronics and Applications (ICIEA)•May 1, 2006•13 citations

Hybrid Quantum Evolutionary Algorithms and its Application

Yafei Tian

Inspired by the idea of hybrid optimization algorithms,this paper proposes two hybrid Quantum Evolutionary Algorithms(QEA) based on combining QEA with Particle Swarm Optimization(PSO).The main idea of the first method called PSEQEA is to embed the evolutionary equation of PSO in QEA;while the main idea of the second method called QBPSO is to apply the quantum chromosomes of QEA to binary PSO(BPSO).The experiment results of the knapsack problem and multiuser detection problem show that the both of the proposed methods not only have simpler algorithm structure,but also perform better than conventional QEA and BPSO in terms of ability of global optimum.

Computer Engineering and Applications Journal•Jan 1, 2006•2 citations

Hybrid quantum particle swarm optimization algorithm for combinatorial optimization problem

Jiahai Wang, Yanlan Zhou

In this paper, a framework of hybrid PSO is proposed by reasonablycombining the Q-bit evolutionary search of quantum PSO and binary bit evolutionary search of genetic PSO.

Genetic and Evolutionary Computation Conference (GECCO)•Jul 7, 2007•3 citations

Improved Quantum-Inspired Evolutionary Algorithm and Its Application to 3-SAT Problems

Xiaoyue Feng, Enrico Blanzieri, Yanchun Liang

An improved quantum-inspired evolutionary algorithm is presented in this paper. Quantum angle is adopted to present the quantum bit in the proposed algorithm. A novel quantum rotation gate strategy is adopted to adjust the direction of the quantum gate which is used to update the quantum population. The step size is adaptively adjusted rather than a fixed angle. Furthermore, the particle swarm optimization is added into the improved algorithm to accelerate the convergent speed and develop the local searching ability. To demonstrate the effectiveness and applicability of the proposed approach, several experiments are performed on the 3-SAT problems. The results show that it is feasible and effective to solve the 3-SAT problem using the proposed algorithm.

•Jan 1, 2008•5 citations

A NEW QUANTUM PARTICLE SWARM OPTIMIZATION ALGORITHM WITH LOCAL ATTRACTING

Dong Xiang Shao, Shenghai Hu, Yuting Fei

This paper proposes a new quantum particle swarm optimization algorithm with local attracting (LAQPSO), which is based on quantum-inspired evolutionary algorithm (QEA) and particle swarm optimization algorithm (PSO).In the proposed LAQPSO, a novel quantum bit expression mechanism called quantum angle is employed to encode the solution onto particle, and a new local attractor is proposed to determine the rotation angle of quantum rotation gate automatically.During the process of seeking the global solution, the magnitude of rotation angle is adjusted by an important parameter called contraction coefficient, which can quantitatively determine the tradeoff between exploration ability and exploitation ability.The simulation results for different contraction coefficients are helpful for selecting the key parameter.A set of benchmark functions are used to evaluate the performance of LAQPSO, QEA and QBPSO, and the results show that the proposed algorithm has a fast convergence rate and can effectively avoid premature convergence.

Neural Network World•Jan 1, 2016•4 citations

A HYBRID ARTIFICIAL BEE COLONY OPTIMIZATION AND QUANTUM EVOLUTIONARY ALGORITHM FOR CONTINUOUS OPTIMIZATION PROBLEMS

Haibin Duan, Chunfang Xu, Zhihui Xing

In this paper, a novel hybrid Artificial Bee Colony (ABC) and Quantum Evolutionary Algorithm (QEA) is proposed for solving continuous optimization problems. ABC is adopted to increase the local search capacity as well as the randomness of the populations. In this way, the improved QEA can jump out of the premature convergence and find the optimal value. To show the performance of our proposed hybrid QEA with ABC, a number of experiments are carried out on a set of well-known Benchmark continuous optimization problems and the related results are compared with two other QEAs: the QEA with classical crossover operation, and the QEA with 2-crossover strategy. The experimental comparison results demonstrate that the proposed hybrid ABC and QEA approach is feasible and effective in solving complex continuous optimization problems.

International Journal of Neural Systems•Feb 1, 2010•105 citations
A quantum evolutionary computation (QEC) algorithm with particle swarm optimization (PSO) and two-crossovers is proposed to overcome identified limitations. PSO is adopted to update the Q-bit automatically, and two-crossovers are applied to improve the convergence quality in the basic QEC model. This hybrid strategy can effectively employ both the ability to jump out of the local minima and the capacity of searching the global optimum. The performance of the proposed approach is compared with basic QEC on the standard unconstrained scalable benchmark problem that numerous hard combinatorial optimization problems can be formulated. The experimental results show that the proposed method outperforms the basic QEC quite significantly.
Inspired by the idea of hybrid optimization algorithms, this paper proposes two hybrid quantum evolutionary algorithms (QEA) based on combining QEA with particle swarm optimization (PSO) to improve the performance of QEA . The main idea of the first method called PSEQEA is to embed the evolutionary equation of PSO in the evolutionary operator of QEA; while the main idea of the second method called PSSQEA is to replace the evolutionary operator of QEA using the evolutionary equation of PSO which is redefined the meanings of the original evolutionary equations. The experiment results of the knapsack problem, the function optimization problems and multiuser detection problem show that the both of the proposed methods not only have simpler algorithm structure, but also perform better than conventional QEA and PSO in terms of ability of global optimum
Inspired by the idea of hybrid optimization algorithms,this paper proposes two hybrid Quantum Evolutionary Algorithms(QEA) based on combining QEA with Particle Swarm Optimization(PSO).The main idea of the first method called PSEQEA is to embed the evolutionary equation of PSO in QEA;while the main idea of the second method called QBPSO is to apply the quantum chromosomes of QEA to binary PSO(BPSO).The experiment results of the knapsack problem and multiuser detection problem show that the both of the proposed methods not only have simpler algorithm structure,but also perform better than conventional QEA and BPSO in terms of ability of global optimum.
An improved quantum-inspired evolutionary algorithm is presented in this paper. Quantum angle is adopted to present the quantum bit in the proposed algorithm. A novel quantum rotation gate strategy is adopted to adjust the direction of the quantum gate which is used to update the quantum population. The step size is adaptively adjusted rather than a fixed angle. Furthermore, the particle swarm optimization is added into the improved algorithm to accelerate the convergent speed and develop the local searching ability. To demonstrate the effectiveness and applicability of the proposed approach, several experiments are performed on the 3-SAT problems. The results show that it is feasible and effective to solve the 3-SAT problem using the proposed algorithm.
This paper proposes a new quantum particle swarm optimization algorithm with local attracting (LAQPSO), which is based on quantum-inspired evolutionary algorithm (QEA) and particle swarm optimization algorithm (PSO).In the proposed LAQPSO, a novel quantum bit expression mechanism called quantum angle is employed to encode the solution onto particle, and a new local attractor is proposed to determine the rotation angle of quantum rotation gate automatically.During the process of seeking the global solution, the magnitude of rotation angle is adjusted by an important parameter called contraction coefficient, which can quantitatively determine the tradeoff between exploration ability and exploitation ability.The simulation results for different contraction coefficients are helpful for selecting the key parameter.A set of benchmark functions are used to evaluate the performance of LAQPSO, QEA and QBPSO, and the results show that the proposed algorithm has a fast convergence rate and can effectively avoid premature convergence.
In this paper, a novel hybrid Artificial Bee Colony (ABC) and Quantum Evolutionary Algorithm (QEA) is proposed for solving continuous optimization problems. ABC is adopted to increase the local search capacity as well as the randomness of the populations. In this way, the improved QEA can jump out of the premature convergence and find the optimal value. To show the performance of our proposed hybrid QEA with ABC, a number of experiments are carried out on a set of well-known Benchmark continuous optimization problems and the related results are compared with two other QEAs: the QEA with classical crossover operation, and the QEA with 2-crossover strategy. The experimental comparison results demonstrate that the proposed hybrid ABC and QEA approach is feasible and effective in solving complex continuous optimization problems.