qea

qea

1. Research and Design of the QEA Encryption Algorithm

QEA加密算法的研究与设计

2. The function optimization and 0-k knapsack problem experiments show that PEA has apparent superior in application area, searching capability and computation time compared with QEA and canonical genetic algorithm (CGA).

函数优化和0-k背包问题实验表明,与量子进化算法和传统遗传算法相比,概率进化算法在适用范围、搜索能力和收敛速度上有明显的优势。

3. Quantum Evolutionary Algorithm（QEA） is a distinctive type of algorithm for optimization currently，and the theoretical basis of QEA is quantum computation.

摘要 量子进化算法（QEA）是目前较为独特的优化算法，它的理论基础是量子计算。

4. 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).

摘要将量子进化算法(QEA)和粒子群算法(PSO)互相结合，提出了两种混合量子进化算法。

5. improved QEA

改进的量子进化算法

6. 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).

文章将量子进化算法(QEA)和粒子群算法(PSO)互相结合,提出了两种混合量子进化算法。

7. In the paper, we discuss the theory, encryption, decryption and design of the QEA algorithm.

本文主要论述了快速加密算法QEA、加密和解密过程及实现。

8. QEAMSA is a new proposed aligning method. It altered the basic QEA: represented the MSA problem with Q-bit, designed one method adapting to MSA updating Q-bit individual.

本文提出的QEAMSA是一种新的比对方法,它是对基本QEA算法做了改造:采用新的表示方法Q-bit来表示MSA问题,设计了适合于该问题的量子个体更新方法;

9. while the main idea of the second method called QBPSO is to apply the quantum chromosomes of QEA to binary PSO(BPSO).

第二种算法叫做量子二进制粒子群算法,其主要思想是将QEA中的量子染色体的概念引入二进制粒子群算法(BPSO),提高了BPSO算法保持种群多样性的能力和运算速度。

10. The algorithm takes advantage of intervention and parallelism of quantum bit thoroughly，which enables QEA to solve combinatorial optimization problems.

算法充分借鉴了量子比特的干涉性、并行性，使得QEA求解组合优化问题具备了可行性。

11. Experiments on 23 test functions of diverse complexities are implemented and compared with QEA in this paper.

通过一组典型函数优化实验对该算法的性能进行了考察,并与QEA进行了比较。

12. The experiment results of multiuser detection problem show that 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.

通过对多用户检测问题的求解表明，新的算法不仅操作更简单，而且全局搜索能力有了显著的提高。

13. The computation results of problems from TSP database indicate that the performance of improved QEA is superior to that of the conventional QEA and the immune evolutionary algorithm.

通过求解TSP问题库中的部分问题,表明改进的算法比经典的量子进化算法及免疫遗传算法具有更快的收敛速度和更好的全局寻优能力。

14. Quantum Evolutionary Algorithm (QEA)

量子进化算法