一种新的混合量子进化算法

量子进化算法(QEA)用于多峰函数优化时,容易陷入局部最优.本文提出一种新的混合量子进化算法,通过双编码机制(经典二进制编码和量子概率编码),以及经典交叉和量子概率编码更新策略,实现了经典遗传算法与量子进化算法的有机结合,在发挥经典遗传算法全局优化能力的同时,利用量子概率搜索提高了算法的局部搜索能力.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.结果表明,本文算法在解的质量和收敛速度上都要优于QEA.     

求最优装载的量子算法

随着Grover量子搜索算法的不断发展,它的实际应用价值也在逐渐体现.通过介绍量子并行计算和量子算法的基本思想以及对改进的Grover搜索算法进行研究的基础上,分析给出了一个时间复杂度为O(√N)的求解最优装载问题的量子算法.对于最优装载问题,分别用经典计算机上的贪心算法和量子算法来求解,得出了这两种算法的时间复杂度,从而可以看出量子算法相对于经典算法具有更快的搜索速度.     

基于量子进化算法的路由选择

网络中存在许多设计和优化问题,其中相当一部分属于NP类型。传统的解法由于计算复杂度过大而失效。为了降低计算机网络的时延和运营费用以改进网络性能,采用量子进化算法优化计算机网络中路由选择问题,深入研究了量子进化算法及其在路由选择优化问题中的应用,并对量子进化算法进行了改进,使之更适合这类问题的求解。仿真实验结果表明,同传统优化算法相比该方法对求解网络的路由选择具有很大优越性。研究结果不仅对各类网络的优化问题有一定的应用价值,而且也扩展了量子进化算法的应用范围。     

量子多目标进化算法研究

本文首次将量子计算的理论用于多目标优化,提出量子多目标进化算法（QMOEA）,其采用量子位染色体表示法,利用量子门旋转策略和量子变异实现群体的进化,使用ε支配关系构造外部种群以此保持算法的较好分布性,提出基于快速排序的非劣最优解构造方法加快算法运行效率,实验表明,这种方法与经典的多目标进化算法SPEA2相比,其收敛性更好且分布更均匀     

一种新的求解TSP的混合量子进化算法

在分析量子进化基本概念的基础上,提出了一种新的求解TSP的混合量子进化算法（MQEA）。该算法将三段优化局部搜索算法融入量子进化机制,采用一种基于边的编码方法,应用最近邻规则设置初始参数,并设计了排序交叉算子以扩展种群的搜索范围。通过选取国际通用旅行商问题（TSP）实例库(TSPLIB)中的多个实例进行测试,表明新算法具有高的精确度和鲁棒性,即使对于中大规模问题（城市数大于500）,也能以很小的种群和微小的相对误差求得满意解。     

多进制概率角复合位编码量子进化算法

针对量子进化算法求解二进制编码问题比较有效,而求解多进制编码问题则比较困难的情况,本文提出了一种多进制概率角复合位编码量子进化算法.该算法将量子进化算法中量子位的概率幅表示法转化为复合位的概率角表示法,采用随机观测方法得到观测个体,采用概率角增减对个体进行更新.该算法适用于采用任意进制编码的问题.实验表明,与量子进化算法和传统遗传算法相比,多进制概率角复合位编码量子进化算法在适用范围、搜索能力和运算速度上具有较明显优势.     

一种基于量子进化算法改进的 k-mean 聚类算法

聚类分析是模式识别中的一个重要问题,是非监督学习的重要方法。K -means 算法是其中最经典的聚类算法之一。但是这种方法面对大规模数据的时候工作量非常巨大,并且保证不了聚类结果的最优性。提出了一种基于量子进化算法的改进的 K -means 聚类算法。该方法结合了两个方法的优点,用量子进化算法进行优化,并且改进了量子进化算法中的交叉算子和更新算子,提高了基于量子进化算法的 K -means 算法局部搜索能力。实验结果表明,改进算法取得了较好的效果。     

混合量子算法及其在flow shop问题中的应用

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。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。由于在求解排序问题中,算法本身存在收敛慢,没有利用其它未成熟个体等缺陷,将微粒群算法（PSO）及进化计算思想融入QEA中,构成了混合量子算法（HQA）。采用flow shop经典问题对算法进行了测试,结果证明混合算法克服了QEA的缺陷,对于求解排序问题具有一定的普适性。     

基于量子计算机的数据库搜索

Grover提出的量子搜索算法,可以用O(N1/2)的时间复杂度完成对规模为N的非结构化数据集的搜索,这在经典计算机上需要O(N)的复杂度。其中量子黑盒(又称为Oracle)依赖于具体问题,根据数据库搜索的要求,设计了量子黑盒的内部结构和相应的量子线路,给出了适合于数据库搜索的量子算法。     

一种改进的量子搜索算法

Rrover提出的对无序数据库进行搜索的量子算法，可以将搜索时间复杂度从经典计算机上的O（N）降低为O（N的平方根）。该算法显示了量子计算的强大能力，在量子计算研究中具有重要地位。但是，我们在研究Grover算法中发现Grover算法存在搜索失效等问题。本文分析了Grover算法中存在的问题，针对其不足之处进行了改进，并证明了改进后量子搜索算法的有效性。     

Evolutionary Approaches to Figure-Ground Separation

The problem of figure-ground separation is tackled from the perspective of combinatorial optimization. Previous attempts have used deterministic optimization techniques based on relaxation and gradient descent-based search, and stochastic optimization techniques based on simulated annealing and microcanonical annealing. A mathematical model encapsulating the figure-ground separation problem that makes explicit the definition of shape in terms of attributes such as cocircularity, smoothness, proximity and contrast is described. The model is based on the formulation of an energy function that incorporates pairwise interactions between local image features in the form of edgels and is shown to be isomorphic to the interacting spin (Ising) system from quantum physics. This paper explores a class of stochastic optimization techniques based on evolutionary algorithms for the problem of figure-ground separation. A class of hybrid evolutionary stochastic optimization algorithms based on a combination of evolutionary algorithms, simulated annealing and microcanonical annealing are shown to exhibit superior performance when compared to their purely evolutionary counterparts and to classical simulated annealing and microcanonical annealing algorithms. Experimental results on synthetic edgel maps and edgel maps derived from gray scale images are presented.     

改进量子进化算法及其在物流配送路径优化问题中的应用

量子进化算法的性能直接受量子旋转门旋转角计算方法的影响.文中提出一种改进量子进化算法,核心是设计了基于量子比特概率幅比值自适应计算量子旋转门旋转角的新方法,算法具有收敛速度快和全局搜索能力强的特点.通过0/1背包问题分析了新方法中相关参数对算法性能的影响,并应用算法求解物流配送路径优化问题,仿真表明改进量子进化算法性能优于量子进化算法和传统进化算法.     

混合量子差分进化算法及应用

量子进化算法基于量子旋转门更新量子比特状态影响了算法搜索性能．提出一种差分进化（DE）与和声搜索（Hs）相结合更新量子比特状态的混合量子差分进化算法（HQDE）．该方法采用实数量子角形式编码染色体,设计一种由差分进化计算更新量子位状态的量子差分进化算法（QDE）和一种由和声搜索更新量子位状态的量子和声搜索（QHS）,并相互机制融合,采用两种不同进化策略共同作用产生种群新量子个体以克服常规算法中早熟及收敛速度慢等缺陷;在此基础上,算法还引入量子非门算子对当前最劣个体以一定概率选中的量子比特位进行变异操作增强算法跳出局部最优解能力．理论分析证明该算法收敛于全局最优解．0／1背包问题及旅行商问题实例测试结果验证了该方法有效性．     

A quantum-inspired Tabu search algorithm for solving combinatorial optimization problems

In this study, we propose a novel quantum-inspired evolutionary algorithm (QEA), called quantum inspired Tabu search (QTS). QTS is based on the classical Tabu search and characteristics of quantum computation, such as superposition. The process of qubit measurement is a probability operation that increases diversification; a quantum rotation gate used to searching toward attractive regions will increase intensification. This paper will show how to implement QTS into NP-complete problems such as 0/1 knapsack problems, multiple knapsack problems and the traveling salesman problem. These problems are important to computer science, cryptography and network security. Furthermore, our experimental results on 0/1 knapsack problems are compared with those of other heuristic algorithms, such as a conventional genetic algorithm, a Tabu search algorithm and the original QEA. The final outcomes show that QTS performs much better than other heuristic algorithms without premature convergence and with more efficiency. Also on multiple knapsack problems and the traveling salesman problem QTS verify its effectiveness.     

A new real-coded quantum-inspired evolutionary algorithm for continuous optimization

This paper presents a recursive deepening hybrid strategy to solve real-parameter optimization problems. It couples a local search technique with a quantum-inspired evolutionary algorithm. In order to adapt the quantum-inspired evolutionary algorithm for continuous optimization without losing the states superposition property, a suitable sampling of the search space that tightens recursively and an integration of a uniformly generated random part after measurement have been utilized. The use of local search provides, for each search window, a good exploitation of the quantum inspired generated solution's neighbourhood. The proposed approach has been tested through the reference black-box optimization benchmarking framework. The comparison of the obtained results with those of some state-of-the-art algorithms has shown its actual effectiveness.     

A two‐phase harmony search algorithm for continuous optimization

Harmony search (HS) algorithm is inspired by the music improvisation process in which a musician searches for the best harmony and continues to polish the harmony to improve its aesthetics. The efficiency of evolutionary algorithms depends on the extent of balance between diversification and intensification during the course of the search. An ideal evolutionary algorithm must have efficient exploration in the beginning and enhanced exploitation toward the end. In this paper, a two‐phase harmony search (TPHS) algorithm is proposed that attempts to strike a balance between exploration and exploitation by concentrating on diversification in the first phase using catastrophic mutation and then switches to intensification using local search in the second phase. The performance of TPHS is analyzed and compared with 4 state‐of‐the‐art HS variants on all the 30 IEEE CEC 2014 benchmark functions. The numerical results demonstrate the superiority of the proposed TPHS algorithm in terms of accuracy, particularly on multimodal functions when compared with other state‐of‐the‐art HS variants; further comparison with state‐of‐the‐art evolutionary algorithms reveals excellent performance of TPHS on composition functions. Composition functions are combined, rotated, shifted, and biased version of other unimodal and multimodal test functions and mimic the difficulties of real search spaces by providing a massive number of local optima and different shapes for different regions of the search space. The performance of the TPHS algorithm is also evaluated on a real‐life problem from the field of computer vision called camera calibration problem, ie, a 12‐dimensional highly nonlinear optimization problem with several local optima.     

变邻域量子烟花算法求解CVRP

针对带容量约束的车辆路径问题，提出一种融合量子进化算法和变邻域优化策略的变邻域量子烟花算法。该算法采用等分随机键与最大位置法结合的实数编码方式，通过量子旋转门和非门变异提高算法全局搜索能力，同时运用结合2-Opt的变邻域优化策略加强局部搜索能力。选取17个基准算例进行参数实验和对比实验，实验结果表明，相对于对比算法，所提出的算法具有较好的寻优能力和收敛速度。     

A hybrid cuckoo search and genetic algorithm for reliability–redundancy allocation problems

Solving reliability and redundancy allocation problems via meta-heuristic algorithms has attracted increasing attention in recent years. In this study, a recently developed meta-heuristic optimization algorithm cuckoo search (CS) is hybridized with well-known genetic algorithm (GA) called CS–GA is proposed to solve the reliability and redundancy allocation problem. By embedding the genetic operators in standard CS, the balance between the exploration and exploitation ability further improved and more search space are observed during the algorithms’ performance. The computational results carried out on four classical reliability–redundancy allocation problems taken from the literature confirm the validity of the proposed algorithm. Experimental results are presented and compared with the best known solutions. The comparison results with other evolutionary optimization methods demonstrate that the proposed CS–GA algorithm proves to be extremely effective and efficient at locating optimal solutions.     

基于混沌理论的免疫量子进化算法

提出了基于混沌理论的免疫量子进化算法,该算法应用混沌理论并依据小生境机制将初始个体划分为实数编码染色体的子群,各子群应用免疫特性的局域搜索能力找出优化解。混沌优化搜索机制能有效避免早熟收敛。为解决2进制算法所不能避免的精度与效率的冲突,采用10进制编码染色体。算法综合了量子计算的天然并行性、免疫算法的充分自适应性和混沌系统的遍历性,它比传统的进化算法具有更好的种群多样性,更快的收敛速度,更有效的全局和局域寻优能力。仿真实验也表明了该算法的优越性。