A Whale Optimization Algorithm with Distributed Collaboration and Reverse Learning Ability

Due to the development of digital transformation,intelligent algorithms are getting more and more attention.The whale optimization algorithm(WOA)is one of swarm intelligence optimization algorithms and is widely used to solve practical engineering optimization problems.However,with the increased dimensions,higher requirements are put forward for algorithm performance.The double population whale optimization algorithm with distributed collaboration and reverse learning ability(DCRWOA)is proposed to solve the slow convergence speed and unstable search accuracy of the WOA algorithm in optimization problems.In the DCRWOA algorithm,the novel double population search strategy is constructed.Meanwhile,the reverse learning strategy is adopted in the population search process to help individuals quickly jump out of the non-ideal search area.Numerical experi-ments are carried out using standard test functions with different dimensions(10,50,100,200).The optimization case of shield construction parameters is also used to test the practical application performance of the proposed algo-rithm.The results show that the DCRWOA algorithm has higher optimization accuracy and stability,and the convergence speed is significantly improved.Therefore,the proposed DCRWOA algorithm provides a better method for solving practical optimization problems.

不确定数据上的Reverse Top-k查询

传统的top-k查询为顾客返回符合其偏好的产品集合,reverse top-k查询则返回将给定产品作为top-k结果的偏好集合。reverse top-k查询由于能帮助生产者评估产品对顾客的影响,因此在商业分析中具有重要价值。现有的reverse top-k查询假设数据是精确的,许多现实应用中,数据的不确定性广泛存在。将reverse top-k查询扩展到不确定数据上,并给出了基于物化视图的高效查询算法GMV。实验结果表明,GMV算法能够减少需要计算的偏好数量,具有较高的计算效率。

A genetic algorithm for dynamic parameters reverse deduction of integrated anchorage system

In the analysis of the system of anchoring bar and wall rock in small strain and longitudinal vibration dynamic response, the influence of the cement grouting as well as the rock layer on the anchor bar can be evaluated as the two kinds of parameters: the dynamic stiffness and the damp, which are the vital reference of the anchorage quality. Based on the analytic solution to the dynamic equation of the integrated anchor bar, the new approach which combines genetic algorithm and the toolbox of Matlab is applied to solve the problem of multi-parameters reverse deduction for integrated anchorage system in dynamic testing. Using the traits of the self-organizing, self-adapting and the fast convergence speed of the genetic algorithm, the optimum of all possible solutions to dynamic parameters is obtained by calculating the project instances. Examples show that the method presented in this paper is effective and reliable.

AN IMPROVED DIGITAL-REVERSED PERMUTATION ALGORITHM FOR THE FAST FOURIER ANDHARTLEY TRANSFORMS

A more efficient permutation algorithm which has less computer operation and better structure is presented here for radix-2 FFT(FHT). It can fasten the FFT and FHT efficiently when N becomes large.

Multiple vehicle routing problem integrated reverse logistics with fuzzy reverse demands

A new type of vehicle routing problem （VRP）, multiple vehicle routing problem integrated reverse logistics （MVRPRL）, is studied. In this problem, there is delivery or pickup （or both） and uncertain features in the demands of the clients. The deliveries of every client as uncertain parameters are expressed as triangular fuzzy numbers. In order to describe MVRPRL, a multi-objective fuzzy programming model with credibility measure theory is constructed. Then the simulationbased tabu search algorithm combining inter-route and intra-route neighborhoods and embedded restarts are designed to solve it. Computational results show that the tabu search algorithm developed is superior to sweep algorithms and that compared with handling each on separate routes, the transportation costs can be reduced by 43% through combining pickups with deliveries.

Literature Review on Network Design Problems in Closed Loop and Reverse Supply Chains

The productivity of an organization is very much affected by non-value adding activity like logistics, which moves the resources from suppliers to factory, raw materials/semi-finished items within the factory and finished goods from factory to customers via a designated distribution channel called as forward logistics. In some cases, parts of the products such as automobiles, computers, cameras, mobile phones, washing machines, refrigerators, garments, footwear and empty glass bottles of beverages, etc. will be brought back to the factories as a product recovery strategy through reverse logistics network which is integrated in a sustainable closed loop supply chain network. So, it is highly essential to optimize the movement of the items in the reverse logistics network. This paper gives a comprehensive review of literature of the design of networks for the reverse logistics as well as for the reverse logistics coupled with forward logistics. The contributions of the researchers are classified into nine categories based on the methods used to design the logistics network.

Evolutionary Techniques for Reverse Auctions

Winner determination is one of the main challenges in combinatorial auctions. However, not much work has been done to solve this problem in the case of reverse auctions using evolutionary techniques. This has motivated us to propose an improvement of a genetic algorithm based method, we have previously proposed, to address two important issues in the context of combinatorial reverse auctions: determining the winner(s) in a reasonable processing time, and reducing the procurement cost. In order to evaluate the performance of our proposed method in practice, we conduct several experiments on combinatorial reverse auctions instances. The results we report in this paper clearly demonstrate the efficiency of our new method in terms of processing time and procurement cost.

Multi-objective Optimization of Controller for Process with Reverse Response and Dead Time

Due to the difficulty of controlling the process with inverse response and dead time,a Multi-objective Optimization based on Genetic Algorithm (MOGA) method for tuning of proportional-integral-derivative (PID) controller is proposed. The settings of the controller are valued by two criteria,the error between output and reference signals and control moves. An appropriate set of Pareto optimal setting of the PID controller is founded by analyzing the results of Pareto optimal surfaces for balancing the two criteria. A high order process with inverse response and dead time is used to illustrate the results of the proposed method. And the efficiency and robustness of the tuning method are evident compared with methods in recent literature.

A Database-Driven Algorithm for Building Top-k Service-Based Systems

The purpose of this work aims is to automatically build top-k(the number of suggested results)light weight service based systems(LitSBSs)on the basis of user-given keywords.Compared with our previous work,we use a score(oscore)to evaluate the keyword matching degree and QoS performance of a service so that we could find top-k LitSBSs with both high keyword matching degree and great QoS performance at the same time.In addition,to guarantee the quality of found top-k LitSBSs and improve the time efficiency,we redesign the database-driven algorithm(LitDB).We add the step of referential services selecting into the process of the LitDB,which could prioritize services with high quality(high keyword matching degree and great QoS performance).We design comprehensive experiments to demonstrate the great time performance of LitDB.

一种混合多策略改进的麻雀搜索算法

针对麻雀搜索算法SSA求解目标函数最优解时具有过早收敛、在多峰条件下易陷入局部最优和在高维情况下求解精度不足等问题,提出了一种混合多策略改进的麻雀搜索算法MISSA。考虑到算法初始解的质量很大程度上会影响整个算法的收敛速度与精度,引入精英反向学习策略,扩大算法的搜索区域,提升初始种群的质量和多样性;对步长进行分阶段控制,以提高算法的求解精度;通过在跟随者的位置中加入Circle映射参数与余弦因子,提高算法的遍历性与搜索能力;采用自适应选择机制在麻雀个体位置更新中加入Lévy飞行,增强算法寻优和跳出局部最优的能力。将改进后的算法与麻雀搜索算法及其他算法在13个测试函数上进行对比,并进行Friedman检验。实验结果表明,改进后的麻雀搜索算法能够有效提高寻优精度与收敛速度,并在高维问题中也具备较高的稳定性。

改进贝叶斯网络在变压器故障诊断中的应用

针对变压器故障诊断精度低的问题,文中提出一种基于改进黏菌优化算法(Improved Slime Mould Algorithm,ISMA)优化贝叶斯网络(Bayesian Network,BN)的变压器故障诊断方法。通过爬山算法对定向最大支撑树搜索得到贝叶斯网络初始结构即初始种群,在改进黏菌优化算法中引入反向学习策略,增加种群多样性。添加正弦-余弦算法(Sine Cosine Algorithm,SCA),更新解的位置以避免种群陷入局部最优。根据改良的无编码比值法选取变压器故障状态的特征,利用改进黏菌优化算法优化贝叶斯网络结构,提高基于贝叶斯网络的变压器故障诊断的准确率,并利用不同种类的测试函数验证了改进黏菌优化算法具有收敛速度快、收敛精度高的优良性能。仿真结果表明,ISMA-BN诊断模型的训练集和测试集准确率分别为98.2%和97.14%,具有一定的研究价值。

基于改进布谷鸟搜索算法的光伏MPPT控制

复杂阴影情况下,光伏阵列的P-V特性曲线会出现多个峰值,传统的MPPT算法因不能准确识别局部峰值和全局峰值,而无法进行复杂阴影情况下的最大功率点跟踪。针对传统布谷鸟搜索(Cuckoo Search,CS)算法因鸟窝之间缺乏交流能力导致可能陷入局部最优的问题,提出了一种多策略改进布谷鸟搜索(EGICS)算法。将传统CS算法中发现概率值的选择自适应变化,提高算法的搜索能力;将步长因子自适应化,提高算法的收敛速度;引入高斯扰动和精英反向学习策略,增加种群多样性,避免算法陷入局部最优。对EGICS算法在单峰和多峰函数中进行性能测试,并将其应用于光伏系统MPPT控制中进行仿真验证。仿真结果表明,EGICS算法在收敛速度、跟踪精度以及动态稳定三个方面有更好的效果。

支持密码逆向防火墙的基于SM9的属性基可搜索加密方案

针对属性基可搜索加密(ABSE)方案大都基于非国密算法设计,且无法抵抗内部算法替换攻击(ASA)的问题,提出一种支持密码逆向防火墙的基于SM9的属性基可搜索加密方案(SM9ABSE-CRF)。该方案将国密算法SM9扩展至ABSE领域,实现了细粒度数据访问控制,并引入密码逆向防火墙(CRF)技术有效抵御ASA。分析了SM9ABSE-CRF在判定性Diffie-Hellman(DBDH)假设下满足了选择关键词下的不可区分性,并形式化证明了CRF的部署满足维持功能性、保留安全性以及抵抗泄漏性。理论分析和仿真实验结果表明,与提供CRF的ABSE方案cABKSCRF(consistent Attribute-Based Keyword Search system with CRF)相比,SM9ABSE-CRF具有更高的安全性,并且在索引与陷门生成阶段也表现出显著的性能优势。

Multi-View Point Clouds Registration without Feature Extraction for Use in Reverse Engineering of CAD Models

For reverse engineering a CAD model, it is necessary to integrate measured points from several views of an object into a common reference frame. Given a rough initial alignment of point cloud in different views with point-normal method, further refinement is achieved by using an improved iterative closest point (ICP) algorithm. Compared with other methods used for mult-view registration, this approach is automatic because no geometric feature, such as line, plane or sphere needs to be extracted from the original point cloud manually. A good initial alignment can be acquired automatically and the registration accuracy and efficiency is proven better than the normal point-point ICP algorithm both experimentally and theoretically.

改进麻雀搜索算法的轮式机器人路径规划

针对传统麻雀搜索算法(sparrow search algorithm,SSA)在轮式机器人路径规划应用中易陷入局部最优使得规划路径较长以及算法后期容易陷入早熟等问题,提出一种改进麻雀搜索算法(improved sparrow search algorithm,ISSA)应用在轮式机器人路径规划中。首先,在算法初期初始化种群时利用Logistic混沌提高初始种群的多样性;其次,将线性动态惯性权重调整方法引入到发现者位置更新中,使得算法的全局搜索能力以及收敛速度得以提升;然后,在跟随者位置更新方法中结合中垂线算法(midperpendicular algorithm,MA)使跟随者快速精准地向种群适应度最高的个体靠拢;最后,在算法后期结合最优爆炸粒子策略与反向学习策略在最优解附近产生扰动,防止算法后期陷入局部最优解。并且在机器人路径规划应用中将全局最优解再次进行局部搜索来提高机器人的路径规划能力。仿真结果表明,ISSA应用在路径规划中,其路径长度、寻优速度以及迭代次数方面均有显著提高。

基于染色算法的地震干涉成像技术

为了提高基于染色算法的逆时偏移计算效率,提出基于染色算法的地震干涉成像技术(CC-STRTM)。地震干涉技术作为一种炮记录的处理手段可以消去两个炮记录中相同的信号。该技术将观测系统从地表下移到人为选取的地下校准面,将研究区域缩小到校准面下侧。再分析干涉成像结果,选出染色区域。最后完成复数域的逆时偏移处理,输出虚部偏移结果。通过复杂模型对该方法进行测试,对比分析成像结果和计算效率。结果表明,该方法在提高成像精度的同时不降低计算效率。

基于逆向重布线改进RRT-Connect算法的机械臂路径规划

为解决RRT-Connect算法在复杂非结构环境下规划出的路径成本较长、有效节点数过多、曲折不平滑等问题,提出了一种基于逆向重布线的改进RRT-Connect算法(Improved-RRT-Connect).首先,在算法的起始位置加入碰撞检测函数,当起点和终点之间不存在障碍物时,能够快速生成一条路径;其次,通过对生成的路径采取逆向重布线的措施,对生成的路径中的节点进行重新筛选布线,达到缩短生成路径长度,减少有效节点数量的目的;最后,通过结合B样条曲线对生成的路径进行拟合处理,得到一条平滑连续的路径.通过Improved-RRT-Connect算法与RRT、RRT^(*)-Smart、RRT-Connect等算法的对比实验表明,在规划时间上,Improved-RRT-Connect算法比RRT、RRT^(*)-Smart算法减少了52.14%、98.53%,在路径成本上比RRT、RRT-Connect算法减少了19.39%、17.15%,阐明了提出的Improved-RRT-Connect算法的优越性.

基于混合策略改进的鹈鹕优化算法

针对鹈鹕优化算法求解精度低、稳定性不足、易陷入局部最优等问题,文章提出一种混合策略改进的鹈鹕优化算法(IPOA)。首先,为了增强种群的随机性和多样性,扩大种群的搜索范围,引入反向折射学习机制;其次,利用正余弦算法和鹈鹕算法融合,改进鹈鹕搜索猎物的方式,增强算法的局部搜索与全局搜索能力;然后,采用Levy飞行机制对鹈鹕位置进行更新,从而提高算法的搜索能力以寻找最优值;最后,引入自适应t分布变异算子,使用算法的迭代次数作为t分布的自由度参数来增强鹈鹕种群的多样性,避免算法陷入局部最优。通过12个标准测试函数对改进算法与海鸥优化算法、黑猩猩优化算法、鲸鱼优化算法、蛇群优化算法和基本鹈鹕优化算法进行测试比较,结果表明,IPOA具有更好的收敛速度和稳定性。最后将改进鹈鹕算法应用于压力容器设计优化问题,进一步证实改进后的算法具有较好的求解性能。

融合三维螺旋运动和混合反向学习策略的改进鹈鹕优化算法

针对鹈鹕优化算法收敛速度较慢、初始化过程随机产生初始种群导致种群多样性差,在后期易陷入局部最优等问题,提出了一种融合三维螺旋飞行和混合反向学习策略的鹈鹕优化算法。首先使用Gauss映射初始化种群,提高种群多样性;其次利用三维螺旋飞行和混合最优最差反向学习策略,加强算法跳出局部最优的能力;最后,引入自适应平衡因子与自适应步长,提出鹈鹕坠落策略,以模拟捕食过程中群体的微小变化。最后,通过12个基准函数和实际案例对IPOA(improved pelican optimization algorithm)进行测试,并与8个仿生算法进行对比,测试结果与Wilcoxon符号秩和检验结果均表明IPOA收敛精度与稳定性等各项性能都有所提升,具有明显优势。

基于改进白鲸优化算法的D2D通信功率控制

D2D(Device-to-Device)通信作为未来移动通信网络的关键技术,为用户提供了直接通信的便利性和资源共享的高效性.然而,D2D通信的功率控制一直是影响通信质量和系统性能的关键问题.为解决这一问题,将精英反向学习、自适应权重两种策略引入到白鲸优化算法(Beluga Whale Optimization,BWO)中,并利用莱维飞行的随机步长策略来增加算法寻优的多样性,提出了基于改进白鲸优化算法的D2D通信功率控制方法.该方法利用最优解的信息引导搜索过程,可提高搜索效率和全局收敛,并能够有效提高通信效率和系统稳定性.为了验证所提出方法的有效性,开展了大量的数值仿真实验.结果显示,基于改进白鲸优化算法的D2D通信功率控制方法在增加系统吞吐量、减少干扰方面有显著的改善.同时,提出的算法相对于已有的算法有着更出色的收敛性与鲁棒性,在不同通信环境和参数设置下都能表现出更稳定的性能.