Generating of Test Data by Harmony Search Against Genetic Algorithms

Many search-based algorithms have been successfully applied in sev-eral software engineering activities.Genetic algorithms(GAs)are the most used in the scientific domains by scholars to solve software testing problems.They imi-tate the theory of natural selection and evolution.The harmony search algorithm(HSA)is one of the most recent search algorithms in the last years.It imitates the behavior of a musician tofind the best harmony.Scholars have estimated the simi-larities and the differences between genetic algorithms and the harmony search algorithm in diverse research domains.The test data generation process represents a critical task in software validation.Unfortunately,there is no work comparing the performance of genetic algorithms and the harmony search algorithm in the test data generation process.This paper studies the similarities and the differences between genetic algorithms and the harmony search algorithm based on the ability and speed offinding the required test data.The current research performs an empirical comparison of the HSA and the GAs,and then the significance of the results is estimated using the t-Test.The study investigates the efficiency of the harmony search algorithm and the genetic algorithms according to(1)the time performance,(2)the significance of the generated test data,and(3)the adequacy of the generated test data to satisfy a given testing criterion.The results showed that the harmony search algorithm is significantly faster than the genetic algo-rithms because the t-Test showed that the p-value of the time values is 0.026<α(αis the significance level=0.05 at 95%confidence level).In contrast,there is no significant difference between the two algorithms in generating the adequate test data because the t-Test showed that the p-value of thefitness values is 0.25>α.

A global harmony search algorithm for finding optimal capacitor location and size in distribution networks

Shunt capacitors are broadly applied in distribution systems to scale down power losses, improve voltage profile and boost system capacity. The amount of capacitors added and location of deployment in the system highly determine the advantage of compensation. A novel global harmony search(GHS) algorithm in parallel with the backward/ forward sweep power flow technique and radial harmonic power flow was used to investigate the optimal placement and sizing of capacitors in radial distribution networks for minimizing power loss and total cost by taking account load unbalancing, mutual coupling and harmonics. The optimal capacitor placement outcomes show that the GHS algorithm can reduce total power losses up to 60 k W and leads to more than 18% of cost saving. The results also demonstrate that the GHS algorithm is more effective in minimization of power loss and total costs compared with genetic algorithm(GA), particle swarm optimization(PSO) and harmony search(HS) algorithm. Moreover, the proposed algorithm converges within 800 iterations and is faster in terms of computational time and gives better performance in finding optimal capacitor location and size compared with other optimization techniques.

Annealing Harmony Search Algorithm to Solve the Nurse Rostering Problem

A real-life problem is the rostering of nurses at hospitals.It is a famous nondeterministic,polynomial time(NP)-hard combinatorial optimization problem.Handling the real-world nurse rostering problem(NRP)constraints in distributing workload equally between available nurses is still a difficult task to achieve.The international shortage of nurses,in addition to the spread of COVID-19,has made it more difficult to provide convenient rosters for nurses.Based on the literature,heuristic-based methods are the most commonly used methods to solve the NRP due to its computational complexity,especially for large rosters.Heuristic-based algorithms in general have problems striking the balance between diversification and intensification.Therefore,this paper aims to introduce a novel metaheuristic hybridization that combines the enhanced harmony search algorithm(EHSA)with the simulated annealing(SA)algorithm called the annealing harmony search algorithm(AHSA).The AHSA is used to solve NRP from a Malaysian hospital.The AHSA performance is compared to the EHSA,climbing harmony search algorithm(CHSA),deluge harmony search algorithm(DHSA),and harmony annealing search algorithm(HAS).The results show that the AHSA performs better than the other compared algorithms for all the tested instances where the best ever results reported for the UKMMC dataset.

Sensor Layout of Hoisting Machinery Vibration Monitoring Based on Harmony Genetic Search Algorithm

With the construction of automated docks,health monitoring technology as a parallel safety assurance technology for unmanned hoisting machinery has become a hot spot in the development of the industry.Hoisting machinery has a huge structure and numerous welded joints.The complexity and nonlinearity of the welded structure itself makes the structural failure parts random and difficult to arrange for monitoring sensors.In order to solve the problem of effectiveness and stability of the sensor arrangement method for monitoring the structure of hoisting machinery.Using the global and local search capabilities enhanced by the complementary search mechanism,a structural vibration monitoring sensor placement algorithm based on the harmony genetic algorithm is proposed.Firstly,the model is established for modal analysis to obtain the displacement matrix of each mode.Secondly,the optimal parameter combination is established through parameter comparison,and the random search mechanism is used to quickly search in the modal matrix to obtain the preliminary solution,and then the preliminary solution is genetically summed The mutation operation obtains the optimized solution,and the optimal solution is retained through repeated iterations to realize the decision of the vibration sensor layout of the crane structure monitoring.Combining the comparison test of harmony genetic algorithm,harmony search algorithm and genet-ic algorithm,the fitness of harmony genetic algorithm in X,Y and Z directions were 0.0045,0.0084 and 0.0058,respectively,which were all optimal.And the average probability of deviating from the optimal path is 1.10%,19.34%,and 54.43%,which are also optimal.Harmony genetic algorithm has the advantages of simplicity,fastness and strong global search ability,and can obtain better fitness value and better search stability.

Modeling the Proposal of the Simultaneous Purchases and Sales of Electricity and Gas for the Energy Market in a Microgrid Using the Harmony Search Algorithm

The use of different energy carriers together,known as an energy hub,has been a hot topic of research in recent years amongst scientists and researchers.The term energy hub refers to the simultaneous operation of various infrastructures for energy generation and transfer,which has gained momentum in the form of microgrids(MGs).This paper introduces a new strategy for the optimal performance of an MG consisting of different energy carriers for each day.In a smart distribution network(DN),MGs can reduce their own costs in the previous-day market by bidding on sales and purchases.The sales and purchases bidding problem is challenging due to different uncertainties,however.This paper proposes a two-stage strategy for making an optimal bid on electricity sales and purchases with electricity and gas price dependency in the previous-day and real-time markets for an energy hub.In this model,the MG behavior regarding the electricity and gas energy sales/purchase,the simultaneous effects of electricity and gas prices,as well as the energy carriers’dependence on one another are all examined.Due to the inherent uncertainty in the sources of clean energy production,the probabilistic model and the production and reduction scenario have been used in the paper to cover this issue.In the proposed grid,energy sales/purchases are presented in a multi-carrier MG in a two-stage model.This model is solved by using the harmony search algorithm in MATLAB.Numeric results demonstrate the benefits of this model in reducing energy hub costs of operation.

Implementing an Optimal Energy Management System for a Set of Microgrids Using the Harmony Search Algorithm

A microgrid(MG)refers to a set of loads,generation resources and energy storage systems acting as a controllable load or a generator to supply power and heating to a local area.The MG-generated power management is a central topic for MG design and operation.The existence of dispersed generation(DG)resources has faced MG management with new issues.Depending on the level of exchanges between an MG and the main grid,the MG operation states can be divided into independent or grid-connected ones.Energy management in MGs aims to supply power at the lowest cost for optimal load response.This study examines MG energy management in two operational modes of islanded and grid-connected,and proposes a structure with two control layers(primary and secondary)for energy management.At the principal level of control,the energy management system is determined individually for all MG by taking into consideration the probability constraints and RES uncertainty by the Weibull the probability density function(PDF),generation resources’power as well as the generation surplus and deficit of each MG.Then,the information of the power surplus and deficit of each MG must be sent to the central energy management system.To confirm the proposed structure,a case system with two MGs and a condensive load is simulated by using a multi-time harmony search algorithm.Several scenarios are applied to evaluate the performance of this algorithm.The findings clearly show the effectiveness of the proposed system in the energy management of several MGs,leading to the optimal performance of the resources per MG.Moreover,the proposed control scheme properly controls the MG and grid’s performance in their interactions and offers a high level of robustness,stable behavior under different conditions and high quality of power supply.

Design of Sharp 2D Multiplier-Less Circularly Symmetric FIR Filter Using Harmony Search Algorithm and Frequency Transformation

In this paper, we present a novel and efficient method for the design of a sharp, two dimensional (2D) wideband, circularly symmetric, FIR filter. First of all, a sharp one dimensional (1D) infinite precision FIR filter is designed using the Frequency Response Masking (FRM) technique. This filter is converted into a multiplier-less filter by representing it in the Canonic Signed Digit (CSD) space. The design of the FRM filter in the CSD space calls for the use of a discrete optimization technique. To this end, a new optimization approach is proposed using a modified Harmony Search Algorithm (HSA). HSA is modified in such a way that, in every exploitation and exploration phase, the candidate solutions turns out to be integers. The 1D FRM multiplier-less filter, is in turn transformed to the 2D equivalent using the recently proposed multiplier-less transformations namely, T1 and T2. These transformations are successful in generating circular contours even for wideband filters. Since multipliers are the most power consuming elements in a 2D filter, the multiplier-less realization calls for reduced power consumption as well as computation time. Significant reduction in the computational complexity and computation time are the highlights of our proposed design technique. Besides, the proposed discrete optimization using modified HSA can be used to solve optimization problems in other engineering disciplines, where the search space consists of integers.

Optimization of Thermal Aware VLSI Non-Slicing Floorplanning Using Hybrid Particle Swarm Optimization Algorithm-Harmony Search Algorithm

Floorplanning is a prominent area in the Very Large-Scale Integrated (VLSI) circuit design automation, because it influences the performance, size, yield and reliability of the VLSI chips. It is the process of estimating the positions and shapes of the modules. A high packing density, small feature size and high clock frequency make the Integrated Circuit (IC) to dissipate large amount of heat. So, in this paper, a methodology is presented to distribute the temperature of the module on the layout while simultaneously optimizing the total area and wirelength by using a hybrid Particle Swarm Optimization-Harmony Search (HPSOHS) algorithm. This hybrid algorithm employs diversification technique (PSO) to obtain global optima and intensification strategy (HS) to achieve the best solution at the local level and Modified Corner List algorithm (MCL) for floorplan representation. A thermal modelling tool called hotspot tool is integrated with the proposed algorithm to obtain the temperature at the block level. The proposed algorithm is illustrated using Microelectronics Centre of North Carolina (MCNC) benchmark circuits. The results obtained are compared with the solutions derived from other stochastic algorithms and the proposed algorithm provides better solution.

Harmony Search-Dijkstra混合算法在铁路物流中心分层选址中的应用

为了满足铁路集中化运输的要求,基于铁路物流中心的层级特点,结合分层选址的理论和方法,以铁路物流中心覆盖的总需求最大化为目标,提出了铁路物流中心分层选址分配模型.针对铁路物流中心选址分配模型的特点,提出采用求解效率较高的和声搜索算法(Harmony Search,HS)和Dijkstra算法相结合的混合算法进行求解,并通过算例验证了模型的可行性和算法的有效性.为了验证算法的性能,对不同规模下的算法进行多次测试,结果表明当规模较大时,HS-Dijkstra混合算法仍可以在可接受的时间范围内收敛.为了体现算法的计算效率,对大规模下的Harmony Search-Dijkstra混合算法与遗传算法(Genetic Algorithm,GA)进行比较,结果显示Harmony Search-Dijkstra混合算法收敛速度明显优于遗传算法.因此,Harmony Search-Dijkstra混合算法可以有效解决铁路物流中心选址分配问题.

Approach to WTA in air combat using IAFSA-IHS algorithm

In this paper, a static weapon target assignment（WTA）problem is studied. As a critical problem in cooperative air combat,outcome of WTA directly influences the battle. Along with the cost of weapons rising rapidly, it is indispensable to design a target assignment model that can ensure minimizing targets survivability and weapons consumption simultaneously. Afterwards an algorithm named as improved artificial fish swarm algorithm-improved harmony search algorithm（IAFSA-IHS） is proposed to solve the problem. The effect of the proposed algorithm is demonstrated in numerical simulations, and results show that it performs positively in searching the optimal solution and solving the WTA problem.

Harmony search optimization applied to reservoir engineering assisted history matching

Based on the analysis of characteristics and advantages of HSO(harmony search optimization) algorithm, HSO was used in reservoir engineering assisted history matching of Kareem reservoir in Amal field in the Gulf of Suez, Egypt. HSO algorithm has the following advantages:(1) The good balance between exploration and exploitation techniques during searching for optimal solutions makes the HSO algorithm robust and efficient.(2) The diversity of generated solutions is more effectively controlled by two components, making it suitable for highly non-linear problems in reservoir engineering history matching.(3) The integration between the three components(harmony memory values, pitch adjusting and randomization) of the HSO helps in finding unbiased solutions.(4) The implementation process of the HSO algorithm is much easier. The HSO algorithm and two other commonly used algorithms(genetic and particle swarm optimization algorithms) were used in three reservoir engineering history match questions of different complex degrees, which are two material balance history matches of different scales and one reservoir history matching. The results were compared, which proves the superiority and validity of HSO. The results of Kareem reservoir history matching show that using the HSO algorithm as the optimization method in the assisted history matching workflow improves the simulation quality and saves solution time significantly.

改进HS算法在码垛机器人轨迹规划中的应用

目的 解决码垛机器人在抓取目标物时需要满足最大速度约束条件、较短运行时间以及运行平稳性等问题。方法 以Func200id六轴工业机器人为研究对象,通过机器人的各关节尺寸关系计算其DH参数,并建立仿真模型。对改进的HS算法通过增加变异因子VF来改变算法的搜索策略,使得算法中的每个个体不但有机会参与到迭代的过程之中,而且有概率向最优个体学习,加快算法的收敛速度。结果 通过对4种算法在6种不同测试函数下分别运行30次的对比实验可知,改进后的HS算法相较于其他3种算法的寻优能力更强,收敛速度更快。结论 改进后的HS算法能够明显缩短码垛机器人的工作时间,且运行连续平稳,在其他工业场景下也具有一定的应用价值。

基于HS-Bat算法的电网负荷卸载协调分配系统设计

多电网负荷联合分配后,系统中将产生较多的剩余负荷。为了实现剩余负荷的优化分配,设计了基于和声搜索-蝙蝠(HSBat)算法的电网负荷卸载协调分配系统。硬件设计调整了电网负荷卸载异步数据的接收装置,将时钟点位与异步数据调整为同步状态,以提升电网负荷卸载分配精度。软件设计基于HS-Bat计算电网剩余负荷最大值,建立电网负荷分配模型。设定单电网联合分配与多电网联合分配的目标函数,划分整体与局部功率的约束条件,设计电网负荷卸载协调分配算法。以剩余负荷峰谷差和剩余艾伦方差为指标进行性能测试。测试结果表明:设计系统应用后,剩余负荷峰谷差和剩余艾伦方差低于1.4,电网负荷分配效果更好,提高了电网负荷协调分配后的系统稳定性。

改进的HSA算法在水利工程项目工期—成本优化中的应用研究

为解决工期-成本综合优化问题,对和声搜索算法(Harmony search algorithm,HSA)的参数取值范围进行改进。首先分析了水利工程项目工期与成本估算的影响因素,构建了工期-成本集成管理模型,然后构建BIM模型提取工程量信息,同时对HSA的主要参数记忆库取值概率HMCR与和声微调概率PAR作了改进,使其参数范围更加适应工期—成本模型的取值,整个运行过程耗时短,精度高,模型经过改进后更易于实现运算,并借助MATLAB得到了工期-成本最优解。最后,以某水闸项目为例进行验证,为水利工程项目工期-成本优化探索了新的模式。

基于IHS_RELM的网络安全态势预测方法

针对网络安全态势感知中的态势预测问题,提出一种基于IHS_RELM的网络安全态势预测方法。对和声搜索算法的原理进行了研究,在此基础上提出一种改进的和声搜索算法。将正则极速学习机(RELM)嵌入到改进的和声搜索算法(IHS)的目标函数计算过程中,利用IHS算法的全局搜索能力来优化选取RELM的输入权值和隐含层阈值,在一定程度上提升了RLLM的学习能力和泛化能力。仿真实验表明,与已有的其他预测方法相比,该方法具有更好的预测效果。

基于IHS_LSSVR的网络安全态势预测方法

针对网络安全态势感知中的态势预测问题,提出一种基于IHS_LSSVR的网络安全态势预测方法。对和声搜索算法(HS)的原理进行了研究,在该基础上提出一种改进的和声搜索算法(IHS)。将最小二乘支持向量回归机(L-SSVR)嵌入到改进的和声搜索算法(IHS)的目标函数计算过程中,利用IHS算法的全局搜索能力来优化选取LSSV-R的参数,在一定程度上提升了LSSVR的学习能力和泛化能力。仿真实验表明,通过与已有的其他预测方法作对比,该方法具有更好的预测效果。

基于QHSME的装备危险耦合传导GERT分析

针对装备活动中危险耦合传导关系描述不清楚、分析不具体的问题,构建考虑任意分布的危险耦合传导图形评审技术(graph evaluation and review technique,GERT)改进模型。提出了系统危险度、危险耦元重要度、危险路径隶属度等扩展分析参数,进一步刻画装备活动中的微观危险信息;采用系统耦合理论和极大熵方法(maximum entropy,ME)配置GERT模型变量,并设计量子和声算法(quantum harmony search algorithm,QHS)求解危险度概率密度极大熵模型。最后,以飞机大表速低空俯冲科目为例进行分析。结果表明,与传统GERT模型比较,该改进模型能充分反映危险耦元对装备活动随机过程的交互影响关系,以及其他相关微观信息,且具有较好的求解精度与适用性。

OLPDHS算法在雷达组网优化部署中的应用

为解决优化部署问题,首先与其他元启发式算法相比,分析了和声搜索算法的特色,并推导出参数的理论关系;然后,将相对立学习概念引入HS算法,结合参数调整关系,提出了相对立学习参数调整和声搜索算法,并通过仿真实验与其他HS算法在收敛速度和精度两方面进行了比较;最后,以空域体积建立目标函数作为雷达组网优化部署的数学模型,利用该算法进行寻优部署,结果表明:该算法可以较快地得出最优部署方案,操作性强。

基于混合和谐搜索算法的自压滴灌田间管网多目标优化设计

为了综合考虑管网投资和水压分布,合理进行管网布置和管径选择,以满足管网的经济性和可靠性,使自压滴灌管网系统达到投资最低和灌水均匀度最高的目的。以管网投资最低(管网经济性)和节点富余水头均值最小(管网可靠性)为目标,建立了自压滴灌“梳子”形和“丰字”形田间管网系统的多目标优化数学模型,提出了混合和谐搜索算法求解多目标优化模型的方法和步骤。以新疆某自压滴灌工程为例,采用该方法对其田间管网系统进行优化,优化方案的管网总投资为47.63万元,与原设计方案相比降低了9.86%;管网节点富余水头均值为8.87 m,与原设计方案相比降低了28.37%。该优化设计方法得到的优化结果,不仅节省投资明显,而且节点富余水头均值显著减小,说明管网系统的压力偏差小,灌水的均匀度显著提高。优化结果表明,混合和谐搜索算法收敛速度快,计算精度高,在自压滴灌管网的多目标优化设计中具有很好的应用价值。

基于改进和声搜索的配电网WSNs多路径QoS路由算法

针对智能配电网(Smart distribution network,SDN)中无线通信数据传输不可靠且网络寿命低等问题,提出一种基于改进和声搜索的无线传感器网络(Wireless sensor networks,WSNs)多路径QoS路由算法(Multipath QoS routing algorithm,MQRA)。首先,分析配电网的通信需求并建立数据传输模型;然后,针对无线传感器路由问题的特点对和声搜索算法(Harmony search,HS)做出改进;再将其自适应参数公式进行改进以避免算法陷入局部最优;通过在源节点与目的节点之间构建前传区域来优化转发节点集以避免数据回传,提升算法的运行效率;最后,在初始和声库生成阶段,综合考虑距离、丢包率以及节点剩余能量来改进轮盘法中的概率公式。仿真结果表明MQRA算法能有效降低数据传输的时延,提升数据可靠性并均衡节点能耗延长网络使用寿命。