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.

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.

A novel hybrid algorithm based on a harmony search and artificial bee colony for solving a portfolio optimization problem using a mean-semi variance approach

Portfolio selection is one of the major capital allocation and budgeting issues in financial management, and a variety of models have been presented for optimal selection. Semi-variance is usually considered as a risk factor in drawing up an efficient frontier and the optimal portfolio. Since semi-variance offers a better estimation of the actual risk portfolio, it was used as a measure to approximate the risk of investment in this work. The optimal portfolio selection is one of the non-deterministic polynomial(NP)-hard problems that have not been presented in an exact algorithm, which can solve this problem in a polynomial time. Meta-heuristic algorithms are usually used to solve such problems. A novel hybrid harmony search and artificial bee colony algorithm and its application were introduced in order to draw efficient frontier portfolios. Computational results show that this algorithm is more successful than the harmony search method and genetic algorithm. In addition, it is more accurate in finding optimal solutions at all levels of risk and return.

Harmony search algorithm with differential evolution based control parameter co-evolution and its application in chemical process dynamic optimization

A modified harmony search algorithm with co-evolutional control parameters(DEHS), applied through differential evolution optimization, is proposed. In DEHS, two control parameters, i.e., harmony memory considering rate and pitch adjusting rate, are encoded as a symbiotic individual of an original individual(i.e., harmony vector). Harmony search operators are applied to evolving the original population. DE is applied to co-evolving the symbiotic population based on feedback information from the original population. Thus, with the evolution of the original population in DEHS, the symbiotic population is dynamically and self-adaptively adjusted, and real-time optimum control parameters are obtained. The proposed DEHS algorithm has been applied to various benchmark functions and two typical dynamic optimization problems. The experimental results show that the performance of the proposed algorithm is better than that of other HS variants. Satisfactory results are obtained in the application.

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.

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.

Harmony Search and Cellular Automata in Spatial Optimization

The combined optimization problem of resource production and allocation is considered. The spatial character of the problem is emphasized and cellular modeling is introduced. First a new enhanced harmony search algorithm is applied combined with cellular concepts. Then another new approach is presented involving a cellular automaton combined with harmony search. This second approach renders solutions with greater compactness, a desirable characteristic in spatial optimization. The two algorithms are compared and discussed.

Hybrid Discrete Harmony Search Algorithm for Flow Shop Scheduling with Limited Buffers

The flow shop scheduling problem with limited buffers( LBFSP) widely exists in manufacturing systems. A hybrid discrete harmony search algorithm is proposed for the problem to minimize total flow time. The algorithm presents a novel discrete improvisation and a differential evolution scheme with the jobpermutation-based representation. Moreover,the discrete harmony search is hybridized with the problem-dependent local search based on insert neighborhood to balance the global exploration and local exploitation. In addition, an orthogonal experiment design is employed to provide a receipt for turning the adjustable parameters of the algorithm. Comparisons based on the Taillard benchmarks indicate the superiority of the proposed algorithm in terms of effectiveness and efficiency.

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.

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.

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.

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.

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.

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.

应用HS改进原子分解的电能质量扰动辨识分析

结合原子分解与和声搜索算法,提出了一种电能质量扰动信号自适应分解及特征参数辨识方法。针对Gabor原子分解匹配追踪算法计算量大、实时性差的问题,首先利用傅里叶变换进行频谱分析,估计出扰动信号频率、幅值等参数,并以估计值作为搜索的初始解,加快算法的收敛速度;然后根据电能质量扰动信号特点将Gabor原子库分解为类基波库、脉冲库、谐波库、振荡库4个子库,依次搜索各子库,降低搜索的复杂度;再次,利用和声搜索算法快速、准确的全局搜索和协同搜索的特点对匹配追踪算法进行改进,加快了搜索速度;最后,依据获得Gabor原子索引参数实现电能质量扰动信号参数辨识。算例仿真表明,所提方法在保留匹配追踪算法优良重构性能的前提下,计算复杂度显著降低,搜索效率和收敛速度加快,扰动参数辨识精度得到提高。

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HS-PSO算法的PID控制器参数自整定

针对粒子群算法(PSO)算法局部搜索能力差的问题,提出一种对PID控制器参数进行自整定的基于和声搜索(HS)的改进粒子群优化算法(HS-PSO)。通过引入种群进程因子对惯性权重进行自适应调节以提高PSO算法的收敛速度。另外在PSO进化过程中每代产生的最优个体以新陈代谢方式进入和声记忆库中并进行和声搜索,以克服粒子群优化算法局部搜索能力差的缺陷。针对典型对象进行PID控制器参数自整定,仿真和工程应用结果表明所提HS-PSO算法较他它智能优化算法具有更好的全局优化能力。

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。