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 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.

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.

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.

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.

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.

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.

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.

Parameter Optimization of Tuned Mass Damper Inerter via Adaptive Harmony Search

Dynamic impacts such as wind and earthquakes cause loss of life and economic damage.To ensure safety against these effects,various measures have been taken from past to present and solutions have been developed using different technologies.Tall buildings are more susceptible to vibrations such as wind and earthquakes.Therefore,vibration control has become an important issue in civil engineering.This study optimizes tuned mass damper inerter(TMDI)using far-fault ground motion records.This study derives the optimum parameters of TMDI using the Adaptive Harmony Search algorithm.Structure displacement and total acceleration against earthquake load are analyzed to assess the performance of the TMDI system.The effect of the inerter when connected to different floors is observed,and the results are compared to the conventional tuned mass damper(TMD).It is indicated that the case of connecting the inerter force to the 5th floor gives better results.As a result,TMD and TMDI systems reduce the displacement by 21.87%and 25.45%,respectively,and the total acceleration by 25.45%and 19.59%,respectively.These percentage reductions indicated that the structure resilience against dynamic loads can be increased using control systems.

A modified harmony search algorithm and its applications in weighted fuzzy production rule extraction

Harmony search(HS)is a form of stochastic meta-heuristic inspired by the improvisation process of musicians.In this study,a modified HS with a hybrid cuckoo search(CS)operator,HS-CS,is proposed to enhance global search ability while avoiding falling into local optima.First,the randomness of the HS pitch disturbance adjusting method is analyzed to generate an adaptive inertia weight according to the quality of solutions in the harmony memory and to reconstruct the fine-tuning bandwidth optimization.This is to improve the efficiency and accuracy of HS algorithm optimization.Second,the CS operator is introduced to expand the scope of the solution space and improve the density of the population,which can quickly jump out of the local optimum in the randomly generated harmony and update stage.Finally,a dynamic parameter adjustment mechanism is set to improve the efficiency of optimization.Three theorems are proved to reveal HS-CS as a global convergence meta-heuristic algorithm.In addition,12 benchmark functions are selected for the optimization solution to verify the performance of HS-CS.The analysis shows that HS-CS is significantly better than other algorithms in optimizing high-dimensional problems with strong robustness,high convergence speed,and high convergence accuracy.For further verification,HS-CS is used to optimize the back propagation neural network(BPNN)to extract weighted fuzzy production rules.Simulation results show that the BPNN optimized by HS-CS can obtain higher classification accuracy of weighted fuzzy production rules.Therefore,the proposed HS-CS is proved to be effective.

Harmony Search Algorithm Based on Dual-Memory Dynamic Search and Its Application on Data Clustering

Harmony Search(HS)algorithm is highly effective in solving a wide range of real-world engineering optimization problems.However,it still has the problems such as being prone to local optima,low optimization accuracy,and low search efficiency.To address the limitations of the HS algorithm,a novel approach called the Dual-Memory Dynamic Search Harmony Search(DMDS-HS)algorithm is introduced.The main innovations of this algorithm are as follows:Firstly,a dual-memory structure is introduced to rank and hierarchically organize the harmonies in the harmony memory,creating an effective and selectable trust region to reduce approach blind searching.Furthermore,the trust region is dynamically adjusted to improve the convergence of the algorithm while maintaining its global search capability.Secondly,to boost the algorithm’s convergence speed,a phased dynamic convergence domain concept is introduced to strategically devise a global random search strategy.Lastly,the algorithm constructs an adaptive parameter adjustment strategy to adjust the usage probability of the algorithm’s search strategies,which aim to rationalize the abilities of exploration and exploitation of the algorithm.The results tested on the Computational Experiment Competition on 2017(CEC2017)test function set show that DMDS-HS outperforms the other nine HS algorithms and the other four state-of-the-art algorithms in terms of diversity,freedom from local optima,and solution accuracy.In addition,applying DMDS-HS to data clustering problems,the results show that it exhibits clustering performance that exceeds the other seven classical clustering algorithms,which verifies the effectiveness and reliability of DMDS-HS in solving complex data clustering problems.

Solving unit commitment problem using a novel version of harmony search algorithm

In this context, a novel structure was proposed for improving harmony search （HS） algorithm to solve the unit comment （UC） problem. The HS algorithm obtained optimal solution for defined objective function by improvising, updating and checking operators. In the proposed improved self-adaptive HS （SGHS） algorithm, two important control parameters were adjusted to reach better solution from the simple HS algorithm. The objective function of this study consisted of operation, start-up and shut-down costs. To confirm the effectiveness, the SGHS algorithm was tested on systems with 10, 20, 40 and 60 generating units, and the obtained results were compared with those of the simple HS algorithm and other related works.

Model penicillin fermentation by least squares support vector machine with tuning based on amended harmony search

Penicillin fermentation is an important part of microbial fermentation. Due to the existence of error date in the independent variables and dependent variables of the penicillin fermentation sample data, the accuracy of the model of penicillin fermentation is affected. In this paper, an amended harmony search （AHS） algorithm is developed to adjust the hyper-parameters of least squares support vector machine （LS-SVM） in order to build penicillin fermentation process model with prediction accuracy. The AHS algorithm is investigated by unconstrained benchmark functions with different characteristics. Compared with other several optimization approaches, AHS demonstrates a better performance. Moreover, using the simulation data from the PenSim simulation platform to validate the effectiveness of the penicillin fermentation process modeling, experiment results show that the penicillin fermentation process modeling based on the tuned LS-SVM by AHS possesses robustness and generalization ability.

Pareto-based multi-objective node placement of industrial wireless sensor networks using binary differential evolution harmony search

The reliability and real time of industrial wireless sensor networks （IWSNs） are the absolute requirements for industrial systems, which are two fore- most obstacles for the large-scale applications of IWSNs. This paper studies the multi-objective node placement problem to guarantee the reliability and real time of IWSNs from the perspective of systems. A novel multi-objective node deployment model is proposed in which the reliabil- ity, real time, costs and scalability of IWSNs are addressed. Considering that the optimal node placement is an NP-hard problem, a new multi-objective binary differential evolu- tion harmony search （MOBDEHS） is developed to tackle it, which is inspired by the mechanism of harmony search and differential evolution. Three large-scale node deploy- ment problems are generated as the benCHmarks to verify the proposed model and algorithm. The experimental results demonstrate that the developed model is valid and can be used to design large-scale IWSNs with guaranteed reliability and real-time performance efficiently. Moreover, the comparison results indicate that the proposed MOB- DEHS is an effective tool for multi-objective node place- ment problems and superior to Pareto-based binary differential evolution algorithms, nondominated sorting genetic algorithm II （NSGA-II） and modified NSGA-II.

A novel global harmony search method based off-line tuning of RFNN for adaptive control of uncertain nonlinear systems

Purpose–The two main purposes of this paper are:first,the development of a new optimization algorithm called GHSACO by incorporating the global-best harmony search(GHS)which is a stochastic optimization algorithm recently developed,with the ant colony optimization(ACO)algorithm.Second,design of a new indirect adaptive recurrent fuzzy-neural controller(IARFNNC)for uncertain nonlinear systems using the developed optimization method(GHSACO)and the concept of the supervisory controller.Design/methodology/approach–The novel optimization method introduces a novel improvization process,which is different from that of the GHS in the following aspects:a modified harmony memory representation and conception.The use of a global random switching mechanism to monitor the choice between the ACO and GHS.An additional memory consideration selection rule using the ACO random proportional transition rule with a pheromone trail update mechanism.The developed optimization method is applied for parametric optimization of all recurrent fuzzy neural networks adaptive controller parameters.In addition,in order to guarantee that the system states are confined to the safe region,a supervisory controller is incorporated into the IARFNNC global structure.Findings–First,to analyze the performance of GHSACO method and shows its effectiveness,some benchmark functions with different dimensions are used.Simulation results demonstrate that it can find significantly better solutions when compared with the Harmony Search(HS),GHS,improved HS(IHS)and conventional ACO algorithm.In addition,simulation results obtained using an example of nonlinear system shows clearly the feasibility and the applicability of the proposed control method and the superiority of the GHSACO method compared to the HS,its variants,particle swarm optimization,and genetic algorithms applied to the same problem.Originality/value–The proposed new GHS algorithm is more efficient than the original HS method and its most known variants IHS and GHS.The proposed control method is applicable to any uncertain nonlinear system belongs in the class of systems treated in this paper.

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.

Adaptive Cloud Intrusion Detection System Based on Pruned Exact Linear Time Technique

Cloud computing environments,characterized by dynamic scaling,distributed architectures,and complex work-loads,are increasingly targeted by malicious actors.These threats encompass unauthorized access,data breaches,denial-of-service attacks,and evolving malware variants.Traditional security solutions often struggle with the dynamic nature of cloud environments,highlighting the need for robust Adaptive Cloud Intrusion Detection Systems(CIDS).Existing adaptive CIDS solutions,while offering improved detection capabilities,often face limitations such as reliance on approximations for change point detection,hindering their precision in identifying anomalies.This can lead to missed attacks or an abundance of false alarms,impacting overall security effectiveness.To address these challenges,we propose ACIDS(Adaptive Cloud Intrusion Detection System)-PELT.This novel Adaptive CIDS framework leverages the Pruned Exact Linear Time(PELT)algorithm and a Support Vector Machine(SVM)for enhanced accuracy and efficiency.ACIDS-PELT comprises four key components:(1)Feature Selection:Utilizing a hybrid harmony search algorithm and the symmetrical uncertainty filter(HSO-SU)to identify the most relevant features that effectively differentiate between normal and anomalous network traffic in the cloud environment.(2)Surveillance:Employing the PELT algorithm to detect change points within the network traffic data,enabling the identification of anomalies and potential security threats with improved precision compared to existing approaches.(3)Training Set:Labeled network traffic data forms the training set used to train the SVM classifier to distinguish between normal and anomalous behaviour patterns.(4)Testing Set:The testing set evaluates ACIDS-PELT’s performance by measuring its accuracy,precision,and recall in detecting security threats within the cloud environment.We evaluate the performance of ACIDS-PELT using the NSL-KDD benchmark dataset.The results demonstrate that ACIDS-PELT outperforms existing cloud intrusion detection techniques in terms of accuracy,precision,and recall.This superiority stems from ACIDS-PELT’s ability to overcome limitations associated with approximation and imprecision in change point detection while offering a more accurate and precise approach to detecting security threats in dynamic cloud environments.

Efficient Heuristic Replication Techniques for High Data Availability in Cloud

Most social networks allow connections amongst many people based on shared interests.Social networks have to offer shared data like videos,photos with minimum latency to the group,which could be challenging as the storage cost has to be minimized and hence entire data replication is not a solution.The replication of data across a network of read-intensive can potentially lead to increased savings in cost and energy and reduce the end-user’s response time.Though simple and adaptive replication strategies exist,the solution is non-deter-ministic;the replicas of the data need to be optimized to the data usability,perfor-mance,and stability of the application systems.To resolve the non-deterministic issue of replication,metaheuristics are applied.In this work,Harmony Search and Tabu Search algorithms are used optimizing the replication process.A novel Har-mony-Tabu search is proposed for effective placement and replication of data.Experiments on large datasets show the effectiveness of the proposed technique.It is seen that the bandwidth saving for proposed harmony-Tabu replication per-forms better in the range of 3.57%to 18.18%for varying number of cloud data-centers when compared to simple replication,Tabu replication and Harmony replication algorithm.

Hybridized Artificial Neural Network for Automated Software Test Oracle

Software testing is the methodology of analyzing the nature of software to test if it works as anticipated so as to boost its reliability and quality.These two characteristics are very critical in the software applications of present times.When testers want to perform scenario evaluations,test oracles are generally employed in the third phase.Upon test case execution and test outcome generation,it is essential to validate the results so as to establish the software behavior’s correctness.By choosing a feasible technique for the test case optimization and prioritization as along with an appropriate assessment of the application,leads to a reduction in the fault detection work with minimal loss of information and would also greatly reduce the cost for clearing up.A hybrid Particle Swarm Optimization(PSO)with Stochastic Diffusion Search(PSO-SDS)based Neural Network,and a hybrid Harmony Search with Stochastic Diffusion Search(HS-SDS)based Neural Network has been proposed in this work.Further to evaluate the performance,it is compared with PSO-SDS based artificial Neural Network(PSO-SDS ANN)and Artificial Neural Network(ANN).The Misclassification of correction output(MCO)of HS-SDS Neural Network is 6.37 for 5 iterations and is well suited for automated testing.

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.