Harmony Search Optimization Algorithm Research Articles

Enhancing Ovarian Cancer Detection Using Self-Organizing Maps and Improved Recurrent Neural Networks with Extended Harmony Search Optimization

Background: Ovarian cancer (OC) is a highly fatal malignancy of the female reproductive system, characterized by its high mortality rate and the challenges associated with clinical research due to the disease's complexity and late-stage diagnosis. Advances in technology, such as the Internet of Medical Things (IoMT), offer new opportunities for improving OC detection and diagnosis. Objective: This study aimed to develop and evaluate a novel method for OC detection using IoMT data, leveraging Self-Organizing Maps (SOM) and Improved Recurrent Neural Networks (IRNN) enhanced with the Extended Harmony Search Optimization (EHSO) algorithm to improve feature selection and classification accuracy. Methods: The study utilized OC data from the IoMT and applied SOM for feature selection, which helps in managing and classifying large datasets. SOM was employed to improve data representation and address challenges in labeling and classifying data. The IRNN model, optimized using the EHSO algorithm, was developed to enhance classification performance. The model was tested using a dataset from Kaggle comprising 179 benign and 172 malignant OC images with 50 attributes. Results: The IRNN model with EHSO demonstrated superior performance compared to other methods. In the training dataset, it achieved an accuracy of 95.72% and a Root Mean Square Error (RMSE) of 4.8%. For the testing dataset, the model maintained a high accuracy of 90.4% and an RMSE of 6.8%. The IRNN with EHSO outperformed alternative methods in terms of specificity and sensitivity, while the Genetic Algorithm (GA) showed the lowest performance across all metrics. Conclusion: The proposed method using SOM and IRNN with EHSO significantly improves the detection of ovarian cancer by optimizing feature selection and classification accuracy. This approach offers a promising advancement in utilizing IoMT data for early and accurate OC diagnosis, potentially enhancing patient outcomes through more effective detection and treatment strategies.

Image Multi-threshold Segmentation Based on an Ameliorated Harmony Search Optimization Algorithm

Image segmentation is the basis and premise of image processing, though traditional multi-threshold image segmentation methods are simple and effective, they suffer the problems of low accuracy and slow convergence rate. For that reason, this paper introduces the multi-threshold image segmentation scheme by combining the harmony search (HS) optimization algorithm and the maximum between-class variance (Otsu) to solve them. Firstly, to further improve the performance of the basic HS, an ameliorated harmony search (AHS) is put forward by modifying the generation method of the new harmony improvisation and introducing a convergence coefficient. Secondly, the AHS algorithm, which takes the maximum between-class variance as its objective function, namely AHS-Otsu, is applied to image multi-level threshold segmentation. Finally, six test images are selected to verify the multilevel segmentation performance of AHS-Otsu. Peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) are two commonly used metrics for evaluating the effectiveness of image segmentation, which are both used in this article. Comprehensive experimental results indicate that the AHS-Otsu does not only has fast segmentation processing speed, but also can obtain more accurate segmentation performance than others, which prove the effectiveness and potential of the AHS-Otsu algorithm in the field of image segmentation especially for the multi-threshold.

Modeling and optimization of a hybrid solar-battery-diesel power system for remote consumers.

Hybrid power systems can be affected by various uncertain parameters such as technical, economic, and environmental factors. These parameters may have both positive and negative impacts on the overall performance of the system. Therefore, in this study, an effective optimization method for modeling and optimization of a hybrid solar-battery-diesel power structure for remote consumers is proposed. The purpose of this method is to find the optimal configuration of the hybrid structure from the technical-economical-environmental point of view to cover the load demand requirements of remote consumers. To verify the performance of the hybrid microgrid, the results of the hybrid system based on the hourly meteorological data and load profile are compared with the results of the conventional diesel generator (DG). The optimization problem is solved using a harmony search optimization algorithm. The results show that the solar/battery/DG system is more suitable than the DG-only system in different uncertainty indexes based on economic and environmental aspects. Also, by the increase of the uncertainty index from 0.5 to 10%, the optimal total cost of the system decreases by 36.5%, while at the same time the unavailability value increases from 0.2259% to 9.9568%. Additionally, when the interest rate value increases from 1 to 7%, the total cost value increases around 17. 4%.

Hybrid Harmony Search Optimization Algorithm for Continuous Functions

This paper proposes a hybrid harmony search algorithm that incorporates a method of reinitializing harmonies memory using a particle swarm optimization algorithm with an improved opposition-based learning method (IOBL) to solve continuous optimization problems. This method allows the algorithm to obtain better results by increasing the search space of the solutions. This approach has been validated by comparing the performance of the proposed algorithm with that of a state-of-the-art harmony search algorithm, solving fifteen standard mathematical functions, and applying the Wilcoxon parametric test at a 5% significance level. The state-of-the-art algorithm uses an opposition-based improvement method (IOBL). Computational experiments show that the proposed algorithm outperforms the state-of-the-art algorithm. In quality, it is better in fourteen of the fifteen instances, and in efficiency is better in seven of fifteen instances.

Extremum hybrid intelligent-inspired models for accurate predicting mechanical performances of turbine blisk

In complex aeroengine structures, it is necessary to consider multi-physical loads involving vibrational, structural, aerodynamical and heat loads for safe design. Turbine blisk commonly involves multi-failure modes such as stress, strain, deformation, fatigue, and creep under multi-physical loads during operation, so that efficient and accurate simulating approach is one of main issues in structural safety design. Machine learning (ML) approaches using artificial intelligence (AI) are workable to improve the simulation efficiency of mechanical responses under nonlinear dynamic structural analysis. In this work, hybrid artificial neural network (ANN) models are proposed to simulate the failure modes of turbine blisk. The accuracy of AI-based ANN is discussed using six music-inspired optimization algorithms named as harmony search (HS), improved HS (IHS), global-best HS (GHS), improved GHS (IGHS), adaptive GHS (AGHS) and Gaussian GHS (GGHS). Herein, these six music-inspired optimization algorithms are employed to find the undetermined coefficients and hyperparameters for the ANN models, and two adjusting strategy is introduced to explore the optimal values of the undetermined coefficients and hyperparameters in the IGHS, AGHS and GGHS. These hybrid models are used to perform the approximation of dimensional mechanical capacities such as stress, strain and deformation of aeroengine structures. These hybrid models are compared by using tendency, accuracy and agreement for failure modes of turbine system. It was conducted that the music-inspired basis harmony optimization procedures can provide the acceptable nonlinear connection between input and output responses of aeroengine turbine blisk in training phase of ANN models. The GGHS algorithm shows superior performance with the highest accuracy and tendency among other HS optimization algorithms.

Design Optimization of PCI Girders: A Parametric Study

This study investigates the effect of superstructure configuration on the optimum design of slab on Precast I (PCI) girder bridges. For this purpose, more than 20,000 bridge cases of varying superstructure configurations are considered to investigate the effects of various superstructure parameters such as girder spacing, span length, slab thickness and girder types on the optimum design of slab on PCI girder bridges. PCI girders are designed conforming to the AASHTO LRFD for flexure using stress limits at the service limit state, then checked at ultimate for flexure and shear using factored loads at the strength limit state. A modified harmony search optimization algorithm is used to obtain optimum bridge design parameters using standard AASHTO PCI girders according to these AASHTO LRFD requirements. Those girders are designed taking into consideration geometrical constraints, stress constraints and constraints related to the conformity of the design with the AASHTO LRFD code. Various sensitivity analysis are performed to investigate the effect of different geometrical factors on the design of the girders, and easy-to-use design aids were developed. The outcomes of this study may facilitate the bridge engineers to choose optimum design parameters such as girder types and spacing as well as number strands for a certain span length before the design of slab on PCI girder bridges.

Categorical Data Clustering Using Harmony Search Algorithm for Healthcare Datasets

Healthcare analytics provide many benefits in healthcare dashboard systems. Healthcare datasets majorly contains categorical attributes. This paper proposed an optimized clustering for healthcare dataset named harmony search based categorical clustering (HSCC). The existing k-modes clustering algorithm is one of the well-known categorical data-clustering algorithm. Since the k-modes algorithm produces local optimal clusters. Generally, researchers use genetic algorithm (GA) based clustering algorithms to converge locally optimal solutions to global optimal solutions. GA has some deficiencies such as premature convergence with low speed. In this paper, harmony search (HS) optimization algorithm used to optimize clustering results. The result shows the proposed HSCC algorithm produced global optimized solution, unbiased and matured results. HSCC produces 98% accuracy for dental and 71% for lung cancer dataset. While GACC produces 95% and 65% accuracy for dental dataset and lung cancer dataset.

A new intelligent approach for size optimization of a renewable energy based grid connected hybrid energy system

AbstractAdopting hybrid power systems to provide access to clean, reliable, and inexpensive energy is necessary for countries like India to accomplish their goals of sustainable development. This work proposes an optimization approach for sizing a grid‐connected hybrid renewable energy system (HRES) that includes photovoltaic, biomass, biogas, and a battery. In this research, a recently developed Aquila optimization algorithm is utilized to evaluate the optimal size of HRES for a cluster of villages near Sarai Jairam, Agra district, Uttar Pradesh, India using the MATLAB software package. The goal of this optimization approach is to reduce the net present cost for HRES while satisfying the operational constraints, including the reliability of the power supply and maximum utilization of the photovoltaic, biomass, and biogas complementary properties. Initially, three HRES configurations in the off‐grid mode were optimized using the suggested Aquila optimization technique. To confirm the findings, the optimization results for the same models of off‐grid HRES were obtained using the harmony search (HS) and particle swarm optimization (PSO) algorithms. The same algorithms were also used to optimize the grid‐connected SPV/biomass/biogas with the battery model of HRES. Finally, the outcomes from the off‐grid models presented above were compared to the grid‐linked model. The grid‐connected configuration which is obtained by the Aquila optimization approach provides an optimal solution with the least net present cos t (NPC) and minimum cost of energy (COE) when compared with HS and PSO. The optimal grid‐connected HRES includes 235 kW of PV, a 10 kW biogas generator, a 64 kW biomass generator, and a 50.40 kWh battery bank. The overall net present cost and the COE are found to be $547 670 and $0.0768/kWh, respectively.

A novel PID controller for BLDCM speed control using dual fuzzy logic systems with HSA optimization

In order to enhance the speed control performance of the brushless DC motor (BLDCM), a novel proportion integration differentiation (PID) is proposed in this paper by using dual fuzzy logic systems (FLSs) with harmony search algorithm (HSA) optimization, which is called DFPID-HSA. Firstly, the FLS1 in DFPID-HSA locks the three coefficients of the PID controller in an extensive range on the basis of the system error and error change rate. Then, the FLS2 is optimized by HSA (HSA-F2) to obtain the precise correction of the three coefficients. To get the optimal global harmony better, the improved dynamic adjustment mode is used for the pitch adjustment rate (PAR) and distance bandwidth (BW) in HSA, and the triple selection method is adopted in the composition harmony section to realize the global search. Finally, DFPID-HSA provides the optimal supply control signal to BLDCM so that it can control the speed effectively. Moreover, the stability of the system is analyzed by the pole, Lyapunov, and Nyquist determination methods. And the sensitivity analysis of DFPID-HSA is carried out under the condition of different motor’s mechanical parameters to check its robustness. In addition, the superiority of DFPID-HSA is verified by MATLAB simulation and experiment platform.

Modeling of wave run-up by applying integrated models of group method of data handling

Wave-induced inundation in coastal zones is a serious problem for residents. Accurate prediction of wave run-up height is a complex phenomenon in coastal engineering. In this study, several machine learning (ML) models are developed to simulate wave run-up height. The developed methods are based on optimization techniques employing the group method of data handling (GMDH). The invasive weed optimization (IWO), firefly algorithm (FA), teaching–learning-based optimization (TLBO), harmony search (HS), and differential evolution (DE) meta-heuristic optimization algorithms are embedded with the GMDH to yield better feasible optimization. Preliminary results indicate that the developed ML models are robust tools for modeling the wave run-up height. All ML models’ accuracies are higher than empirical relations. The obtained results show that employing heuristic methods enhances the accuracy of the standard GMDH model. As such, the FA, IWO, DE, TLBO, and HS improve the RMSE criterion of the standard GMDH by the rate of 47.5%, 44.7%, 24.1%, 41.1%, and 34.3%, respectively. The GMDH-FA and GMDH-IWO are recommended for applications in coastal engineering.

An Improved Harmony Search Approach for Block Placement for VLSI Design Automation

The technology grows quickly in the area of the VLSI physical design; it is crucial to integrate the greater number of transistors and parts into a very small range. Before the placement is completed, the physical and technical positioning of the blocks in the chip area is planned, which is nothing but floor planning. In order to lessen the placement region in the physical layout, floor planning must be carried out effectively. This paper proposes a blended harmony search and particle swarm optimization (BHSPS) algorithm which is the deliberate blend of the harmony search (HS) algorithm, and the particle swarm optimization (PSO) algorithm is proposed to acquire the central goal of the VLSI placement strategy. The objective here is to lessen the field of plan. The MATLAB code for the blended harmony search and particle swarm optimization (BHSPS) algorithm is compiled, and investigations were carried out for better examination through the standard MCNC, i.e., North Carolina Microelectronics Center benchmark circuits.

Application of the Harmony Search Algorithm for Optimization of WDN and Assessment of Pipe Deterioration

The relevance of the optimal design of water distribution networks lies in its applicability. Thus, the design must be as efficient and affordable as possible, meaning that it achieves a minimum level of serviceability using an appropriate amount of resources. In this context, an ideal water network requires the use of minimum-size elements to reach the minimum head pressure required for each node of the system with the lowest energy consumption. For this purpose, a relatively new meta-heuristic algorithm, called Harmony Search, was used in this study to optimize the water distribution network of Cheongna International City, Korea, in terms of the pipe diameter. Furthermore, using the same algorithm under a different approach, an assessment of the deterioration of conduits over time was conducted by analyzing the optimal roughness coefficient for the original pipe material, which provides a useful tool for decision making. Other heuristic and meta-heuristic algorithms have been applied to this type of problem; however, Harmony Search provides a convenient implementation at a reasonable computational cost. In this study, Harmony Search is demonstrated to be a valuable tool for water distribution network optimization as well as for pipe aging assessment.

RETRACTED: A novel power system scheduling based on hydrogen-based micro energy hub

Simultaneous operation of different energy generation and transmission infrastructures is a subject introduced as the concept of energy hub. This subject is highly regarded in microgrids. Integrating different energy sources under the concept of energy hub can improve the system performance as well as its reliability. The present paper is focused on the concept of hydrogen-based micro energy hub (SMEH) with regard to the integrated demand response (IDR) and hydrogen storage system (HSS). The SMEH studied in the present paper consists of different energy sources including the CHP, boiler, wind turbines, electrical energy storage system (ESS), gas storage system (GSS), thermal storage system (TSS), and hydrogen storage system (HSS). Demand response (DR) is introduced for controlling the consumers' heat demand and electrical pattern. Furthermore, the HSS not only can convert the power from renewable energy sources (RESs) into hydrogen (P2H) in the low power price periods, but also can, in turn, supply the hydrogen industry (H2P). The proposed model aims to minimize the energy cost of the whole hydrogen-based system considering the uncertainty of the power price. This optimization is performed by the harmony search (HS) optimization algorithm, which has been improved and developed by means of the local search algorithm. Eventually, the simulation and numerical results confirm the effectiveness of the proposed model.

The Analysis of Public Opinion in Colleges and Universities Oriented to Wireless Networks under the Application of Intelligent Data Mining

In recent years, the incidence of public opinion in colleges and universities has been high. Monitoring, forecasting, and responding to public opinion of students in colleges and universities have increasingly become the work that education management departments at all levels attach great importance to. For each university, how to understand the sensation of teachers and students in real time in the era of informationization entering the intelligent campus has become an urgent problem. How to collect college campus network information, analyze and manage this information, and find hot topics from it has a profound impact on the reform of colleges and universities. Hence, in this paper, we propose a public opinion analysis framework based on intelligent data mining technique. Its advantage lies in the fact that it can withdraw the needed and unknown knowledge and regularities from the massive network data and host log data. It is a new attempt to use data mining in achieving public opinion. At present, data mining algorithm applied to public opinion analysis mainly has four basic patterns: association, sequence, classification, and clustering. Data mining technology is advanced for: it can process large amount of data. It does not need the users’ subjective evaluation and is more likely to discover the ignored and hidden information. Here, initially, the dataset is collected, which is preprocessed and divided into a training set and test set. Feature extraction of the text is done using Linear Discriminant Analysis (LDA). After that, text cosine similarity calculation is performed to compute the similarity between text vectors obtained from the LDA. Convolutional neural network (CNN) is used for classification purpose. We proposed Krill Herd Harmony Search Optimization Algorithm (KHHSOA) for optimizing the CNN and classifying the text into positive and negative opinion. The proposed system is simulated using MATLAB simulation tool, and the performance is analyzed in terms of metrics like accuracy, precision, recall, F -measure, kappa coefficient, and error rate. The proposed method is proved to be better when compared with the existing techniques.

Optimized Blade Angle Controller to Enhance Wind Speed Variability Impacts

—This paper discusses the negative impact of wind farm penetration on the grid. This bad impact in this study is caused by wind farms due to wind speed variability. As wind turbines depend on wind speed to capture energy, this wind energy is assumed to be unpredicted due to the stochastic nature of wind. The paper focuses on that problem, wind speed variability, showing the most common solutions to overcome it. The paper presents pitch angle control as one of the most practical techniques experienced to overcome the wind speed variability challenge. Also, it illustrates the PID controller based on both Harmony Search Optimization and Whale Optimization Algorithms. First, the paper compares the effect of applying optimized PID control on blade angles with both HSA and WOA in one season of the year. Then; it studies the most effective algorithm among them and applies it to the rest of the year seasons.

Multi-Objective Design of Profit Volumes and Closeness Ratings Using MBHS Optimizing Based on the PrefixSpan Mining Approach (PSMA) for Product Layout in Supermarkets

Product layout significantly impacts consumer demand for purchases in supermarkets. Product shelf renovation is a crucial process that can increase supermarket efficiency. The development of a sequential pattern mining algorithm for investigating the correlation patterns of product layouts, solving the numerous problems of shelf design, and the development of an algorithm that considers in-store purchase and shelf profit data with the goal of improving supermarket efficiency, and consequently profitability, were the goals of this research. The authors of this research developed two types of algorithms to enhance efficiency and reach the goals. The first was a PrefixSpan algorithm, which was used to optimize sequential pattern mining, known as the PrefixSpan mining approach. The second was a new multi-objective design that considered the objective functions of profit volumes and closeness rating using the mutation-based harmony search (MBHS) optimization algorithm, which was used to evaluate the performance of the first algorithm based on the PrefixSpan algorithm. The experimental results demonstrated that the PrefixSpan algorithm can determine correlation rules more efficiently and accurately ascertain correlation rules better than any other algorithms used in the study. Additionally, the authors found that MBHS with a new multi-objective design can effectively find the product layout in supermarket solutions. Finally, the proposed product layout algorithm was found to lead to higher profit volumes and closeness ratings than traditional shelf layouts, as well as to be more efficient than other algorithms.

Adaptive hybrid harmony search optimization algorithm for point cloud fine registration

The fine registration of point clouds is a key step in point cloud pre-processing. However, the point cloud fine registration algorithm still has problems, such as low iteration accuracy and slower iteration speed. To further improve the efficiency of the point cloud fine registration algorithm, a point cloud fine registration method based on an adaptive hybrid harmony search algorithm was designed. First, the fine registration mathematical model of the point cloud was established according to the iterative closest point algorithm. Second, regarding the basic harmony search algorithm, the disadvantages were described through algorithm mechanism analysis, and four strategies—cube chaotic mapping, the shuffled frog leaping algorithm, adaptive parameters, and quadratic interpolation—were introduced to improve the algorithm’s computational performance. Finally, the adaptive hybrid harmony search algorithm was validated by benchmark functions and point cloud registration data. Another five traditional algorithms were used for comparative analysis with adaptive hybrid harmony search. The results of the simulation experiment show the effectiveness of the adaptive hybrid harmony search algorithm in point cloud fine registration.

Application of the novel harmony search optimization algorithm for DBSCAN clustering

At present, the DBSCAN clustering algorithm has been commonly used principally due to its ability in discovering clusters with arbitrary shapes. When the cluster number K is predefined, though the partitional clustering methods can perform efficiently, they cannot process the non-convex clustering and easily fall into local optimum. Thereby the concept of K-DBSCAN clustering is proposed in this paper. But the basic DBSCAN has a crucial defect, that is, difficult to predict the suitable clustering parameters. Here, the well-known harmony search (HS) optimization algorithm is considered to deal with this problem. By modifying the original HS, the novel harmony search (novel-HS) is put forward, which can improve the accuracy of results as well as enhance the robustness of optimization. In K-DBSCAN, the novel-HS is used to optimize the clustering parameters of DBSCAN to obtain better clustering effect with the number of K classifications. Experimental results show that the designed clustering method has superior performance to others and can be successfully considered as a new clustering scheme for further research.

An Improved Harmony Search Algorithm for Proactive Routing Protocol in VANET

Vehicular ad-hoc network (VANET) is the direct application of mobile ad-hoc network (MANET) in which the nodes represent vehicles moving in a city or highway scenario. The deployment of VANET relies on routing protocols to transmit the information between the nodes. Different routing protocols that have been designed for MANET were proposed to be applied in VANET. However, the real-time implementation is still facing challenges to fulfill the quality of service (QoS) of VANET. Therefore, this study mainly focuses on the well-known MANET proactive optimized link state routing (OLSR) protocol. The OLSR in VANET gives a moderate performance; this is due to its necessity of maintaining an updated routing table for all possible routes. The performance of OLSR is highly dependent on its parameter. Thus, finding optimal parameter configurations that best fit VANET features and improve its quality of services is essential before its deployment. The harmony search (HS) is an emerging metaheuristic optimization algorithm with features of simplicity and exploration efficiency. Therefore, this paper aims to propose an improved harmony search optimization (EHSO) algorithm that considers the configuration of the OLSR parameters by coupling two stages, a procedure for optimization carried out by the EHSO algorithm based on embedding two popular selection methods in its memory, namely, roulette wheel selection and tournament selection. The experimental analysis shows that the proposed approach has achieved the QoS requirement, compared to the existing algorithms.

Novel hybrid machine leaning model for predicting shear strength of reinforced concrete shear walls

Accurate prediction of the ultimate shear capacity of reinforced concrete shear walls (RCSWs) is essential for robust design of buildings under seismic and wind loads. However, the shear capacity of RCSWs depends on multiple complex design variables characterized by diverse geometric and materials properties. Thus, a powerful modeling framework is required. In this paper, a hybrid artificial intelligence model is proposed for predicting the ultimate shear capacity of RCSWs named artificial neural network (ANN) coupled with adaptive harmony search optimization (AHS) algorithm. Different statistical metrics were used to compare the performances of the ANN model coupled with AHS (ANN-AHS) to three existing empirical relations and two ANN models combined with harmony search (ANN-HS) and global-best harmony search (ANN-GHS). Results show that the proposed ANN-AHS achieved superior performance in modelling the shear strength of RCSWs compared to ANN-HS and ANN-GHS models. The soft-computing models have proven to be more accurate than existing empirical relations.