Search

Inspired by the chemical vapor deposition process in semiconductor manufacturing, this paper addresses the challenging NP-hard scheduling problem with flexible-variable maintenance in a single-machine environment. The problem also includes sequence-dependent setups and release times. The key aspect lies in maintenance operations, where the start times can vary within specified intervals and durations depending on working conditions. To tackle the problem at hand, a new metaheuristic algorithm, named Improved Self-Adaptive Harmony Search, is proposed. The algorithm introduces a dynamic calibration of parameters and three procedures to enhance the solution quality. The algorithm is compared with state-of-the-art algorithms using a full-factorial design of experiments, with total weighted tardiness as the objective function. The statistical analysis demonstrates the algorithm’s effectiveness, particularly for medium- and large-sized problems. This work not only studies a new variant of the scheduling problem but also provides a robust algorithmic solution, which can offer practical applications in manufacturing systems. [ABSTRACT FROM AUTHOR]

White tea is valuable for having a high antioxidant content, which is considered to possess numerous beneficial effects on health. This study investigated the application of microwave-assisted extraction (MAE) for the extraction of total phenolic compounds from white tea. The experimental setup included four independent variables: microwave power (ranging from 100 to 300 W), extraction time (ranging from 10 to 40 min), temperature (ranging from 35 to 50 °C), and the ratio of food to solvent (ranging from 0.25 to 0.5 g/10 mL). The responses that were evaluated were IC50 (ppm) and total phenolic content (mg/g). The experimental design consisted of thirty runs conducted within the MAE system. The group method of data handling (GMDH) models were used to predict important efficiency measures (IC50 and total phenol content) in the extraction process. The models were assessed based on their ability to capture the relationships between input conditions and efficiency outputs. Three GMDH variants were compared: baseline GMDH, GMDH optimized with a genetic algorithm (GMDH-GA), and GMDH optimized with a harmony search algorithm (GMDH-HS). While all models achieved high predictive ability on a test set, GMDH-HS emerged as the superior performer. It achieved near-perfect agreement with observations (d-index > 0.998), minimal errors (NRMSE < 0.02), and effectively captured data variance (NSE > 0.99) for both outputs. Correlation diagrams and Taylor diagrams confirmed the superior performance of GMDH-HS in terms of linearity, correlation, and error minimization. This study demonstrates the effectiveness of hybridizing GMDH with a harmony search algorithm for complex modeling tasks, paving the way for improved efficiency and yield optimization in extraction processes. [ABSTRACT FROM AUTHOR]

The performance of a deep neural network is affected by its configuration as well as its training process. Determining the configuration of a DNN and training its parameters are challenging tasks due to high-dimensional problems. Therefore, there is a need for methods that can optimize the configuration and parameters of a DNN. Most of the existing DNN optimization research concerns the optimization of DNN parameters, and there are only a few studies discussing the optimization of DNN configuration. In this paper, enhanced harmony search is proposed to optimize the configuration of a fully connected neural network. The proposed harmony search enhancement is conducted by introducing various types of harmony memory consideration rate and various types of harmony memory selection. Four types of harmony memory consideration rate are proposed in this research: constant rate, linear increase rate, linear decrease rate, and sigmoid rate. Two types of harmony memory selection are proposed in this research: rank-based selection and random selection. The combination of types of harmony memory consideration rate and types of selection generates eight harmony search scenarios. The performance of the proposed method is compared to random search and genetic algorithm using 12 datasets of classification problems. The experiment results show that the proposed harmony search outperforms random search in 8 out of 12 problems and approximately has the same performance in 4 problems. Harmony search also outperforms genetic algorithm in five problems, approximately has the same performance in six problems, and has worse performance in one problem. In addition, combining various types of harmony memory consideration rate and rank-based selection increases the performance of the ordinary harmony search. The combination of harmony memory consideration with linear increase rate and rank-based selection performs the best among all combinations. It is better than the ordinary harmony search in seven problems, approximately equal in three problems, and worse in two problems. The results show that the proposed method has some advantages in solving classification problems using a DNN. First, the configuration of the DNN is represented as an optimization problem so that it can be used to find a specific FCNN configuration that is suitable for a specific problem. Second, the approach is a global optimization approach as it tunes the DNN hyperparameter (configuration) as well as the DNN parameter (connection weight). Therefore, it is able to find the best combination of DNN configuration as well as its connection weight. However, there is a need to develop a strategy to balance the hyperparameter tuning and the parameter tuning. Inappropriate balance could lead to a high computational cost. Future research can be directed to balance the hyperparameter and parameter tuning during the solution search. [ABSTRACT FROM AUTHOR]

Abstract White tea is valuable for having a high antioxidant content, which is considered to possess numerous beneficial effects on health. This study investigated the application of microwave-assisted extraction (MAE) for the extraction of total phenolic compounds from white tea. The experimental setup included four independent variables: microwave power (ranging from 100 to 300 W), extraction time (ranging from 10 to 40 min), temperature (ranging from 35 to 50 °C), and the ratio of food to solvent (ranging from 0.25 to 0.5 g/10 mL). The responses that were evaluated were IC50 (ppm) and total phenolic content (mg/g). The experimental design consisted of thirty runs conducted within the MAE system. The group method of data handling (GMDH) models were used to predict important efficiency measures (IC50 and total phenol content) in the extraction process. The models were assessed based on their ability to capture the relationships between input conditions and efficiency outputs. Three GMDH variants were compared: baseline GMDH, GMDH optimized with a genetic algorithm (GMDH-GA), and GMDH optimized with a harmony search algorithm (GMDH-HS). While all models achieved high predictive ability on a test set, GMDH-HS emerged as the superior performer. It achieved near-perfect agreement with observations (d-index > 0.998), minimal errors (NRMSE 0.99) for both outputs. Correlation diagrams and Taylor diagrams confirmed the superior performance of GMDH-HS in terms of linearity, correlation, and error minimization. This study demonstrates the effectiveness of hybridizing GMDH with a harmony search algorithm for complex modeling tasks, paving the way for improved efficiency and yield optimization in extraction processes.

This research focused on intervening in the optimization algorithm used by the Computational Reverse-Engineering Analysis for Scattering Experiments (CREASE) tool to analyze small-angle scattering (SAS) profiles using the Rigid-Body model. CREASE uses the genetic algorithm (GA) with dynamic adaptation as its optimization algorithm. The aim is to evaluate the performance of CREASE by replacing the GA with a Harmony Search (HS)-based metaheuristic, specifically the Nobel Global Harmony Search (NGHS), in the analysis of SAS profiles of low-concentration solutions vesicles-assembled amphiphilic macromolecules. Results showed that NGHS achieved similar accuracy to GA but with higher efficiency, achieving similar quality solutions with only one-sixth, and in some cases one-tenth, the number of fitness function evaluations used by GA. Besides, CREASE-NGHS achieved SAS profile analysis convergence with less than half the number of fitness function evaluations, saving computational resources and facilitating a more complete analysis. In addition, NGHS addressed some shortcomings of the GA optimization process and facilitated its use and adaptation to distinct types of samples for users with little experience in optimization.

The continuous supply of drinking water for human life is essential to ensure the sustainability of cities, society, and the environment. At a time when water scarcity is worsening due to climate change, the construction of an optimized water supply infrastructure is necessary. In this study, an improved version of the Harmony Search Algorithm (HSA), named the Maisonette-type Harmony Search Algorithm (MTHSA), was developed. Unlike the HSA, the MTHSA has a two-floor structure, which increases the optimizing efficiency by employing multiple explorations on the first floor and additional exploitations of excellent solutions. Parallel explorations enhance the ability in terms of exploration (global search), which is the tendency to uniformly explore the entire search space. Additional exploitations among excellent solutions also enhance the ability of local searches (effective exploitation), which is the intensive exploration of solutions that seem to have high possibilities. Following the development of the improved algorithm, it was applied to water distribution networks in order to verify its efficiency, and the numerical results were analyzed. Through the considered applications, the improved algorithm is shown to be highly efficient when applied to large-scale optimization problems with large numbers of decision variables, as shown in comparison with the considered optimizers. [ABSTRACT FROM AUTHOR]

The advancement of new tools in the field of control systems is a contemporary development. This work introduces the utilization of Type-3 fuzzy logic, a relatively recent concept that has been applied across various disciplines. In our case, a Type-3 fuzzy system is designed to enhance the optimization of parameters within the harmony search algorithm, specifically tailored for a control problem. Through a series of experiments, the efficacy of this novel Type-3 fuzzy logic tool is put to the test. Previous studies have primarily explored Type-1 and Type-2 fuzzy logic. To assess the performance of this new Type-3 fuzzy logic tool, a comparative analysis of results is conducted using statistical testing. The introduction of Type-3 fuzzy logic in the control domain represents a novel and innovative approach. This approach extends beyond the conventional Type-1 and Type-2 fuzzy logic, showcasing the dynamic evolution in the field. Results obtained through experimentation are analyzed, and statistical tests are employed to determine whether the Type-3 fuzzy logic tool yields superior outcomes compared to its predecessors. By doing so, this study contributes to the growing body of research that explores the potential benefits of Type-3 fuzzy logic and its application in control systems, offering new perspectives and opportunities for further advancements in the field. We have to mention that the utilization of Type-3 fuzzy logic in enhancing metaheuristics is a relatively new trend, and in this work, this research has extended this to the realm of harmony search. In addition, the application of the optimal design of the ball-and-beam fuzzy controllers has not been previously carried out with the Type-3 fuzzy harmony search algorithm, which is the novelty of this study. [ABSTRACT FROM AUTHOR]

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%. [ABSTRACT FROM AUTHOR]

Cloud computing environments, characterized by dynamic scaling, distributed architectures, and complex workloads, 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. [ABSTRACT FROM AUTHOR]

Nahuatl, a low-resource language, does not have an online translator application. Instead, resources are limited to dictionaries, web pages, or digital books. Given this condition, it is vital to provide as much support to the language as possible. This research aims to enhance the BLEU score in machine translation by applying the harmony search heuristic method to state-of-the-art transformers models. This is conducted by finding the optimal hyperparameter settings for the models. Models are trained and tested using a fresh moderate-size parallel corpus of 1.5k phrases. By utilizing harmony search, the study shows an improvement in the BLEU score, enhancing it by 2.569%. In order to accomplish this, various factors related to the hyperparameters need to be considered. The application of harmony search with transformers can be extended to various parallel corpora or models, taking these considerations into account.

World Health Organization (WHO) declared Coronavirus Disease 2019 (COVID-19), caused by the virus named Severe Acute Respiratory Syndrome Corona Virus-2 (SARS-CoV-2), as a global pandemic on March 11, 2020. Several researchers have used various statistical models and techniques to study and forecast the trend of spread and magnitude of the impact of SARS-CoV-2. ARIMA is one such model that has been widely used for this purpose, but in most cases, the model was not optimized suitably. In this paper, a music-inspired metaheuristic optimization algorithm, named Harmony Search (HS), has been integrated with ARIMA in order to improve the forecasting of the COVID-19 data set. The accuracy of forecasting has significantly increased after optimizing the model using the proposed algorithm. Furthermore, a novel application of HS has also been discussed in this paper.

Due to its high spectral efficiency and various other advantages, filter bank multicarrier/offset quadrate amplitude modulation (FBMC/OQAM) has long been considered as a candidate waveform for the fifth generation (5G) and beyond telecommunication technologies. On the other hand, it is possible to both increase the data rate and alleviate the channel fading effects by using the multiple-input multiple-output (MIMO) antenna structure in the FBMC/OQAM transceiver. However, since the symbol detection is an indispensable task to be fulfilled in wireless communication, it is crucial to employ an efficient symbol detector at the MIMO-FBMC/OQAM receiver. Maximum likelihood (ML) detector, which always finds the optimal symbols by trying all of the possible symbol combinations likely to be transmitted, is known for its extremely high computational complexity making it impractical to be used in any system. On the other hand, it is possible to both considerably reduce the ML complexity and achieve the near-ML performance by optimizing the symbol vectors instead of implementing an exhaustive search. Since searching for the optimal symbol combination in discrete space is a combinatorial optimization problem, we developed a novel discrete harmony search (disHS) algorithm to perform this operation. According to the simulation results, the newly developed disHS algorithm not only achieves near-ML performance with lower computational complexity, but also clearly leaves behind the other symbol detectors considered in this paper. [ABSTRACT FROM AUTHOR]

Combining the harmony search algorithm (HS) with the local search algorithm (LS) can prevent the HS from falling into a local optimum. However, how LS affects the performance of HS has not yet been studied systematically. Therefore, in this paper, it is first proposed to combine four frequently used LS with HS to obtain several search algorithms (HSLSs). Then, by taking the flexible job-shop scheduling problem (FJSP) as an example and considering decoding times, study how the parameters of HSLSs affect their performance, where the performance is evaluated by the difference rate based on the decoding times. The simulation results mainly show that (I) as the harmony memory size (HMS) gradually increases, the performance of HSLSs first increases rapidly and then tends to remain unchanged, and HMS is not the larger the better; (II) as harmony memory considering rate increases, the performance continues to improve, while the performance of pitch adjusting rate on HSLSs goes to the opposite; Finally, more benchmark instances are also used to verify the effectiveness of the proposed algorithms. The results of this paper have a certain guiding significance on how to choose LS and other parameters to improve HS for solving FJSP. [ABSTRACT FROM AUTHOR]

As a classical clustering algorithm, DBSCAN algorithm is widely used in various fields. However, due to the poor adaptability of its parameters, the application effect depends entirely on the setting of parameters. Based on this, an adaptive DBSCAN algorithm based on improved harmony search is proposed to improve the adaptability of DBSCAN algorithm. The algorithm first uses the K-means nearest neighbor algorithm to optimize the initial population, thereby improving the quality of the initial population and providing a high-quality solution for subsequent evolutionary calculations. Secondly, an update operator based on double difference is designed to improve the search ability of the algorithm. Thirdly, two update strategy structures are used to avoid premature convergence of the algorithm, improve the optimization ability of the harmony search algorithm, and comprehensively improve the adaptability of the DBSCAN algorithm. Finally, a variety of datasets are used and comparative experiments are designed to verify the proposed algorithm. Experimental results show that the proposed algorithm has better recognition ability and adaptability. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is the most prevalent brain disorder which affects millions of people worldwide. Early detection is crucial for possible treatment. In this regard, machine learning (ML) approaches are widely utilized for AD detection. In this paper, we propose an ML-based method that drastically reduces the dimensionality of features while maintaining the relevant features and boosting the overall performance. To remove irrelevant features, first statistical feature extraction method is applied, and then further reduction among remaining features is applied by utilizing the harmony search method (HSM). The selected features are the most informative features that are fed to the different classifiers. To test the effectiveness of the proposed method, we deployed three classification techniques including support vector machine (SVM), k-nearest neighbor (k-NN), and decision tree (DT). The experimental results show that the proposed method has a higher performance while decreasing the dimensionality of feature space. To guarantee that the performance of the proposed method is accurate, we applied an ensemble of three classifiers (SVM, KNN, and DT) for classification. The results of the proposed method verify that this method can be successfully deployed for AD detection, due to its high performance and low dimensional features, and can help improve the accuracy and efficiency of Alzheimer's disease diagnosis. The proposed method demonstrated a significant improvement, achieving high performance in AD/HC classification, with accuracy, sensitivity, specificity, F1-score, MCC, and Cohen's Kappa rates reaching 95.5%, 97%, 94%, 95.56%, 0.9104, and 0.9109, respectively. AD/HC classification displayed the highest performance. Additionally, in the more challenging pMCI/sMCI classification, the method achieved an accuracy of 78.50%, sensitivity of 84.00%, specificity of 73.00%, F1-score of 79.62%, MCC of 0.57, and Cohen's Kappa of 0.59. [ABSTRACT FROM AUTHOR]

This study explores the efficacy of metaheuristic-based feature selection in improving machine learning performance for diagnosing sarcopenia. Extraction and utilization of features significantly impacting diagnosis efficacy emerge as a critical facet when applying machine learning for sarcopenia diagnosis. Using data from the 8th Korean Longitudinal Study on Aging (KLoSA), this study examines harmony search (HS) and the genetic algorithm (GA) for feature selection. Evaluation of the resulting feature set involves a decision tree, a random forest, a support vector machine, and naïve bayes algorithms. As a result, the HS-derived feature set trained with a support vector machine yielded an accuracy of 0.785 and a weighted F1 score of 0.782, which outperformed traditional methods. These findings underscore the competitive edge of metaheuristic-based selection, demonstrating its potential in advancing sarcopenia diagnosis. This study advocates for further exploration of metaheuristic-based feature selection's pivotal role in future sarcopenia research. [ABSTRACT FROM AUTHOR]

PID controllers can regulate and stabilize processes in response to changes and disturbances. This paper provides a comprehensive review of PID controller tuning methods for industrial applications, emphasizing intelligent and nature-inspired algorithms. Techniques such as Fuzzy Logic (FL), Artificial Neural Networks (ANN), and Adaptive Neuro Fuzzy Inference System (ANFIS) are explored. Additionally, nature-inspired algorithms, including evolutionary algorithms like Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), Ant Colony Optimization (ACO), Simulated Annealing (SA), Artificial Bee Colony (ABC), Firefly Algorithm (FA), Cuckoo Search (CS), Harmony Search (HS), and Grey Wolf Optimization (GWO), are examined. While conventional PID tuning methods are valuable, the evolving landscape of control engineering has led to the exploration of intelligent and nature-inspired algorithms to further enhance PID controller performance in specific applications. The study conducts a thorough analysis of these tuning methods, evaluating their effectiveness in industrial applications through a comprehensive literature review. The primary aim is to offer empirical evidence on the efficacy of various algorithms in PID tuning. This work presents a comparative analysis of algorithmic performance and their real-world applications, contributing to a comprehensive understanding of the discussed tuning methods. Findings aim to uncover the strengths and weaknesses of diverse PID tuning methods in industrial contexts, guiding practitioners and researchers. This paper is a sincere effort to address the lack of specific quantitative comparisons in existing literature, bridging the gap in empirical evidence and serving as a valuable reference for optimizing intelligent and nature-inspired algorithms-based PID controllers in various industrial applications. [ABSTRACT FROM AUTHOR]

In the pursuit of software quality, testing plays a pivotal role in identifying and rectifying errors and defects before software is delivered to users. However, the increasing complexity of modern software, driven by diverse user needs, has resulted in numerous input functions and options. While exhaustive testing remains ideal, practical limitations in terms of time and cost impede exhaustive efforts. To address these challenges, researchers have turned to metaheuristic algorithms to formulate effective t-way testing strategies, wherein ‘t’ denotes parameter interaction strength. These strategies encompass a range of exploitation and exploration techniques for generating test data. Despite these advancements, challenges persist in test case generation, such as enhancing software quality through parameter seeding and managing combinatorial explosion by understanding parameter constraints among system elements. Though existing metaheuristic-based t-way strategies offer valuable insights, none reign supreme over their counterparts. To bridge this gap, this article introduces a pioneering approach called SCHOP, integrating seeding and constraint supports within a harmony search algorithm by adopting a one-parameter-at-a-time approach. Benchmarking results illustrate the competitive performance of SCHOP across well-known benchmarking configurations against other strategies. SCHOP’s benchmarking results showed success in 61.07% of cases, with 80 out of 131 entries performing well. However, there were 51 instances of suboptimal test suite sizes (38.93%). Further analysis revealed statistical significance in 12 out of 61 cases, with 48 out of 61 cases showing no significant difference, all at a 95% confidence level ( $\alpha =0.05$ ). Finally, SCHOP concurrently integrates seeding and constraint support within the harmony search algorithm to boost software quality while reducing the test suite’s size.

Multidirectional torsional hysteretic damper is a new type of damper that can be used to isolate and dissipate seismic effects on a structure. It can be designed to have a controllable post-elastic stiffness and exhibit high levels of damping as well as stable cyclic response. In this article, while offering a simplified numerical relationship for force-displacement response of the damper, the structure that is fitted with this innovative type of damper is optimized using the harmony search optimization procedure with discrete design variables. Numerical experiments show that the harmony search methodology can determine the damper parameters with high computational efficiency and outperform genetic algorithm and simulated annealing procedure in this regard.

In this paper, we present a hybrid heuristic method based on the harmonic search algorithm to solve the vehicle routing problem with time windows. The importance of efficient path planning in logistics and supply chain management is emphasized herein, particularly under complex constraints like time window and vehicle capacity limits. The hybrid heuristic harmony search algorithm combines the global search capability of the harmony search algorithm with the accuracy of the local search heuristic to efficiently explore and harness the solution space. Through rigorous testing on the Solomon dataset, the hybrid heuristic harmony search algorithm performed remarkably in the generation of competitive solutions while maintaining the computational efficiency of the solutions. The results showed that the algorithm achieved competitive solutions even under strict time window constraints. The convergence of the algorithm was examined, and its strong performance in handling complex instances was revealed. This study enhances the operational efficiency of an organization and provides perspectives and solutions for optimization strategies in the domains of logistics and supply chain management.

IntroductionClimate change and population increase have caused an increase in consumption in the existing water distribution networks, as a result, an increase in the discharge in the pipes and a decrease in the pressure in the nodes. The decrease in pressure makes some consumers unable to receive the required water. To solve this problem, long-term and short-term solutions have been proposed. Replacing pipes with pipes with a larger diameter is one of the solutions. But it requires considerable time and money. Intermittent supply of water can be used in existing networks to solve the problem.TazeAbad network has a large pressure drop during peak hours. The reason for this is the low diameter of the main pipes, which cause a large drop in the pressure as the consumption increases and the discharge increases. In this study, in order to improve justice and increase water supply in this network, optimal intermittent supply of water was done for 6 areas of this network.MethodologyThe Intermittent supply of water is usually done based on pressure-based hydraulic analysis. In case, the consumers will also store the needs of other hours. In other words, the consumption pattern in continuous supply cannot be used for Intermittent supply. In this research, the Emitter method is used for the hydraulic analysis of the network. In this hydraulic analysis, the amount of water delivered to the node is a function of pressure.Intermittent supply for 6 areas of the water distribution network of TazeAbad city was optimized in 12, 8 and 6 hours with the aim of supplying the required water and high two-objective by using single-objective and two-objective Harmony Search algorithm. In the hydraulic model, the pressure-based hydraulic analysis method and the Emitter method were used to determine the discharge of the nodes and the results were compared. In different scenarios, the possibility of supplying water in 4, 3 and 2 time intervals was investigated.Results and discussionIn all scenarios, the results of pressure-based hydraulic analysis method are located in all areas of Tazehabad city during peak hours. In this method, despite the high distribution uniformity coefficient, the amount of water delivered to consumers is less than their needs in the hour of supply. At present, in TazeAbad distribution network, consumers receive more water at the existing pressures than the pressure-based method.In the Emitter method, in all scenarios, the distribution uniformity is lower than the pressure-based method. But in this method, the supply percentage is much higher. So that more than 95% of customers' demands have been met in 12-hour supply.By reducing the water supply time, the percentage of supply has decreased in both methods. In the single-objective algorithm, the ratio of supplied water to the required water is more than the double-objective algorithm. But the uniformity of the distribution has increased in the double-objective algorithm. In all scenarios, water delivery in the pressure-based method was in one time interval for all areas, but in the Emitter method, water was usually delivered in 2 time intervals.In TazeAbad network, if water is supplied in 2 shifts, the maximum discharge in the network is more than the maximum discharge of the existing conditions of the network. But if water is supplied in 3 and 4 time intervals, the maximum discharge will be closer to the maximum discharge of the network. The difference between the maximum and minimum network discharges in the two-objective algorithm was lower than the single-objective algorithm. The supply of water in 4 time intervals has had the least changes in discharge and consumption coefficients.Conclusions According to the scenarios reviewed in TazeAbad network, water supply in 8 hours was chosen as the best option. In this case, the ratio of supplied water to required water will be around 70% and the uniformity of distribution will be around 87%. The maximum discharge in the water distribution network is almost equal to the maximum discharge in the current conditions. Also, the range of discharge changes and consumption coefficients is lower than the current conditions. In other words, pressure changes in the network will be less than the current conditions.

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.

Flexible process planning involves selecting and sequencing the requisite operations, and assigning the right machine, tool and access direction to each selected operation for minimising the production cost or the completion time. It is one of the challenging combinatorial optimisation problems due to sequencing flexibility, processing flexibility and operation flexibility. A sequence learning harmony search algorithm is accordingly proposed. Distinctively, the well-designed algorithm searches for the optimal process plan by intelligently finding the proper immediate successor for each selected operation in turn rather than resorting to the common shifting and swapping operators in sequencing. The innovative algorithm does not also require extra efforts to plot the operational precedence graph or the AND/OR-network graph. The experimental results indicate that the proposed algorithm significantly outperforms other heuristics in terms of the quality of solution found and the convergence rate of the algorithm. For the large-scale complicated instances, the proposed algorithm establishes a challenging flag. [ABSTRACT FROM AUTHOR]

Multidirectional torsional hysteretic damper is a new type of damper that can be used to isolate and dissipate seismic effects on a structure. It can be designed to have a controllable post-elastic stiffness and exhibit high levels of damping as well as stable cyclic response. In this article, while offering a simplified numerical relationship for force-displacement response of the damper, the structure that is fitted with this innovative type of damper is optimized using the harmony search optimization procedure with discrete design variables. Numerical experiments show that the harmony search methodology can determine the damper parameters with high computational efficiency and outperform genetic algorithm and simulated annealing procedure in this regard. [ABSTRACT FROM AUTHOR]

The attainment of energy sustainability in the building sector can be realised by implementing a green building programme, which has grown significantly over the last thirty years. Green building is considered a technical and management strategy within the building and construction industries. Many different prediction methods, both complex and simple, have been put out in recent years and used to solve a wide variety of issues. Several case studies have highlighted factors that impede energy and resource usage in green buildings. The utilisation, trends, and consequences of wall and thermal insulation materials are examined. The main scope of this investigation is to predict buildings' heat loss by applying artificial neural networks according to the heat transfer coefficients of walls and coating materials, as well as indoor, outdoor, and external surface temperatures. The data has been normalised and presented to two selected neural networks (Harmony search (HS) and particle swarm optimisation are used and contrasted (PSO)). For evaluating the accuracy of models, two statistical indexes are used (R² and RMSE). Model performance of PSO-MLP is shown by R² amounts of 0.97055 and 0.87381, respectively, and RMSE amounts of 0.02534 and 0.09685. Similarly, HS-MLP model accuracy is also indicated by R² amounts of 0.93839 and 0.84176 and RMSE amounts of 0.03635 and 0.10753. The analysis in this paper shows that PSO-MLP predicts heat loss with higher accuracy and improved performance. [ABSTRACT FROM AUTHOR]

In today's banking and financial system, using a credit card has become indispensable. The credit card industry has existed due to a shift in consumer preferences and a rise in national economic growth. The number of issuing banks, card issuers, and transaction volumes has increased significantly. Nevertheless, owing to the growth in the number of transactions made with credit cards, both the total amount due and the rate of defaults on credit card loans have become issues that cannot be neglected. This issue must be resolved to ensure the continued and prosperous growth of the banking industry in the years to come. Currently, a few optimization algorithms—Whale optimization algorithm (WOA), Harmony Search (HS), Multi-verse optimization (MVO), and Vortex Search (VS)—have been used to achieve this purpose. However, because credit card default data is volatile and unequal, it is challenging for typical optimization algorithms to offer steady approaches with optimal performance. Studies have indicated that optimizing algorithms with suitable properties can significantly improve performance. To improve performance, some tuning was applied to the ANN. This study will assess twenty-three parameters, and the efficacy of all four approaches will be compared using ROC and AUC evaluations. The suggested model's performance is contrasted with a scenario where the classifiers were trained using original data. In contrast, the AUC values for VS-MLP were 0.7407 and 0.7271, while those for HS-MLP were 0.7074 and 0.6997. In the training and testing phases, AUC values of 0.7469 and 0.7329 from MVO-MLP and 0.72 and 0.7185 from WOA-MLP, respectively. The results show that the training accuracy of HS, VSA, MVO, and WOA are similar; MVO has the highest training accuracy. The credit card industry can benefit significantly from this methodology, which may help resolve default probabilities.

The advancement of new tools in the field of control systems is a contemporary development. This work introduces the utilization of Type-3 fuzzy logic, a relatively recent concept that has been applied across various disciplines. In our case, a Type-3 fuzzy system is designed to enhance the optimization of parameters within the harmony search algorithm, specifically tailored for a control problem. Through a series of experiments, the efficacy of this novel Type-3 fuzzy logic tool is put to the test. Previous studies have primarily explored Type-1 and Type-2 fuzzy logic. To assess the performance of this new Type-3 fuzzy logic tool, a comparative analysis of results is conducted using statistical testing. The introduction of Type-3 fuzzy logic in the control domain represents a novel and innovative approach. This approach extends beyond the conventional Type-1 and Type-2 fuzzy logic, showcasing the dynamic evolution in the field. Results obtained through experimentation are analyzed, and statistical tests are employed to determine whether the Type-3 fuzzy logic tool yields superior outcomes compared to its predecessors. By doing so, this study contributes to the growing body of research that explores the potential benefits of Type-3 fuzzy logic and its application in control systems, offering new perspectives and opportunities for further advancements in the field. We have to mention that the utilization of Type-3 fuzzy logic in enhancing metaheuristics is a relatively new trend, and in this work, this research has extended this to the realm of harmony search. In addition, the application of the optimal design of the ball-and-beam fuzzy controllers has not been previously carried out with the Type-3 fuzzy harmony search algorithm, which is the novelty of this study.

The development of sustainable building structures has been a crucial measure for carbon reduction. With consideration of both carbon emissions and costs, the sustainable design of building structures is complicated and time consuming for designers. To simplify the process of sustainable design optimization, the present study proposes a multi-objective harmony search algorithm for the design of concrete frame buildings considering low-carbon and cost-efficient requirements. This hybrid method combines structural analysis with local component optimization. Process-based life cycle assessment was used to identify carbon-intensive and high-cost structural members, and these members were optimized using discrete design variables and complicated constraints. Furthermore, a case study was conducted on a four-story frame building, in which the embodied emissions and costs were estimated as 1270.91 tCO2e and 0.67 million USD in the initial design scheme. Beams were identified as the optimization target for this building, and a set of Pareto-optimal solutions was obtained, indicating potential emission and cost reductions of 17.9% and 12.3%, respectively, compared with the initial designs. This study provides an easy and practical approach for the sustainable design of concrete frame buildings and enables designers to better understand building structural optimization from an economic and low-carbon perspective.

Face Reconstruction is the only biometric that allows you to perform passive identification in one to many environments. By changing the geometry and texture of the facial region, there can be an increased intraclass variability between images of an individual before and after surgery. Even so, the nonlinear changes caused by plastic surgery have a hard time being modelled by facial reconstruction systems. An algorithm for plastic surgery face reconstruction is presented here, that uses Harmony Search to confront the challenges involved. A non-disjoint algorithm generates the face granules, each representing different information with varying resolutions and sizes. A Scale-Invariant Feature Transform (SIFT) and Co-occurrence of Adjacent Local Binary Patterns were applied to extract discriminating information from granules of face images. As the concluding step, we used Harmony Search Method (HSM) to combine the various responses to attain the optimal solution. Finally Improved Radial Basis Function Network (IRBFN) algorithm is used for feature matching. The feature matching predict, where the face is recognized or not. The modified method, proposed in this work, has the better performance in classifying more images with higher accuracy. An obtained sensitivity of 100 % and specificity of 97.5 % was achieved by the proposed classifier. The results proved that it is possible to use the developed algorithms understand different pre and post surgical image conditions, evaluate their strength and future development trends, and provide all-facial features basis for detection of pre and post surgical images.

Diabetes is a dreaded disease that can be identified by elevated blood glucose levels in the blood, and undiagnosed diabetes can cause a host of related complications, such as retinopathy and nephropathy. In terms of type, the main categories are type 1 diabetes (T1DM), type 2 diabetes (T2DM) and gestational diabetes mellitus (GDM). Machine learning models and metaheuristic optimization algorithms can play an important role in the early detection, diagnosis and treatment of this disease. To this end, we propose AHDHS-Stacking, an ensemble learning framework for diabetes mellitus classification and diagnosis that is based on the harmony search (HS) algorithm and stacking and includes two stages of feature selection and optimization of base-learner combinations. To improve the model’s overall performance, the average performance of all base learners is used as the feature selection target, and an adaptive hyperparameter strategy is used to accelerate the iterative process. HS is then used to optimize to find the best combination of base learners, which improves model performance while reducing complexity. Following that, we conducted experiments on the Pima Indians Diabetes (PID) dataset and the Chinese and Western Medicine Diabetes (CWMD) dataset, achieving accuracy of 93.09%, precision of 93.22%, recall of 91.60% , F-measure of 92.25%, and MCC of 84.79% on PID dataset, which is better than all benchmark models and validated the model’s validity. CWMD dataset experimental results showed that AHDHS-Stacking screened for key features such as age, gender, urinary glucose, fasting glucose, BMI and cholesterol, and can be used as a practical and accurate method for early diabetes prediction.

The attainment of energy sustainability in the building sector can be realised by implementing a green building programme, which has grown significantly over the last thirty years. Green building is considered a technical and management strategy within the building and construction industries. Many different prediction methods, both complex and simple, have been put out in recent years and used to solve a wide variety of issues. Several case studies have highlighted factors that impede energy and resource usage in green buildings. The utilisation, trends, and consequences of wall and thermal insulation materials are examined. The main scope of this investigation is to predict buildings’ heat loss by applying artificial neural networks according to the heat transfer coefficients of walls and coating materials, as well as indoor, outdoor, and external surface temperatures. The data has been normalised and presented to two selected neural networks (Harmony search (HS) and particle swarm optimisation are used and contrasted (PSO)). For evaluating the accuracy of models, two statistical indexes are used (R2 and RMSE). Model performance of PSO-MLP is shown by R2 amounts of 0.97055 and 0.87381, respectively, and RMSE amounts of 0.02534 and 0.09685. Similarly, HS-MLP model accuracy is also indicated by R2 amounts of 0.93839 and 0.84176 and RMSE amounts of 0.03635 and 0.10753. The analysis in this paper shows that PSO-MLP predicts heat loss with higher accuracy and improved performance.

Precipitation is a key constituent of the water cycle and its accurate measurement is essential for a wide range of hydroclimatic studies. Although advances in technology have provided new sources of precipitation data (e.g., satellite data), gauge measurements are still considered as the most reliable source of information. Accuracy of ground measurements highly depends on the network density and positions of the rain gauges and thus, many studies have attempted to re-design the rain gauge networks around the globe. This is while, a limited number of researches have investigated the potential of utilizing global gridded precipitation products in network optimization context. In this study, a two-step framework was proposed to remove redundant rain gauge stations of the Central Plateau watershed of Iran. In the first step, number of excess rain gauges was detected based on transinformation–distance (T–D) analysis. In the second step, exact location of the extra rain gauges was determined through high degree of freedom and low degree of freedom (LDF) scenarios by a combination of harmony search optimization algorithm and a geostatistical model. According to the results, 58 out of 580 rain gauges were detected to be removed. Results of the proposed scenarios differed in detecting 13 out of 58 gauges. While LDF scenario required less computational time, network re-designed by this scenario was about 32% less accurate than the other scenario. Performance of the both optimized networks was equally similar to the initial network in terms of developing precipitation rasters. [ABSTRACT FROM AUTHOR]

The time of concentration (Tc), which represents the steady-state hydraulic conditions of a watershed, is an essential parameter for understanding precipitation and runoff dynamics in a hydrographic basin. Precise Tc estimates are required in many hydrological models and for the correct scaling of hydraulic structures. In Brazil, common empirical or semi-empirical methods for estimating Tc have not been developed and verified using local data, studies are needed in the area of hydrological modeling, which provide knowledge of the flow dynamics in tropical regions. In this study, we develop a model for estimating Tc for non-urban tropical basins, which are characterized by soils with high to very high runoff potential. The model employs the following input variables: basin area, main thalweg length, and average rainfall intensity, and an unprecedented methodology that applies: the Harmony Search algorithm to optimize parameters, data from 15 basins with areas less than 2,000 km2, 1,002 hydrological events, 46 equations for estimating Tc, and six different statistical techniques. The model is effective for estimating Tc in non-urban basins in a tropical climate region that mostly present soils with a high to very high runoff potential. [ABSTRACT FROM AUTHOR]

The hotel industry has a negative effect on the environment, since its activities generate large consumption of resources and greenhouse gas emissions, which is why the World Tourism Organization makes a strong call to all its sectors to move towards a sustainable development model. In the hotel industry, indicators have been used to improve sustainable performance, however, these sustainable indicators are scattered in the literature, which makes it difficult to select them. Therefore, Harmony Search (HS) is proposed to determine which are the best indicators to evaluate the environmental impact generated by the hotel industry. As a result of the HS search, a list of 20 indicators was obtained, which best met the selection criteria: frequency of use, level of application and year of publication. [ABSTRACT FROM AUTHOR]

Unsupervised clustering methods are used as data-driven models for mapping of mineral prospectivity (MPM). One of the challenging problems for modeling high potential mineral prospects using these algorithms is to select proper cluster centroids in order to locate the same geospatial objects into reasonable clusters. To overcome this kind of problems arise from traditional clustering algorithms (e.g. K-means), the combination of harmony search (HS) and artificial bee colony (ABC) metaheuristic optimization algorithms with K-means have been firstly developed in this study which can be used for selection of optimum cluster centroids for highlighting favorable targets in prospecting stage of mineral explorations. This stage is critically important for exploration of undiscovered mineral deposits, because the time and cost are two substantial factors in regional-scale exploration programs. This study stated that the applying hybrid data-driven clustering models (e.g., HS-KM and ABC-KM) are operational tools for identifying high-favorable areas linked to porphyry Cu systems in prospecting stage. Moreover, the results demonstrated that the properly selection of geospatial exploration features has strong impact on the modeling of mineral prospectivity in Kerman porphyry belt, Iran and cause significant decrease in systemic uncertainty linked to MPM. [ABSTRACT FROM AUTHOR]

Investigating stock price trends and determining future stock prices have become focal points for researchers within the finance sector. However, predicting stock price trends is a complex task due to the multitude of influencing factors. Consequently, there has been a growing interest in developing more precise and heuristic models and methods for stock price prediction in recent years. This study aims to assess the effectiveness of technical indicators for stock price prediction, including closing price, lowest price, highest price, and the exponential moving average method. To thoroughly analyze the relationship between these technical indicators and stock prices over predefined time intervals, we employ an Artificial Neural Network (ANN). This ANN is optimized using a combination of Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Harmony Search (HS) algorithms as meta-heuristic techniques for enhancing stock price prediction. The GA is employed for selecting the most suitable optimization indicators. In addition to indicator selection, PSO and HS are utilized to fine-tune the Neural Network (NN), minimizing network errors and optimizing weights and the number of hidden layers simultaneously. We employ eight estimation criteria for error assessment to evaluate the proposed model's performance and select the best model based on error criteria. An innovative aspect of this research involves testing market efficiency and identifying the most significant companies in Iran as the statistical population. The experimental results clearly indicate that a hybrid ANN-HS algorithm outperforms other algorithms regarding stock price prediction accuracy. Finally, we conduct run tests, a non-parametric test, to evaluate the Efficient Market Hypothesis (EMH) in its weak form. [ABSTRACT FROM AUTHOR]