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Summary In this paper, two improved differential harmony search algorithms called self‐adaptive differential harmony search (SADHS) algorithm and chaotic SADHS (CSADHS) algorithm are proposed to solve economic load dispatch problems with valve‐point effect, ramp‐rate limits, prohibited operating zones, and multiple fuel options. The effectiveness and suitability of the proposed algorithms are demonstrated on test systems consisting of 13, 15, and 40 generating units with valve‐point effect and 10 generating units with multiple fuel options and valve‐point effect. The simulation results obtained by the proposed algorithms are compared with the results obtained using DHS and with the results of other methods reported in the literature. The solution obtained by the proposed CSADHS algorithm is found to be better than the other methods in terms of solution quality. In terms of convergence speed and computation time, the proposed CSADHS algorithm is better than DHS and the other proposed SADHS algorithms. Copyright © 2014 John Wiley & Sons, Ltd.

In this era of the fourth industrial revolution, the integration of big data and 3D printing technology with the construction industry has maximized productivity. Currently, there is an active effort to research the optimal cladding structure through 3D printing technology to reduce production costs. This paper proposes a new type of 3D print curtain wall, using a high-strength ABS-M30 polymer panel, which is stronger than the standard acrylonitrile butadiene styrene (ABS) polymer, as an internally reinforced structure. This structure is fabricated via fused deposition modeling, a 3D printing method, to reduce the weight of the general cement panel. In addition, the shape of the polymer board was designed; three shapes were considered—O, W, and X types—which aided in further reducing the weight of the cladding. After comparing the center deformation of the structure through a lateral load test and finite element method analysis, the optimal model was selected. The measured data of the two methods at a design wind speed of 100% showed a difference of approximately 10%; however, at 150% of the design wind speed, the difference between the two sets of data increased to 27%.

To overcome the shortcomings of the harmony search algorithm, such as its slow convergence rate and poor global search ability, a reward population-based differential genetic harmony search algorithm is proposed. In this algorithm, a population is divided into four ordinary sub-populations and one reward sub-population, for each of which the evolution strategy of the differential genetic harmony search is used. After the evolution, the population with the optimal average fitness is combined with the reward population to produce a new reward population. During an experiment, tests were conducted first on determining the value of the harmony memory size (HMS) and the harmony memory consideration rate (HMCR), followed by an analysis of the effect of their values on the performance of the proposed algorithm. Then, six benchmark functions were selected for the experiment, and a comparison was made on the calculation results of the standard harmony memory search algorithm, reward population harmony search algorithm, differential genetic harmony algorithm, and reward population-based differential genetic harmony search algorithm. The result suggests that the reward population-based differential genetic harmony search algorithm has the merits of a strong global search ability, high solving accuracy, and satisfactory stability.

This research article describes the frequency regulation of an interconnected power system that includes wind energy systems and thermal non-reheat systems, with a Proportional Integral Derivative (PID) controller optimized using metaheuristic algorithms such as Genetic-Algorithm (GA), Harmony-Search-Algorithm (HSA), Bat-Algorithm (BA), and Flower-Pollination-Algorithm (FPA). With the demand for precisely efficient energy systems growing, system engineers are increasingly looking for the finely optimized control solution that also has the benefit of faster convergence and avoids entrapment in local minimal. To minimize the fitness function which is based on ITAE (Integral of Time multiplied Absolute Error) criteria composed of frequency and tie-line power changes, we have obtained an optimum solution in terms of PID controller gain values using the metaheuristics optimizing techniques. Change in frequency in area 1, deviation in tie-line power, and change in frequency in area 2 obtained from different techniques are compared. The results obtained by simulating MATLAB/Simulink convey that PID controller gain values optimized using the HSA technique provide better dynamic performance compared to BA, FPA and GA techniques. The simulation results have been experimentally validated using hardware-in-loop (HIL) on a real-time simulator based on field-programmable gate arrays (FPGA). The HSA optimized PID controller is used to investigate the robustness of the system by Step-Load-Perturbation (SLP) and Random Step Load Pattern (RSLP). Results obtained by running simulation also show that the HSA optimized PID controller for the same optimized gain value can withstand the SLP and RSLP variation made in the system.

L’objectif principal de la production d’énergie électrique est de fournir au consommateur l’énergie requise par un fonctionnement économique optimal. Le système électrique est un grand système interconnecté et l'utilisation de l'énergie augmente considérablement avec la modernisation et le coût de production de l'énergie électrique augmentera avec la croissance rapide de l'infrastructure de transport et de distribution, entraînant des pertes pour la nouvelle transmission et distribution. Il est donc nécessaire d'optimiser la production d'électricité à différents endroits du système d'alimentation. Ceci conduit à une minimisation du coût des unités de production thermique d'entrée répondant à la demande de charge et aux pertes de transmission. C’est une tâche complexe appelée Economic Dispatch (ED), qui inclut des charges quadratiques non linéaires et des chargements de points de vanne, et qui nécessite une résolution complexe. La littérature présente ces fonctions de coût convexes des unités de génération thermiques à vapeur lorsqu'elles sont incluses avec des chargements de points de vanne conduisant à une non-convexité entraînant un problème non convexe. Une modélisation parfaite de l'ED pour les unités génératrices thermiques à vapeur est possible avec les chargements de points de vannes. La résolution du problème non convexe par des méthodes conventionnelles est une question difficile car des changements soudains et des discontinuités sont possibles dans la fonction de coût incrémentiel. Un algorithme amélioré de recherche de l’harmonie(EHS) a été introduit dans cet article pour résoudre le problème de l'ED. La méthode proposée est appliquée à un système d’essai de treize unités avec deux demandes de charge différentes et les résultats sont également présentés. TITRE Solution de répartition optimale de la charge du système d'alimentation utilisant un algorithme de recherche d'harmonie amélioré TITLE Optimal load dispatch solution of power system using enhanced harmony search algorithm RÉSUMÉ L’objectif principal de la production d’énergie électrique est de fournir au consommateur l’énergie requise par un fonctionnement économique optimal. Le système électrique est un grand système interconnecté et l'utilisation de l'énergie augmente considérablement avec la modernisation et le coût de production de l'énergie électrique augmentera avec la croissance rapide de l'infrastructure de transport et de distribution, entraînant des pertes pour la nouvelle transmission et distribution. Il est donc nécessaire d'optimiser la production d'électricité à différents endroits du système d'alimentation. Ceci conduit à une minimisation du coût des unités de production thermique d'entrée répondant à la demande de charge et aux pertes de transmission. C’est une tâche complexe appelée Economic Dispatch (ED), qui inclut des charges quadratiques non linéaires et des chargements de points de vanne, et qui nécessite une résolution complexe. La littérature présente ces fonctions de coût convexes des unités de génération thermiques à vapeur lorsqu'elles sont incluses avec des chargements de points de vanne conduisant à une non-convexité entraînant un problème non convexe. Une modélisation parfaite de l'ED pour les unités génératrices thermiques à vapeur est possible avec les chargements de points de vannes. La résolution du problème non convexe par des méthodes conventionnelles est une question difficile car des changements soudains et des discontinuités sont possibles dans la fonction de coût incrémentiel. Un algorithme amélioré de recherche de l’harmonie(EHS) a été introduit dans cet article pour résoudre le problème de l'ED. La méthode proposée est appliquée à un système d’essai de treize unités avec deux demandes de charge différentes et les résultats sont également présentés. ABSTRACT The key object of electric power generation is to deliver required energy to the consumer by optimum economic operation. Power system is a large interconnected system and usage of power also increasing drastically with modernization and the cost of production of electric energy will increase with rapid growth of transmission and distribution infrastructure as losses for new transmission and distribution occurs. So there is a need for optimizing generation of power at different locations of power system. This leads to minimization of cost of input thermal generating units meeting the load demand and transmission losses. This is a challenging task called Economic Dispatch (ED) which includes non-linear quadratic function and valve point loadings, and requires complex solving. Literature exhibits those convex cost functions of steam thermal generating units when included with valve point loadings leads to non-convexity resulting in non-convex problem. Perfect modeling of ED for steam thermal generating units is possible with valve point loadings. Solving non-convex problem by conventional methods is a difficult issue as sudden changes and discontinuities are possible in incremental cost function. Improved Harmony Search (EHS) algorithm has been introduced in this workto resolve the ED problem. The proposed method is applied to thirteen-unit test system at 2-different load demands and the results also presented. AUTHOR(S) D.S.N.M. RAO, Niranjan KUMAR MOTS-CLÉS non convexe, répartition économique de la charge, algorithme de recherche de l’harmonie (HS), algorithme amélioré de recherche de l’harmonie(EHS), chargements de points de vannes. KEYWORDS non convex, economic load dispatch, harmony search algorithm (HS), enhanced harmony search algorithm (EHS), valve point loading. ARTICLE LANGUAGE English

This paper develops an improved novel global harmony search (INGHS) algorithm for solving optimization problems. INGHS employs a novel method for generating new solution vectors that enhances accuracy and convergence rate of novel global harmony search (NGHS) algorithm. Simulations for five benchmark test functions show that INGHS possesses better ability to find the global optimum than that of harmony search (HS) algorithm. Compared with NGHS and HS, INGHS is better in terms of robustness and efficiency.

The Harmony Search Algorithm (HSA) is a swarm intelligence optimization algorithm which has been successfully applied to a broad range of clustering applications, including data clustering, text clustering, fuzzy clustering, image processing, and wireless sensor networks. We provide a comprehensive survey of the literature on HSA and its variants, analyze its strengths and weaknesses, and suggest future research directions.

The coronavirus disease 2019 (COVID-19) was first reported in December 2019 in Wuhan, China, and then moved to almost every country showing an unprecedented outbreak. The world health organization declared COVID-19 a pandemic. Since then, millions of people were infected, and millions have lost their lives all around the globe. By the end of 2020, effective vaccines that could prevent the fast spread of the disease started to loom on the horizon. Nevertheless, isolation, social distancing, face masks, and quarantine are the best-known measures, in the time being, to fight the pandemic. On the other hand, contact tracing is an effective procedure in tracking infections and saving others' lives. In this paper, we devise a new approach using a hybrid harmony search (HHS) algorithm that casts the problem of finding strongly connected components (SCCs) to contact tracing. This new approach is named as hybrid harmony search contact tracing (HHS-CT) algorithm. The hybridization is achieved by integrating the stochastic hill climbing into the operators' design of the harmony search algorithm. The HHS-CT algorithm is compared to other existing algorithms of finding SCCs in directed graphs, where it showed its superiority over these algorithms. The devised approach provides a 77.18% enhancement in terms of run time and an exceptional average error rate of 1.7% compared to the other existing algorithms of finding SCCs.

Artificial intelligence is a future and valuable tool for early disease recognition and support in patient condition monitoring. It can increase the reliability of the cure and decision making by developing useful systems and algorithms. Healthcare workers, especially nurses and physicians, are overworked due to a massive and unexpected increase in the number of patients during the coronavirus pandemic. In such situations, artificial intelligence techniques could be used to diagnose a patient with life-threatening illnesses. In particular, diseases that increase the risk of hospitalization and death in coronavirus patients, such as high blood pressure, heart disease and diabetes, should be diagnosed at an early stage. This article focuses on diagnosing a diabetic patient through data mining techniques. If we are able to diagnose diabetes in the early stages of the disease, we can force patients to stay home and care for their health, so the risk of being infected with the coronavirus would be reduced. The proposed method has three steps: preprocessing, feature selection and classification. Several combinations of Harmony search algorithm, genetic algorithm, and particle swarm optimization algorithm are examined with K-means for feature selection. The combinations have not examined before for diabetes diagnosis applications. K-nearest neighbor is used for classification of the diabetes dataset. Sensitivity, specificity, and accuracy have been measured to evaluate the results. The results achieved indicate that the proposed method with an accuracy of 91.65% outperformed the results of the earlier methods examined in this article.

In this study, we propose a method to find an optimal combination of hyperparameters to improve the accuracy of respiration pattern recognition in a 1D (Dimensional) convolutional neural network (CNN). The proposed method is designed to integrate with a 1D CNN using the harmony search algorithm. In an experiment, we used the depth of the convolutional layer of the 1D CNN, the number and size of kernels in each layer, and the number of neurons in the dense layer as hyperparameters for optimization. The experimental results demonstrate that the proposed method provided a recognition rate for five respiration patterns of approximately 96.7% on average, which is an approximately 2.8% improvement over an existing method. In addition, the number of iterations required to derive the optimal combination of hyperparameters was 2,000,000 in the previous study. In contrast, the proposed method required only 3652 iterations.