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In this era of smartphones, a huge amount of multimedia files like audio, video, images, animation, and plain text are shared. And with this comes the threat of data being stolen and misused. Most people don’t think about the security of data before uploading it to any platform. Most apps used on smartphones upload our data to their server. Not only this, but other third-party apps can also read that data while it is being transmitted. One solution to this problem is encrypting the data before sharing it and decrypting it back at the other end so that even if it is intercepted in between the transmission, it would be impossible to decrypt it. In this paper, a newly designed hybrid encryption algorithm EMAES that includes the efficiency of MAES (Modified Advanced Encryption Standard) and security of ECC (Elliptic Curve Cryptography) was implemented in MATLAB as well as in android studio 4.0. using a mobile messaging application. Also, it was tested for different speeds and security parameters. Further, it was compared with standard algorithms like the RC4, RC6 and Blowfish as well as with other hybrid algorithms like RC4+ECC, RC6+ECC and Blowfish+ECC. The EMAES was found 30% more efficient in terms of encryption and decryption time. The security of EMAES also showed improvement when compared with other hybrid algorithms for parameters like SSIM (structural similarity index measure), SNR (Signal to Noise Ratio), PSNR(Peak Signal to Noise Ratio), MSE (Mean Squared Error) and RMSE (Root Mean Squared Error). And finally, no significant improvement was found in the CPU and RAM usage.

This paper elucidates diverse algorithms that are used in drones for face identification. Face identification is the thriving technology that utilizes image processing for the identification of human faces. The main reasons for the booming interest in face recognition include the rise in population which demands high security and surveillance systems, requisite for identity verification in the digital world, for the combat in rural areas, alleviation during disasters, and so on. The goal of this paper is to explore, compare and contrast numerous face identification algorithms such as Linear Discriminant Analysis (LDA), Local Binary Pattern Histogram (LBPH), Principal Component Analysis (PCA), Elastic Bunch Graph Matching (EBGM) and neural networks. This study would assist the technologists in the field of face recognition to frame a hybrid algorithm based on the needs of the real time applications.

In this paper, we present a new hybrid spectral-conjugate gradient (SCG) algorithm for finding approximate solutions to nonlinear monotone operator equations. The hybrid conjugate gradient parameter has the Polak–Ribiere–Polyak (PRP), Dai–Yuan (DY), Hestenes–Stiefel (HS) and Fletcher–Reeves (FR) as special cases. Moreover, the spectral parameter is selected such that the search direction has the descent property. Also, the search directions are bounded and the sequence of iterates generated by the new hybrid algorithm converge globally. Furthermore, numerical experiments were conducted on some benchmark nonlinear monotone operator equations to assess the efficiency of the proposed algorithm. Finally, the algorithm is shown to have the ability to recover disturbed signals.

Camel Traveling Behavior Algorithm (CA) is a nature-inspired meta-heuristic proposed in 2016 by Mohammed Khalid Ibrahim and Ramzy Salim Ali. There exist few publications that measure the performance of the CA on scientific literature. CA was implemented to global optimization and some engineering problems in the literature. It was shown that CA demonstrates better performance than Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) in global optimization. However, it gives poor solutions at combinatorial optimization as well as in traveling salesman problems (TSP). Besides, a modified camel algorithm (MCA) was applied in the field of engineering and was proved that it is better than Cuckoo Search (CS), PSO, and CA. Therefore, it is a need for improvement in CA by hybridizing with a constructive heuristic (Nearest Neighbor Algorithm-NN). A set of thirteen small and medium-scale datasets that have cities scales ranging from 29 to 195 was used in the comparative study. The results show that the hybrid algorithm (HA) outperforms Tabu Search (TS), GA, CA, and Ant system (AS) for 70% of all datasets, excluding wi29, eil76, pr76, and rat99. Also, it was given that a detailed analysis presents the number of best, worst, average solutions, standard deviation, and average CPU time. The metrics also stress that the hybrid meta-heuristic demonstrates 64% performance in finding acceptable solutions. Finally, the hybrid algorithm solves the discrete problem in short computational times when compared to other test algorithms for small and medium-scale datasets., Deve gezgin davranışı algoritması (CA) 2016 yılında Mohammed Khalid Ibrahim ve Ramzy Salim Ali tarafından önerilmiş, doğadan ilham alan bir meta-sezgiseldir. Bilimsel literatürde CA’ nın performansını ölçen birkaç çalışma bulunmaktadır. CA literatürde global optimizasyon ve mühendislik problemlerine uygulanmıştır. CA’ nın global optimizasyonda parçacık sürü optimizasyonu ve genetik algoritmadan daha iyi performans sergilediği gösterilmiştir. Buna karşın, bu algoritma gezgin satıcı probleminde olduğu gibi kombinatoryal optimizasyonda düşük kaliteli çözümler vermektedir. Bunun yanında, değiştirilmiş deve algoritması mühendislik alanında uygulanmıştır ve CS, PSO, CA’ dan daha iyi olduğu ortaya koyulmuştur. Bu sebeple, CA’ nın tur oluşturucu bir sezgiselle (En yakın komşu algoritması-NN) hibrid edilerek iyileştirilmesi ihtiyacı bulunmaktadır. Bu karşılaştırmalı uygulamada, 29-195 arasında değişen boyutlarda şehir içeren 13 küçük ve orta ölçekli veriseti kullanılmıştır. Sonuçlar, hibrid algoritmanın (HA), tabu arama (TS), GA, CA, ve karınca sistemine (AS) göre wi29, eil76, pr76, ve rat99 dışında bütün verisetlerinin %70 inde daha üstün olduğunu göstermektedir. Çalışmada, detaylı bir analiz verilerek en iyi, en kötü, ortalama çözümler, standard sapma, ve ortalama CPU zamanları sunulmaktadır. Metrikler, ayrıca hibrid meta-sezgiselin kabul edilebilir çözümleri bulmada 64% performans sergilediğini vurgulamaktadır. Sonuç olarak, hibrid algoritma küçük ve orta ölçekli verisetlerinde diğer test algoritmalarına kıyasla kesikli problemi daha kısa hesaplama zamanlarında çözmektedir.

In hazardous gas leakage accidents in chemical clusters, quickly and accurately locating the diffusion source via mobile sensors (searcher) can effectively speed up emergency response. While state-of-the-art researches designed kinds of autonomous sensing and searching techniques, e.g. cognitive-based strategies to complete the localization of the leakage source; the current studies cannot meet chemical cluster’s requirements with complex road network constraints and spatial scales. To address this problem, we firstly combine a cognitive search strategy with great theoretical performance (Entrotaxis algorithm) with an intermittent search strategy and propose a fresh searching algorithm, namely Entrotaxis-Turn (ET). The hybrid algorithm can make use of the triggered turn motion to bypass obstacles to avoid falling into a loop, thereby improving the success rate and saving the searching time. Secondly, we extend the ET algorithm to the multi-robot collaboration pattern (Multi-ET algorithm), in which each mobile sensor shares the information collected along its route with each other. To reveal the performance of the proposed algorithms, a simulation scene is built in a typical chemical cluster scene and diffused gas is generated based on the advection-diffusion equation. Through Monte Carlo simulations, the optimal parameter combinations in the ET and Multi-ET algorithm are obtained. Finally, a verification experiment is carried out based on the simulation scene generated by the computational fluid dynamics (CFD) model. Results show that compared with the Entrotaxis algorithm, the efficiency and success rate of the ET algorithm are dramatically improved. Moreover, the Multi-ET algorithm obtains the optimal source searching performance.

This paper deals with the school timetabling problem. The problem was formulated as encountered in a typical Greek high school. A local version of the particle swarm optimization algorithm was developed and applied to the problem at hand. Results on well-established benchmark instances showed that the proposed algorithm achieved the proven optima provided from an integer programming method presented in earlier research. In almost all cases, the current algorithm beat the integer programming method, either concerning the lower bound yielded or the execution time needed.

The current critical global concerns regarding fossil fuel exhaustion and environmental pollution have been driving advancements in transportation electrification and related battery technologies. In turn, the resultant growing popularity of electric vehicles (EVs) calls for the development of a well-designed charging infrastructure. However, an inappropriate placement of charging stations might hamper smooth operation of the power grid and be inconvenient to EV drivers. Thus, the present work proposes a novel two-stage planning model for charging station placement. The candidate locations for the placement of charging stations are first determined by fuzzy inference considering distance, road traffic, and grid stability. The randomness in road traffic is modelled by applying a Bayesian network (BN). Then, the charging station placement problem is represented in a multi-objective framework with cost, voltage stability reliability power loss (VRP) index, accessibility index, and waiting time as objective functions. A hybrid algorithm combining chicken swarm optimization and the teaching-learning-based optimization (CSO TLBO) algorithm is used to obtain the Pareto front. Further, fuzzy decision making is used to compare the Pareto optimal solutions. The proposed planning model is validated on a superimposed IEEE 33-bus and 25-node test network and on a practical network in Tianjin, China. Simulation results validate the efficacy of the proposed model.

This paper proposes a novel sine-cosine and Nelder-Mead (SCANM) algorithm which hybridizes the sine-cosine algorithm (SCA) and Nelder-Mead (NM) local search method. The original version of SCA is prone to early convergence at the local minimum. The purpose of the SCANM algorithm is to overcome this issue. Thus, it aims to overcome this issue with the employment of the NM method. The SCANM algorithm was firstly compared with the SCA algorithm through 23 well-known test functions. The statistical assessment confirmed the better performance of the proposed algorithm. The comparative convergence profiles further demonstrated the significant performance improvement of the proposed SCANM algorithm. Besides, a non-parametric test was performed, and the results that showed the ability of the proposed approach were not by coincidence. A popular and well-performed metaheuristic algorithm known as grey wolf optimization was also used along with the recent and promising two other algorithms (Archimedes optimization and Harris hawks optimization) to comparatively demonstrate the performance of the SCANM algorithm against well-known classical benchmark functions and CEC 2017 test suite. The comparative assessment showed that the SCANM algorithm has promising performance for optimization problems. The non-parametric test further verified the better capability of the proposed SCANM algorithm for optimization problems.

Nonlinear regression analysis is an important statistical method widely used in many fields of science to model the complex relationships between variables. Therefore, many studies have been conducted to estimate the parameters of nonlinear regression models using various iterative techniques. In this study, an efficient hybrid algorithm, namely PSONM, by combining the exploration capability of Particle Swarm Optimization (PSO) and the exploitation capability of the Nelder-Mead (NM) algorithm is proposed to obtain parameter estimates of nonlinear regression models. To show the performance of the proposed hybrid algorithm, 20 nonlinear regression tasks with various levels of difficulty, and real data sets in the agriculture field have been tested. The experimental results indicated that the suggested hybrid algorithm provides accurate estimates, and its performance is much superior to those of NM and PSO algorithms.

The rising volume of sensitive and personal data being harvested by data controllers has increased the security essentials in the cloud system. The cloud module is not used just to store the data, but also to process them on cloud premises. Security for the cloud premises is essential as the cloud has lot of outsourced, unprotected sensitive data for the public access. This has resulted repeated data violations, and thus there is a need for the advanced legal data protection constraints. Various studies were conducted to adopt the privacy preservation in the cloud, and most of the state-of-the-art techniques fail to handle the optimal privacy when dealing with sensitive data, as it requires separate data sanitization and restoration models. To overcome this challenge, this paper tempts to develop the privacy preservation model in the cloud environment using the advancements of artificial intelligent techniques. Artificial Intelligent capabilities are working in the business cloud computing environment to make organizations more efficient, strategic, and insight-driven. However, by hosting the data, cloud computing offers businesses high flexibility, agility, and cost savings. The two main phases of the proposed privacy preservation system are the data sanitization and restoration. Moreover, the proposed sanitization process depends on the optimal key generation, which is performed by the hybrid meta-heuristic algorithm. This hybrid algorithm merges two well-performed algorithms, such as Shark Smell Optimization (SSO) and Jaya Algorithm (JA), and thus termed as Jaya-based Shark Smell Optimization (J-SSO). The optimal key generation is accomplished by deriving a multi-objective function that involves the parameters, such as the degree of modification, hiding ratio, and information preservation ratio. Finally, the performance analysis has proved the efficiency of the proposed model over the state-of-the-art models in enhancing cloud security.

This paper introduces a complex real-world problem on a large scale that combines the split delivery heterogeneous vehicle routing problem with three-dimensional loading and considers new routing and loading constraints. The problem aims to find a set of routes with minimum transportation cost by satisfying the demand of all customers and loading constraints. We propose a column generation-based heuristic algorithm that employs three heuristic algorithms to solve the subproblem and a hybrid algorithm for loading. Computational experiments on the literature instances indicate that the proposed algorithm can produce very near solutions to the literature algorithm's solutions in significantly shorter running times. This paper introduces real-world instances with 230 to 850 customers, several times larger than the existing benchmarks. The results of the proposed algorithm for real-world instances are effective, and compared to the current situation, it leads to a reduction of 20% in the average total cost of transportation. Also, the average number of vehicles is reduced by 36%.

In recent years, the telecommunication industry is growth and become very competitive where has reached the point maintaining customer is very essential than acquiring new customer. And the two key factor for maintaining customer, the first is defining the segment of customer want to churn and the second is accuracy of predictive model. In this article we propose the hybrid model based on decision tree and artificial neural network (ANN) with the two stages of process to answer the problem of maintaining customer, the first is a segmentation phase with decision rules and the second is a prediction phase with artificial neural network (ANN). Our finding in benchmarked against the previous algorithms (decision tree and ANN) with the AUC metrics show the proposed model or hybrid achieves better accuracy and with the comprehensive information of what a drive customer churn

This paper presents a new hybrid algorithm that combines genetic algorithms (GAs) and the optimizing spotted hyena algorithm (SHOA) to solve the production shop scheduling problem. The proposed GA-SHOA algorithm incorporates genetic operators, such as uniform crossover and mutation, into the SHOA algorithm to improve its performance. We evaluated the algorithm on a set of OR library instances and compared it to other state-of-the-art optimization algorithms, including SSO, SCE-OBL, CLS-BFO and ACGA. The experimental results show that the GA-SHOA algorithm consistently finds optimal or near-optimal solutions for all tested instances, outperforming the other algorithms. Our paper contributes to the field in several ways. First, we propose a hybrid algorithm that effectively combines the exploration and exploitation capabilities of SHO and GA, resulting in a balanced and efficient search process for finding near-optimal solutions for the FSSP. Second, we tailor the SHO and GA methods to the specific requirements of the FSSP, including encoding schemes, objective function evaluation and constraint handling, which ensures that the hybrid algorithm is well suited to address the challenges posed by the FSSP. Third, we perform a comprehensive performance evaluation of the proposed hybrid algorithm, demonstrating its effectiveness in terms of solution quality and computational efficiency. Finally, we provide an in-depth analysis of the behavior of the hybrid algorithm, discussing the roles of the SHO and GA components and their interactions during the search process, which can help understand the factors contributing to the success of the algorithm and provide insight into potential improvements or adaptations to other combinatorial optimization problems.

We propose an approach for large-scale non-separable nonlinear multicommodity flow problems by solving a sequence of subproblems which can be addressed by commercial solvers. Using a combination of solution methods such as modified gradient projection, shortest path algorithm and golden section search, the approach can handle general problem instances, including those with (i) non-separable cost, (ii) objective function not available analytically as polynomial but are evaluated using black-boxes, and (iii) additional side constraints not of network flow types. Implemented as a toolbox in commercial solvers, it allows researchers and practitioners, currently conversant with linear instances, to easily manage large-scale convex instances as well. In this article, we compared the proposed algorithm with alternative approaches in the literature, covering both theory and large test cases. New test cases with non-separable convex costs and non-network flow side constraints are also presented and evaluated. The toolbox is available free for academic use upon request.

Introduction In a Dutch heart centre, a dedicated chronic total occlusion (CTO) team was implemented in June 2017. The aim of this study was to the evaluate treatment success and clinical outcomes before and after this implementation. Methods A total of 662 patients who underwent percutaneous coronary intervention (PCI) for a CTO between January 2013 and June 2020 were included and divided into pre– and post–CTO team groups. The primary endpoint was the angiographic success rate of CTO-PCI. Secondary endpoints included angiographic success stratified by complexity using the J‑CTO score and the following clinical outcomes: in-hospital complications and myocardial infarction, target vessel revascularisation, all-cause mortality, quality of life (QoL) and major adverse cardiac events (MACE) at 30-day and 1‑year follow-up. Results Compared with the pre–CTO team group, the success rate in the post–CTO team group was higher after the first attempt (81.4% vs 62.7%; p p = 0.001). This was mainly driven by higher success rates for difficult and very difficult CTO lesions according to the J‑CTO score. The MACE rate at 1 year was lower in the post–CTO team group than in the pre–CTO team group (6.4% vs 16.0%; p p = 0.74). Angina symptoms were significantly reduced at 30-day and 1‑year follow-up, and QoL scores were higher after 1 year. Conclusion This study demonstrated higher success rates of CTO-PCI and improved clinical outcomes and QoL at 1‑year follow-up after implementation of a dedicated CTO team using the hybrid algorithm.

The operation of connected platooning vehicles (CPVs) with V2V and V2I techniques has the potential to decrease the overall aerodynamic drag and reduce fuel consumption. One of the key technologies for CPVs is the spacing control which is heavily dependent on the drag force. However, the existing studies on drag coefficient of CPVs seldom pay attention to heterogeneous platoon, and most analysis only consider the effect of vehicle spacing and ignore key information such as cross-sectional area of adjacent vehicles. In addition, most research only focuses on qualitative research, without concluding a quick formula for calculating vehicle drag coefficient in a platoon. In this work, we investigate the modeling and estimation of drag coefficient in terms of the inter-vehicle distances, the platoon configurations, and the cross-sectional area of the adjacent vehicles. We calculate the drag coefficients for ten categories of vehicles with different inter-vehicle distances via numerical analysis in the software FLUENT. According to hundreds of the analysis results, we propose a model to estimate the drag correction factor online. A hybrid algorithm combining the BP neural network (BPNN) and particle swarm optimization (PSO) is employed to optimize the parameters in the model. The model is validated via three kinds of regression evaluation indexes and additional simulation tests with different platooning configurations and different types of vehicles. In terms of the comparison results, the developed model is effective to estimate the drag coefficients of CPVs.

Fuzzy logic controllers (FLCs) are widely used to control complex systems with model uncertainty, such as alternating current motors. The design process of the FLC is generally based on the designer’s adjustments on the controller until the desired performance is achieved. However, doing the controller design in this way makes the design process quite difficult and time-consuming, so it is often impossible to make a suitable and successful design. In this study, the output membership functions of the FLC are optimized with heuristic algorithms to reach the best speed control performance of the permanent magnet synchronous motor (PMSM). This paper proposes a new hybrid algorithm called H-GA-GSA, created by combining the advantages of the Genetic Algorithm (GA) and Gravitational Search Algorithm (GSA) to optimize FLC. The paper presents a convenient adjustment and design method for optimizing FLC with heuristic algorithms considered. To evaluate the effectiveness of H-GA-GSA, the proposed hybrid algorithm has been compared with GA and GSA in terms of convergence rate, PMSM speed control performance and electromagnetic torque variations. Optimization performance and results obtained from simulation studies verify that the proposed hybrid H-GA-GSA outperforms GA and GSA.

The paper demonstrates the potential of a hybrid Wind–PV farm as STATCOM (Static Synchronous Compensator) for damping and control of overall chaotic oscillations in a two-area power system. Kundur’s modified system associated with a hybrid Wind–PV farm is simulated in MATLAB to demonstrate the detailed performance assessment of the hybrid farm. A unique controller is deployed to regulate the AC and DC currents of the STATCOM using two PI controllers. A swarm-based hybrid metaheuristic optimizer PSO-BFOA optimally tunes and controls the PI controller parameters. The system is compensated to an optimum level of 85% and exposed to a 3-phase fault. Zero dampings are accompanied by additional disturbances of a 20% change in electric torque and the reference voltage. The disturbances are made to model the worst conditions to examine the rigorous performance of the hybrid Wind–PV​ farm in alleviating the overall chaotic oscillations. The simulation results for the performance assessment in different cases, i.e., without control, with Wind-STATCOM, with PV-STATCOM, and with the hybrid Wind–PV-STATCOM, reveal the potential of the hybrid Wind–PV Farm as STATCOM in alleviating the overall chaotic oscillations.

In this study, an effective intelligent system based on artificial neural networks (ANN) and a new version of the sine cosine algorithm (SCA) is developed to evaluate and predict the FOS of homogenous slopes under static and dynamic loading. In the first step, an effective hybrid optimization algorithm based on the adaptive sine cosine algorithm (ASCA) and pattern search (PS), namely ASCPS, is proposed and verified using a set of benchmark test functions. Then, the new algorithm, along with the Morgenstern and Price method is applied for seismic slope stability evaluation. To provide a neural network training dataset, a set of 189 slopes with different values of slope height, slope angle, friction angle of soil, soil cohesion, and horizontal acceleration coefficient have been analyzed and their corresponding FOS have been recorded. In the next step, the proposed ASCPS algorithm is implemented for training the ANN model using the collected database. The performance and prediction capacity of the developed model are evaluated using root mean square error (RMSE) and correlation coefficient (R). According to the obtained results, the ANN model with the RMSE value of 0.023 and the R value of 0.984 is a reliable, simple, and valid computational model for estimating the FOS and evaluating the slope stability under static and earthquake loads. In addition, the developed ANN model is applied to a case study of slope stability from previous studies, and the results reveal that the proposed model may provide better optimal solutions and outperform existing methods.

Development and deployment of FSPV systems are still in the nascent stages hence long-term performance, control and feasibility study of FSPV systems are not well addressed. Precise and robust estimation of FSPV panel parameters will play an important role in determining the actual performance, carbon savings and long-term feasibility studies of FSPV systems. Here, hybrid stochastic firefly algorithm (HSFA) is used for parameter estimation and optimization. The hybrid algorithm is used to find out the parameters of single diode model, double diode model and FSPV module. An experiment is also performed using FSPV modules under varying irradiance conditions. The accuracy of model is evaluated by comparing the simulated results with the experimental results and computing the relative error and root mean square error (RMSE). The parameters extracted using the proposed method has a very low RMSE value of 9.83002E-04. Assessment of the experimental and estimated results show that the relative error for measured electricity on a sunny day is 0.57% while for an overcast day it is 0.89%. For partial shading condition, the relative error and RMSE was 0.79% and 6.5%, respectively. The results which are in well agreement with the experimental values demonstrate the superior performance of the model in determining the FSPV parameters. Proper estimation of the FSPV parameters will help researchers, scientists, engineers and all actors associated with solar PV systems in making sound judgements towards the deployment of FSPV systems and help the society in developing a sustainable ecosystem towards implementation of industry 4.0 by adapting to low-carbon power generation methods.

We consider the setting where a seller must allocate a collection of goods to budgeted buyers, as exemplified by online advertising systems where platforms decide which impressions to serve to various advertisers. Such resource allocation problems are challenging for two reasons: (a) the seller must strike a balance between optimizing her own revenues and guaranteeing fairness to her (repeat) buyers and (b) the problem is inherently dynamic due to the uncertain, time-varying supply of goods available with the seller. We propose a stochastic approximation scheme akin to a dynamic market equilibrium. Our scheme relies on frequent re-solves of an Eisenberg-Gale convex program, and does not require the seller to have any knowledge about how goods arrival processes evolve over time. The scheme fully extracts buyer budgets (thus maximizing seller revenues), while at the same time provides a 0.47 approximation of the proportionally fair allocation of goods achievable in the offline case, as long as the supply of goods comes from a wide family of (possibly non-stationary) Gaussian processes. We then extend our results to a more general family of metrics called \alpha-fairness. Finally, we deal with a multi-objective problem where the seller is concerned with both the proportional fairness and efficiency of the allocation, and propose a hybrid algorithm which achieves a 0.27 bi-criteria guarantee against fairness and efficiency.

Bidirectional reflectance distribution function (BRDF) models are used to correct surface bidirectional effects and estimate land surface albedo. Many operational BRDF/albedo algorithms adopt a Roujean linear kernel-driven BRDF (RLKB) model because of its simple form and good performance in fitting multidirectional surface reflectance values. However, this model does not explicitly consider topographic effects, resulting in errors when applied over rugged terrain. To address this issue, we proposed a hybrid algorithm suitable for both flat and rugged terrain, called topographical kernel-driven (Topo-KD). First, we constructed a linear kernel-driven BRDF model considering terrain (LKB_T) which describes the topographic effects with a mountain radiative transfer (MRT) model. Then, the Topo-KD algorithm adaptively selects the most suitable model (RLKB or LKB_T) according to the terrain conditions and fitting residuals. The performances of Topo-KD and RLKB using the RossThick-LiSparseReciprocal (RTLSR) kernel are compared using simulated data sets and moderate-resolution imaging spectroradiometer (MODIS) observations. The results show that the BRDF of the pixel is affected by topography. But the RTLSR model does not specifically account for it, resulting in larger biases over rugged terrain than the Topo-KD algorithm in both the red and near-infrared (NIR) bands. The experiment using MODIS data sets demonstrates that the Topo-KD algorithm reduces fitting residuals in the red and NIR bands by 21.5% and 27.4% compared with the RTLSR model. These results indicate that the Topo-KD algorithm can be a better choice for retrieving land surface parameters and describing the radiative transfer process in mountainous areas.

Many new algorithms have been proposed to solve the mathematical equations formulated to describe the real-world problems. But there still does not exist one algorithm that could solve the problems all. And most of the proposed algorithms have defects in some aspects, they need to be improved in application. In order to find a more efficient optimization algorithm and inspired by the better performance of the Arithmetic Optimization algorithm (AOA) and Aquila Optimizer (AO), we proposed a hybridization algorithm of them and abbreviated AOAAO in this paper. Considering the better performance of the Harris Hawk optimization (HHO) algorithm, an energy parameter E was also introduced to balance the exploration and exploitation procedures of individuals in AOAAO swarms, and furthermore, piecewise linear map was introduced to decrease the randomness of the energy parameter. Pseudo code of the proposed AOAAO algorithm was presented, Simulation experiments were carried out on the benchmark functions and three classical engineering problems were also involved in optimization. Nine popular well demonstrated algorithms were included for comparison. Results confirmed the AOAAO would be more efficient in optimization with faster convergence rate, and higher convergence accuracy.

In this paper, we study the scheduling problem for a customized production system consisting of a flow shop production line with a parallel assembly stage that produces various products in two stages. In the first stage of the production line, parts are produced using a flow shop production line, and in the second stage, products are assembled on one of the parallel assembly lines. The objective is to minimize the time required to complete all goods (makespan) using efficient scheduling. A mathematical model is developed; however, the model is NP-hard and cannot be solved in a reasonable amount of time. To solve this NP-hard problem, we propose two well-known metaheuristics and a hybrid algorithm. To calibrate and improve the performance of our algorithms, we employ the Taguchi method. We evaluate the performance of our hybrid algorithm with the two well-known methods of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) and demonstrate that our hybrid algorithm outperforms both the GA and PSO approaches in terms of efficiency.

This paper proposes a new hybrid algorithm for grey wolf optimization (GWO) integrated with a robust learning mechanism to solve the large-scale economic load dispatch (ELD) problem. The robust learning grey wolf optimization (RLGWO) algorithm imitates the hunting behavior and social hierarchy of grey wolves in nature and is reinforced by robust tolerance-based adjust searching direction and opposite-based learning. This technique could effectively prevent search agents from being trapped in local optima and also generate potential candidates to obtain a feasible solution. Several constraints of power generators, such as generation limits, local demand, valve-point loading effect, and transmission losses, are considered in practical operation. Five test systems are used to evaluate the effectiveness and robustness of the proposed algorithm in solving the ELD problem. The simulation results clearly reveal the superiority and feasibility of RLGWO to find better solutions in terms of fuel cost and computational efficiency when compared with the previous literature.

Power from solar energy is not reliable, due to weather-related factors, which diminishes the power system’s reliability. Therefore, this study suggests a way to predict the intensity of solar irradiance using various statistical algorithms and artificial intelligence. In particular, we suggest the use of a hybrid predictive model, combining statistical properties and historical data training. In order to evaluate the maximum prediction steps of solar irradiance, the maximum Lyapunov exponent was applied. Then, we used the cosine similarity algorithm in the hidden Markov model for the initial prediction. The combination of the Hurst exponent and tail parameter revealed the self-similarity and long-range dependence of the fractional generalized Pareto motion, which enabled us to consider the iterative predictive model. The initial prediction was substituted into a stochastic differential equation to achieve the final prediction, which prevents error propagation. The effectiveness of the hybrid model was demonstrated in the case study.

A computer program, capable of carrying out the mathematical modelling and optimization of multi effect evaporation (MEE) systems is developed in the scope of this study. C# programming language is used in the development of the computer program. A Particle Swarm Optimization (PSO) based algorithm is developed and hybridized with a Levenberg-Marquardt (LM) based algorithm. A computer program interface is developed in .NET platform for the user to give inputs such as feed and product streams flowrate and concetration. The optimization results is represented through this interface. Concentrating the sodium hydroxide content in the wastewater of the mercerization process is selected as the sample case.

This paper suggests a new hybrid algorithm by integrating two population-based algorithms: Whale Optimization Algorithm (WOA) and Flower Pollination Algorithm (FPA), to solve complex nonlinear systems and unconstrained optimization problems. WOFPA denotes the suggested algorithm, a hybrid Whale Optimization Algorithm and Flower Pollination Algorithm. Nonlinear systems can be cast into unconstrained optimization problems, called merit functions, where the optimal solutions for the merit functions are equivalent to the solutions of nonlinear systems. WOFPA aims to decrease the execution time and the complexity of WOA and FPA. WOFPA has the advantages of WOA and FPA; WOFPA is a high-quality algorithm to solve both problems, nonlinear systems and unconstrained optimization problems. For example, FPA may have a premature convergence in the local optima, and WOFPA subdues the disadvantage of FPA. Numerical experiments of 14 benchmarks nonlinear systems and 30 CEC 2014 benchmarks unconstrained optimization functions with various dimensions are employed to test the performance of WOFPA. To have a further investigation for the performance of WOFPA, WOFPA is compared with WOA, FPA, and other existing algorithms from the literature. Two non-parametric statistical tests, Wilcoxon statistical test and the Friedman test, are conducted for this study to check the performance of the proposed algorithms and other compared algorithms and the significance of our results. The experiment results demonstrate that WOFPA performs better than other algorithms in the literature by getting the optimum solutions for most nonlinear systems and optimization problems and proves its efficiency compared with other existing algorithms.

The hybrid algorithm was designed to assist with initial and subsequent crossing strategy selection in chronic total occlusion (CTO) percutaneous coronary interventions (PCIs). However, the success of the initially selected strategy has received limited study.We examined the impact of adherence to the hybrid algorithm recommendation for initial CTO crossing technique selection in 4178 CTO PCIs from a large multicenter registry.The initial crossing strategy was concordant with the hybrid algorithm recommendation in 1833 interventions (44%). Patients in the concordant group had a similar age to those in the discordant group but a lower mean J-CTO score (2.0 ± 1.4 vs 2.8 ± 1.1; P .01). The concordant group showed higher technical success with the first crossing strategy (68% vs 48%; P .01) and higher overall technical success (88% vs 83%; P .01) with no difference in the incidence of in-hospital major adverse events (1.8% vs 2.3%; P = .26). In multivariable analysis, after adjustment for age, prior myocardial infarction, prior PCI, prior coronary artery bypass grafting, J-CTO score, and scheduled CTO PCI, nonadherence to the hybrid algorithm was independently associated with lower technical success of the initial crossing strategy (odds ratio, 0.55; 95% confidence interval, 0.48-0.64; P .01).Adherence to the hybrid algorithm for initial crossing strategy selection is associated with higher CTO PCI success but similar in-hospital major adverse cardiac events.

Marine Predators algorithm (MPA) is a newly proposed nature-inspired metaheuristic algorithm. The main inspiration of this algorithm is based on the extensive foraging strategies of marine organisms, namely Lévy movement and Brownian movement, both of which are based on random strategies. In this paper, we combine the marine predator algorithm with Teaching-learning-based optimization algorithm, and propose a hybrid algorithm called Teaching-learning-based Marine Predator algorithm (TLMPA). Teaching-learning-based optimization (TLBO) algorithm consists of two phases: the teacher phase and the learner phase. Combining these two phases with the original MPA enables the predators to obtain prey information for foraging by learning from teachers and interactive learning, thus greatly increasing the encounter rate between predators and prey. In addition, effective mutation and crossover strategies were added to increase the diversity of predators and effectively avoid premature convergence. For performance evaluation TLMPA algorithm, it has been applied to IEEE CEC-2017 benchmark functions and four engineering design problems. The experimental results show that among the proposed TLMPA algorithm has the best comprehensive performance and has more outstanding performance than other the state-of-the-art metaheuristic algorithms in terms of the performance measures.

© 2021 The AuthorThis paper investigates the predictability of the asset prices of commodity transport (i.e. dry bulk carriers) by testing the shipping Q index as a leading indicator. We employ a comprehensive back-testing procedure with a broad spectrum of benchmark simulations. The shipping Q index (an adaptation of Tobin's Q index) has been introduced to benchmark models to observe predictive gain and interpret predictability features. This study presents a novel hybrid model to forecast time series data. The forecasting ability of the proposed hybrid algorithm is compared to specific univariate time series models, dynamic models, nonlinear models, and widely used hybrid models in the literature. The findings document that not only the proposed hybrid model performs better than the other competitive models in terms of hold out sample forecasting, but also using the shipping Q index improves the forecast accuracy by remarkably reducing forecasting error.

Sunshine duration is an important atmospheric indicator used in many agricultural, architectural, and solar energy applications (photovoltaics, thermal systems, and passive building design). Hence, it should be estimated accurately for areas with low-quality data or unavailable precise measurements. This paper aimed to obtain a sunshine duration measurement database in Algeria’s south region and also to study the applicability of computational models to predict them. This work develops ensemble learning models for assessing daily sunshine duration with meteorological datasets that include daily mean relative humidity, daily mean air temperature, daily maximum air temperature, daily minimum air temperature, and daily temperature range as input. The study proposes a unique hybrid model, combining grey wolf and stochastic fractal search (GWO-SFS) optimization algorithms with the random forest regressor ensemble. A pre-feature selection process improved the newly suggested model. Various commonly adopted algorithms in relevant studies have been considered as references for evaluating the new hybrid algorithm. The accuracy of models was examined as a function of some frequently used statistical pointers, as well as the Wilcoxon rank-sum test. Besides, the models were evaluated according to the several input combinations. The numerical experiments show that the proposed optimization ensemble with feature preprocessing outperforms stand-alone models in terms of prediction accuracy and robustness, where relative root mean square errors are reduced by over 20% for all considered locations. In addition, all correlation coefficients are higher than 0.999. Moreover, the proposed model, with RMSEs lower than 0.4884 hours, shows significantly superior performances compared to previously proposed models in the literature.

Federated learning (FL) is a distributed model for deep learning that integrates client-server architecture, edge computing, and real-time intelligence. FL has the capability of revolutionizing machine learning (ML) but lacks in the practicality of implementation due to technological limitations, communication overhead, non-IID (independent and identically distributed) data, and privacy concerns. Training a ML model over heterogeneous non-IID data highly degrades the convergence rate and performance. The existing traditional and clustered FL algorithms exhibit two main limitations, including inefficient client training and static hyperparameter utilization. To overcome these limitations, we propose a novel hybrid algorithm, namely, genetic clustered FL (Genetic CFL), that clusters edge devices based on the training hyperparameters and genetically modifies the parameters clusterwise. Then, we introduce an algorithm that drastically increases the individual cluster accuracy by integrating the density-based clustering and genetic hyperparameter optimization. The results are bench-marked using MNIST handwritten digit dataset and the CIFAR-10 dataset. The proposed genetic CFL shows significant improvements and works well with realistic cases of non-IID and ambiguous data. An accuracy of 99.79% is observed in the MNIST dataset and 76.88% in CIFAR-10 dataset with only 10 training rounds.

In this paper, a novel optimization algorithm is proposed by hybridizing firefly and whale optimization algorithms and it is applied to the problem of determining optimal design parameters of model predictive control. The developed hybrid algorithm is compared with firefly and whale optimization algorithms considering unimodal, multimodal, composite and CEC-C06 2019 benchmark optimization problems in terms of statistical analysis. Comparison results showed that hybrid algorithm has the best performance for approximately 67% of 39 benchmark functions in finding the minimum in an average of independent 100 experiments for each function. For the functions in which the proposed algorithm gives the best results, approximately 64% improvement is achieved compared to FA and 39% improvement compared to WOA. In terms of convergence speed, in average 2% of improvement is obtained. As an application to engineering problems, optimization of parameters of the model predictive control for inverted pendulum and distillation column is considered and the performance of the proposed algorithm is compared with the performances of firefly algorithm and whale optimization algorithm. For the considered applications, a novel objective function is selected. The selected objective function for hybrid firefly–whale optimization algorithm enables finding the optimal prediction horizon and control horizon as well as the Q and R matrices such that the system responses satisfy desired settling time and maximum overshoot criteria. Most of considered comparisons demonstrate that the proposed hybrid algorithm has better performance.

An innovative hybrid algorithm (ihPSODE) is proposed in this paper to solve non-convex dynamic economic dispatch (DED) problem. It integrated with a novel particle swarm optimization (nPSO) and differential evolution (nDE). A new inertia weight, gradually varying acceleration coefficient and position update equation introduced in nPSO, to avoid stagnation. And in nDE a new mutation strategy and crossover rate are presented, to avoid premature convergence. Additionally, in ihPSODE after calculation identify best half member and discard rest of members from the population. In current population applied nPSO, to maintain exploration and exploitation. Then to enhance local search ability and improve convergence accuracy applied nDE. Hence, proposed ihPSODE has higher probability of avoiding local optima and it is likely to find global optima more quickly. Firstly, proposed ihPSODE as well as its anticipated integrating component nPSO and nDE are testified on IEEE CEC2006 constrained benchmark functions. Then performance of these proposed algorithms are evaluated with non-convex DED problem with valve-point effects using five different test systems (5-unit with losses, 10-unit without losses, 10-unit with losses, 15-unit with losses and 30-unit without losses). Several numerical and graphical experiments have been done to verify performances of proposed algorithms. Additionally, statistical and comparative analysis confirms superiority of proposed algorithms over many state-of-the-art algorithms.

We present a two-step procedure called the dynamical self-energy mapping (DSEM) that allows us to find a sparse Hamiltonian representation for molecular problems. In the first part of this procedure, the approximate self-energy of a molecular system is evaluated using a low-level method and subsequently a sparse Hamiltonian is found that best recovers this low-level dynamic self-energy. In the second step, such a sparse Hamiltonian is used by a high-level method that delivers a highly accurate dynamical part of the self-energy that is employed in later calculations. The tests conducted on small molecular problems show that the sparse Hamiltonian parameterizations lead to very good total energies. DSEM has the potential to be used as a classical-quantum hybrid algorithm for quantum computing where the sparse Hamiltonian containing only O(n2) terms on a Gaussian orbital basis, where n is the number of orbitals in the system, could reduce the depth of the quantum circuit by at least an order of magnitude when compared with simulations involving a full Hamiltonian.

The echo signal energy of surface acoustic wave (SAW) strain sensor is weak, resulting in low demodulation accuracy. In order to improve the measurement result, a SAW sensor demodulation method by combining hybrid changing area niche genetic algorithm and MUSIC algorithm was proposed. The proposed method was based on the MUSIC algorithm to construct the power spectrum function of SAW echo signal. A hybrid changing area niche genetic algorithm (CANGA) and particle swarm optimization (PSO) algorithm was developed to improve the spectrum peak search accuracy and reduce the amount calculations of MUSIC algorithm. The power spectrum function of the MUSIC algorithm was used as the fitness function of the hybrid algorithm. The echo frequency was estimated by the variable value corresponding to the optimal solution. The proposed hybrid PSO-CANGA has the characteristics of strong global search ability and fast convergence speed to improve the performance of MUSIC algorithm. The numerical comparisons were performed. The results showed that the hybrid PSO-CANGA gives fastest convergence and the highest estimation accuracy compared to original PSO and NGA. In addition, compared with traditional spectrum estimation algorithms, the proposed algorithm had a minimum estimation error with standard deviation of 0.27KHz. The proposed demodulation method was used to SAW strain system. The nonlinearity of the strain measurement was improved, with a nonlinearity of 0.55%. Simulation and experiment results verified the effectiveness of the proposed algorithm for the demodulation of the SAW strain sensor system. It helps to improve the accuracy of SAW strain measurement.

In this article, a hybrid algorithm of Levenberg–Marquardt (LM) and proportional-integral-derivative neural network (PIDNN) with the relay feedback (LM-PIDNN-RF) is proposed to solve control problems for unknown discrete nonlinear systems. First, the PIDNN is initialized by the relay feedback to solve the problem of assigning initial weights; meanwhile, the LM neural network is regarded as an identifier to fit system input/output quickly and accurately. Second, the partial derivative of the system output to system input is transferred to the PIDNN, which ensures that the weights of the PIDNN can be updated correctly in time. The hybrid algorithm can update the weights of the neural network controller correctly against the errors caused by system instantaneous disturbance, and the controller has only one parameter to be tuned manually. Moreover, the stability of the closed-loop system is proven by using the Lyapunov stability theory. The proposed hybrid algorithm can significantly improve tracking performance in comparison with PIDNN and RF-PID. The results of three simulation examples and a physical experiment are presented to show superior tracking performance of the proposed algorithm.

The most important and demanding part of the artificial neural network is the training process which involves finding the most suitable values for the weights in the network architecture, a challenging optimization problem. Gradient approaches and the meta-heuristic approaches are two methods extensively used to optimize the weights in the network. Gradient approaches have serious disadvantages including getting stuck in local optima, inadequate exploration, etc. To overcome these disadvantages, meta-heuristic approaches are preferred in training the artificial neural network instead of gradient methods. Therefore, in this study, an improved animal migration optimization algorithm with the Levy flight feature was proposed to train the multilayer perceptron. The proposed hybrid algorithm is named IAMO-MLP. The main contributions of this article are that the IAMO algorithm was developed, the IAMO-MLP algorithm can successfully escape from local optima, and the initial positions did not affect the performance of the IAMO-MLP algorithm. The enhanced algorithm was tested and validated against a wider set of benchmark functions and indicated that it substantially outperformed the original implementation. Afterward, the IAMO-MLP was compared with ten algorithms on five classification problems (xor, balloon, iris, breast cancer, and heart) and one real-world problem in terms of mean squared error, classification accuracy, and nonparametric statistical Friedman test. According to the results, the IAMO was successful in training the multilayer perceptron.

Low-inertia microgrids may easily have a single load that can make up most of the total load, thereby greatly affecting stability and power quality. Instead of a static load model, a dynamic constant power load (DCPL) model is considered here. Next, a hybrid algorithm for parameter estimation (HAPE) is introduced. In order to verify the load model and the HAPE, two experiments are conducted with different DCPLs using a power-hardware-in-the-loop (PHiL) testbed. The PHiL testbed consists of a real-time computer working with a programmable power amplifier in order to perturb the input voltage's amplitude and frequency. Each connected DCPL in two separate experiments serves as the device under test. Using the captured experimental data as a reference, the HAPE is then invoked. The resulting parameter estimates are used to define simulation models. Both resulting DCPL models are simulated to produce waveforms that closely resemble experimental waveforms. Finally, the HAPE's resulting parameter estimates are presented, and the performance of the HAPE is discussed.

This paper proposes a hybrid algorithm for capacity configuration optimization of a solar PV-battery-based micro-grid. The hybrid algorithm (BAPSO), which is a combination of Particle Swarm Optimization (PSO) and Bat Algorithm (BA), is designed to optimize the solar generation location and capacity for the efficient performance of a micro-grid. The algorithm considers dynamic transmission power loss optimization and integrates PSO and BA algorithms’ respective advantages to form a hybrid algorithm. The proposed algorithm combines PSO’s fast convergence ability with the less computation time ability of BA to better optimal solution by incorporating the BA’s frequency into the PSO velocity equation to control the pace. The design from the proposed algorithm is tested and validated on IEEE 30 bus test system. The transmission power loss before implementing the algorithm was 22 kW and reduced to 20 kW after the algorithm is used. A further reduction of 0.3 kW of losses is observed by placing the solar generation system at bus number 29.

In this paper, we propose a novel hybrid algorithm based on MAX-MIN Ant System version of ant colony optimization coupled with quadratic programming (MMAS-QP). Quadratic programming is used to optimize the Economic Dispatching process and MMAS for planning the switching schedule of a set of production units. The algorithm is implemented in MATLAB software environment for two systems, one is 4 generating units running for 8 hours, and the other is 10 generating units running for 24 hours. The impact of heuristic parameters on the behavior of the algorithm is highlighted through the parameters setting. Results obtained shows improved solution compared to several methods such as Modified Ant Colony Optimization (MACO), particle Swarm Optimization combined with Lagrange Relaxation (PSO-LR), Swarm and Evolutionary Computation (SEC), Particle Swarm Optimization combined with Genetic Algorithm (PSO-GA). The proposed method improves sufficiently the quality of the solution as well as the execution time.

With the rapid development of cloud computing technology, online recommendation services have been a new trend for providing specific information on topics such as lifestyle, fashion news and a variety of other activities. Recommendation algorithm is crucially important for the delivery of personalized services to users. Currently, the key important problem is how to improve the quality of online recommendation services. To solve the problem of goods popularity bias, this paper introduces the prevalence of items into user interest model, and proposes an item popularity model based on user interest feature. Usually, traditional models do not consider the stable of users’ interests, which may make it hard to capture their interest. To deal with the above situation, we propose a time-sensitive and stable interest similarity model to calculate the similarity of user interest. Furthermore, a novel algorithm termed as item popularity similarity with time sensitivity (IPSTS)is proposed by combining the above two kinds of similarity model, and they are assigned different weight factors to balance their impacts. In this paper, we conduct the comparative experiments to evaluate the proposed approach and the traditional collaborative filtering algorithms. The final experimental results indicate that IPSTS can effectively reduce the value of Mean Absolute Error (MAE) and Root Mean Square Error (RMSE).

In DNA microarray applications, many techniques are proposed for cancer classification in order to detect normal and cancerous humans or classify different types of cancers. Gene selection is usually required as a preliminary step for a cancer classification problem. This step aims to select the most informative genes among a great number of genes, which represent an important issue. Although many studies have been proposed to address this issue, they lack getting the most informative and fewest number of genes with the highest accuracy and little effort from the high dimensionality of microarray datasets. Manta ray foraging optimization(MRFO) algorithm is a new meta-heuristic algorithm that mimics the nature of manta ray fishes in food foraging. MRFO has achieved promising results in other fields, such as solar generating units. Due to the high accuracy results of the support vector machines (SVM), it is the most commonly used classification algorithm in cancer studies, especially with microarray data. For exploiting the pros of both algorithms (i.e., MRFO and SVM), in this paper, a hybrid algorithm is proposed to select the most predictive and informative genes for cancer classification. A binary microarray dataset, which includes colon and leukemia1, and a multi-class microarray dataset that includes SRBCT, lymphoma, and leukemia2, are used to evaluate the accuracy of the proposed technique. Like other optimization techniques, MRFO suffers from some problems related to the high dimensionality and complexity of the microarray data. For solving such problems as well as improving the performance, the minimum redundancy maximum relevance (mRMR) method is used as a preprocessing stage. The proposed technique has been evaluated compared to the most common cancer classification algorithms. The experimental results show that our proposed technique achieves the highest accuracy with the fewest number of informative genes and little effort.

The location problem of Bundling Hubs (BHs) remains a contentious issue for efficient shared transportation systems. In this respect, the strategic configuration of BHs plays a crucial role in saving supply costs, covering demand, and minimizing the external effects of Shared Passenger and Freight Public Transportation (SPFPT). As urban areas become crowded, they show a significant increase in congestion and transport demand. Thus, sites where logistic operations, sales, or services are likely to occur, imply the final customers whose transport demand is a key factor that could affect cargo distribution using SPFPT systems. Since each BH should help efficiently to satisfy the transport demand of allocated customers, they would not play their key role if such factor of demand is not involved upstream in the long-term scheduling horizon. This paper focuses on locating BHs, using a Hybrid Robust Machine Learning-Heuristic Algorithm (HR-MLHA), among established ones and existing demand nodes while assessing a dynamic process so that the configured BH network is robust. The feature of robustness is supported by a robust command to keep BHs attractive and demand-responsive for the long-term in dynamic environments, i.e., the cities. To reduce complexity, the conceptual and computational approaches are structured in two main axes. The first axis includes a machine-learning-based zoning approach that helps with targeting the implementation area and assessing demand behavior. The second axis presents a mathematical model of the capacitated two-echelon BH location problem. When looking across the two-echelon location process, we aim at conducting dynamic location analysis using both current and predicted demand. In order to validate our approach, a set of benchmarks has been performed comparing with existing heuristics and using a whole package of experimental and real-life instances. The experimental results provided through the proposed approach have allowed valuable insights into successfully implementing our methodology.

Metaheuristics is a powerful optimization tool that is increasingly used across disciplines to tackle general purpose optimization problems. Nature‐inspired metaheuristic algorithms is a subclass of metaheuristic algorithms and have been shown to be particularly flexible and useful in solving complicated optimization problems in computer science and engineering. A common practice with metaheuristics is to hybridize it with another suitably chosen algorithm for enhanced performance. This paper reviews metaheuristic algorithms and demonstrates some of its utility in tackling pharmacometric problems. Specifically, we provide three applications using one of its most celebrated members, particle swarm optimization (PSO), and show that PSO can effectively estimate parameters in complicated nonlinear mixed‐effects models and to gain insights into statistical identifiability issues in a complex compartment model. In the third application, we demonstrate how to hybridize PSO with sparse grid, which is an often‐used technique to evaluate high dimensional integrals, to search for D‐efficient designs for estimating parameters in nonlinear mixed‐effects models with a count outcome. We also show the proposed hybrid algorithm outperforms its competitors when sparse grid is replaced by its competitor, adaptive gaussian quadrature to approximate the integral, or when PSO is replaced by three notable nature‐inspired metaheuristic algorithms.

Highest density regions refer to level sets containing points of relatively high density. Their estimation from a random sample, generated from the underlying density, allows to determine the clusters of the corresponding distribution. This task can be accomplished considering different nonparametric perspectives. From a practical point of view, reconstructing highest density regions can be interpreted as a way of determining hot-spots, a crucial task for understanding COVID-19 space-time evolution. In this work, we compare the behaviour of classical plug-in methods and a recently proposed hybrid algorithm for highest density regions estimation through an extensive simulation study. Both methodologies are applied to analyse a real data set about COVID-19 cases in the United States. [ABSTRACT FROM AUTHOR] Copyright of Journal of Nonparametric Statistics is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With renewable energy currently making the headlines, photovoltaic technology has shown significant potential as one of the best energy sources. It thus becomes necessary to predict the performance of photovoltaic systems by modeling it accurately and optimally. We propose a new hybrid algorithm for extracting PV cell parameters to improve their performance and efficiency when subjected to temperature variations. A metaheuristic approach is combined with an analytical approach to improve the accuracy and robustness. We call this approach improved differential evolution (IDE). The performance of the proposed method is evaluated for selected cell data. For validation, several analyses and comparisons are made with other methods, and the results illustrate the accuracy and precision of IDE. The proposed technique can estimate the parameters in an optimal way at any temperature, together with high convergence speed and a short simulation time.