Search

This paper introduces an innovative approach to address the distribution network reconfiguration (DNR) challenge, aiming to reduce power loss through an advanced hyperbolic tangent particle swarm optimization (HT-PSO) method. This approach is distinguished by the adoption of a novel hyperbolic tangent function, which effectively limits the rate of change values, offering a significant improvement over traditional sigmoid function-based methods. A key feature of this new approach is the integration of a tunable parameter, δ , into the HT-PSO, enhancing the curve's adaptability. The careful optimization of δ ensures superior control over the rate of change across the entire operational range. This enhanced control mechanism substantially improves the efficiency of the search and convergence processes in DNR. Comparative simulations conducted on 33- and 94-bus systems show an improvement in convergence, demonstrating a more exhaustive exploration of the search space than existing methods documented in the literature based on PSO and variations where functions are proposed for the rate of change of values. [ABSTRACT FROM AUTHOR]

Metaheuristic optimization algorithms (MHA) play a significant role in obtaining the best (optimal) values of the system's parameters to improve its performance. This role is significantly apparent when dealing with systems where the classical analytical methods fail. Fractional-order (FO) systems have not yet shown an easy procedure to deal with the determination of their optimal parameters through traditional methods. In this paper, a recent, systematic. And comprehensive review is presented to highlight the role of MHA in obtaining the best set of gains and orders for FO controllers. The systematic review starts by exploring the most relevant publications related to the MHA and the FO controllers. The study is focused on the most popular controllers such as the FO-PI, FO-PID, FO Type-1 fuzzy-PID, and FO Type-2 fuzzy-PID. The time domain is restricted in the articles published through the last decade (2014:2023) in the most reputed databases such as Scopus, Web of Science, Science Direct, and Google Scholar. The identified number of papers, from the entire databases, has reached 850 articles. A Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology was applied to the initial set of articles to be screened and filtered to end up with a final list that contains 82 articles. Then, a thorough and comprehensive study was applied to the final list. The results showed that Particle Swarm Optimization (PSO) is the most attractive optimizer to the researchers to be used in the optimal parameters identification of the FO controllers as it attains about 25% of the published papers. In addition, the papers that used PSO as an optimizer have gained a high citation number despite the fact that the Chaotic Atom Search Optimization (ChASO) is the highest one, but it is used only once. Furthermore, the Integral of the Time-Weighted Absolute Error (ITAE) is the best nominated cost function. Based on our comprehensive literature review, this appears to be the first review paper that systematically and comprehensively addresses the optimization of the parameters of the fractional-order PI, PID, Type-1, and Type-2 fuzzy controllers with the use of MHAs. Therefore, the work in this paper can be used as a guide for researchers who are interested in working in this field. [ABSTRACT FROM AUTHOR]

Inspired by the migration and reproduction of species in nature to explore suitable habitats, this paper proposed a new swarm intelligence algorithm called the Migration and Reproduction Algorithm (MARA). This new algorithm discusses how to transform the behavior of an organism looking for a suitable habitat into a mathematical model, which can solve optimization problems. MARA has some common features with other optimization methods such as particle swarm optimization (PSO) and the fireworks algorithm (FWA), which means MARA can also solve the optimization problems that PSO and FWA are used to, namely, high-dimensional optimization problems. MARA also has some unique features among biology-based optimization methods. In this paper, we articulated the structure of MARA by correlating it with natural biogeography; then, we demonstrated the performance of MARA on sets of 12 benchmark functions. In the end, we applied it to optimize a practical problem of power dispatching in a multi-microgrid system that proved it has certain value in practical applications. [ABSTRACT FROM AUTHOR]

Spam emails have become a pervasive issue in recent years, as internet users receive increasing amounts of unwanted or fake emails. To combat this issue, automatic spam detection methods have been proposed, which aim to classify emails into spam and non-spam categories. Machine learning techniques have been utilized for this task with considerable success. In this paper, we introduce a novel approach to spam email detection by presenting significant advancements to the Dandelion Optimizer (DO) algorithm. The DO is a relatively new nature-inspired optimization algorithm inspired by the flight of dandelion seeds. While the DO shows promise, it faces challenges, especially in high-dimensional problems such as feature selection for spam detection. Our primary contributions focus on enhancing the DO algorithm. Firstly, we introduce a new local search algorithm based on flipping (LSAF), designed to improve the DO's ability to find the best solutions. Secondly, we propose a reduction equation that streamlines the population size during algorithm execution, reducing computational complexity. To showcase the effectiveness of our modified DO algorithm, which we refer to as the Improved DO (IDO), we conduct a comprehensive evaluation using the Spam base dataset from the UCI repository. However, we emphasize that our primary objective is to advance the DO algorithm, with spam email detection serving as a case study application. Comparative analysis against several popular algorithms, including Particle Swarm Optimization (PSO), the Genetic Algorithm (GA), Generalized Normal Distribution Optimization (GNDO), the Chimp Optimization Algorithm (ChOA), the Grasshopper Optimization Algorithm (GOA), Ant Lion Optimizer (ALO), and the Dragonfly Algorithm (DA), demonstrates the superior performance of our proposed IDO algorithm. It excels in accuracy, fitness, and the number of selected features, among other metrics. Our results clearly indicate that the IDO overcomes the local optima problem commonly associated with the standard DO algorithm, owing to the incorporation of LSAF and the reduction in equation methods. In summary, our paper underscores the significant advancement made in the form of the IDO algorithm, which represents a promising approach for solving high-dimensional optimization problems, with a keen focus on practical applications in real-world systems. While we employ spam email detection as a case study, our primary contribution lies in the improved DO algorithm, which is efficient, accurate, and outperforms several state-of-the-art algorithms in various metrics. This work opens avenues for enhancing optimization techniques and their applications in machine learning. [ABSTRACT FROM AUTHOR]

Deepfakes are digital audio, video, or images manipulated using machine learning algorithms. These manipulated media files can convincingly depict individuals doing or saying things they never actually did. Deepfakes pose significant risks to our lives, including national security, financial markets, and personal privacy. The ability to create convincing deep fakes can also harm individuals' reputations and can be used to spread disinformation and fake news. As such, there is a growing need for reliable and accurate methods to detect deep fakes and prevent their harmful effects. In this paper, a hybrid convolutional neural network (CNN) and recurrent neural network (RNN) with a particle swarm optimization (PSO) algorithm is utilized to demonstrate a deep learning strategy for detecting deepfake videos. High accuracy, sensitivity, specificity, and F1 score were attained by the proposed approach when tested on two publicly available datasets: Celeb-DF and the Deepfake Detection Challenge Dataset (DFDC). Specifically, the proposed method achieved an average accuracy of 97.26% on Celeb-DF and an average accuracy of 94.2% on DFDC. The results were compared to other state-of-the-art methods and showed that the proposed method outperformed many. The proposed method can effectively detect deepfake videos, which is essential for identifying and preventing the spread of manipulated content online. [ABSTRACT FROM AUTHOR]

This paper presents the minimization of total harmonic distortion in a seven-level cascaded H-bridge multilevel inverter with resistive load using the teaching–learning-based optimization algorithm. The minimization of Total Harmonic Distortion (THD)is a challenging optimization problem due to the fact that nonlinear equations are involved. Recently, bio-inspired algorithms have become very popular approaches to solving various optimization problems in different areas of engineering. For this reason, the results obtained with the Teaching–Learning-Based Optimization (TLBO)algorithm were compared with three other popular bio-inspired algorithms, the genetic algorithm, differential evolution, and particle swarm optimization. The comparative analysis, conducted by sweeping the modulation index, made it possible to obtain graphs and data on the behavior of the four analyzed algorithms. Finally, it was concluded that the TLBO algorithm is very effective and is able to solve the THD-minimization problem. Its main advantage over the other algorithms is the fact that it does not require control parameters for its correct operation in the solution of the problem. [ABSTRACT FROM AUTHOR]

This paper aims to optimize the quality characteristics of the drilling process in glass fiber-reinforced polymer (GFRP) composites. It focuses on optimizing the drilling parameters with drill point angles concerning delamination damage and energy consumption, simultaneously. The effects of drilling process parameters on machinability were analyzed by evaluating the machinability characteristics. The cutting power was modeled through drilling parameters (speed and feed), drill point angle, and laminate thickness. The response surface analysis and artificial neural networks enhanced by the particle swarm optimization algorithm were applied for modeling and evaluating the effect of process parameters on the machinability of the drilling process. The most influential parameters on machinability properties and delamination were determined by analysis of variance (ANOVA). A multi-response optimization was performed to optimize drilling process parameters for sustainable drilling quality characteristics. The obtained models were applied to predict drilling process characteristics, and exhibited an excellent harmony with the experiment results. The optimal drilling process factors were the highest spindle speed and the lowest feed, with a drill point angle of 118° for the laminate of 4.75 mm thickness. [ABSTRACT FROM AUTHOR]

The optimization of industrial processes is a critical task for leveraging profitability and sustainability. To ensure the selection of optimum process parameter levels in any industrial process, numerous metaheuristic algorithms have been proposed so far. However, many algorithms are either computationally too expensive or become trapped in the pit of local optima. To counter these challenges, in this paper, a hybrid metaheuristic called PSO-GSA is employed that works by combining the iterative improvement capability of particle swarm optimization (PSO) and gravitational search algorithm (GSA). A binary PSO is also fused with GSA to develop a BPSO-GSA algorithm. Both the hybrid algorithms i.e., PSO-GSA and BPSO-GSA, are compared against traditional algorithms, such as tabu search (TS), genetic algorithm (GA), differential evolution (DE), GSA and PSO algorithms. Moreover, another popular hybrid algorithm DE-GA is also used for comparison. Since earlier works have already studied the performance of these algorithms on mathematical benchmark functions, in this paper, two real-world-applicable independent case studies on biodiesel production are considered. Based on the extensive comparisons, significantly better solutions are observed in the PSO-GSA algorithm as compared to the traditional algorithms. The outcomes of this work will be beneficial to similar studies that rely on polynomial models. [ABSTRACT FROM AUTHOR]

In this paper, a structural design and controller optimization process for a five-bar planar manipulator are studied using three different population-based optimization techniques: particle swarm optimization, genetic algorithm, and differential evolution. First, the desired kinematic properties of the manipulator, such as the position, velocity, and acceleration of the endpoint, are determined using inverse kinematics. Then, an optimization problem is created to minimize the shaking force and moments, and the desired kinematic quantities are implemented as constraints. All the link properties of the manipulator are defined as design variables, and the optimization results are obtained. The results show that it is possible to significantly reduce the shaking force and moment significantly thanks to the optimal design parameters. Finally, the controller is optimized to find the best PID gains considering the forward kinematics of the manipulator. It is observed that the shaking force and shaking moment can be reduced by 99% and 54%, respectively, which has a very positive effect on the accuracy of the trajectory tracking. Moreover, the performances of the optimization methods are compared by using the same number of iterations in the calculations, and thus, it can be seen that the GA method achieves the best results compared to the others. Therefore, the results of this study are of utmost importance for a manufacturer, who wants to design a five-bar planar manipulator and its controller. [ABSTRACT FROM AUTHOR]

Hydraulic pulsation attenuators (HPA) are commonly used to suppress the noise and vibration in fluid power systems. However, most existing HPAs lack a reasonable optimization strategy and effective methods to improve the performance of HPAs. This paper proposes an adaptive particle swarm optimization (APSO) algorithm to speed up the geometry optimization process of a resonant string-based compound HPA (RSHPA), which was proposed in our previous work. Then, the study discusses the possibility of improving the performance of RSHPA by varying the configuration and location of the RSHPA. The experiment result validates the feasibility of the proposed optimization method for RSHPA. [ABSTRACT FROM AUTHOR]

The coefficients of friction (COFs) is one of the most important parameters used to evaluate the braking performance of a friction brake. Many indicators that affect the safety and comfort of automobiles are associated with brake COFs. The manufacturers of friction brakes and their components are required to spend huge amounts of time and money to carry out experimental tests to ensure the COFs of a newly developed braking system meet the required standards. In order to save time and costs for the development of new friction brake applications, the GRU (Gate Recurrent Unit) algorithm optimized by the improved PSO (particle swarm optimization) global optimization method is employed in this work to predict brake COFs based on existing experimental data obtained from friction braking dynamometer tests. Compared with the LSTM (Long Short-Term Memory) method, the GRU algorithm optimized by PSO avoids the accuracy reduction problem caused by gradient descent in the training process and hence reduces the prediction error and computational cost. The combined PSO–GRU algorithm increases the coefficient of determination (R2) of the prediction by 4.7%, reduces the MAE (mean absolute error) by 14.3%, and increases the prediction speed by 40.1% compared with the standalone GRU method. The prediction method based on machine learning proposed in this study can not only be applied to the prediction of automobile braking COFs but also for other frictional system problems, such as the prediction of braking noise and the friction of various bearing transmission components. [ABSTRACT FROM AUTHOR]

In the optimization field, the ability to efficiently tackle complex and high-dimensional problems remains a persistent challenge. Metaheuristic algorithms, with a particular emphasis on their autonomous variants, are emerging as promising tools to overcome this challenge. The term "autonomous" refers to these variants' ability to dynamically adjust certain parameters based on their own outcomes, without external intervention. The objective is to leverage the advantages and characteristics of an unsupervised machine learning clustering technique to configure the population parameter with autonomous behavior, and emphasize how we incorporate the characteristics of search space clustering to enhance the intensification and diversification of the metaheuristic. This allows dynamic adjustments based on its own outcomes, whether by increasing or decreasing the population in response to the need for diversification or intensification of solutions. In this manner, it aims to imbue the metaheuristic with features for a broader search of solutions that can yield superior results. This study provides an in-depth examination of autonomous metaheuristic algorithms, including Autonomous Particle Swarm Optimization, Autonomous Cuckoo Search Algorithm, and Autonomous Bat Algorithm. We submit these algorithms to a thorough evaluation against their original counterparts using high-density functions from the well-known CEC LSGO benchmark suite. Quantitative results revealed performance enhancements in the autonomous versions, with Autonomous Particle Swarm Optimization consistently outperforming its peers in achieving optimal minimum values. Autonomous Cuckoo Search Algorithm and Autonomous Bat Algorithm also demonstrated noteworthy advancements over their traditional counterparts. A salient feature of these algorithms is the continuous nature of their population, which significantly bolsters their capability to navigate complex and high-dimensional search spaces. However, like all methodologies, there were challenges in ensuring consistent performance across all test scenarios. The intrinsic adaptability and autonomous decision making embedded within these algorithms herald a new era of optimization tools suited for complex real-world challenges. In sum, this research accentuates the potential of autonomous metaheuristics in the optimization arena, laying the groundwork for their expanded application across diverse challenges and domains. We recommend further explorations and adaptations of these autonomous algorithms to fully harness their potential. [ABSTRACT FROM AUTHOR]

Swarm intelligence is a discipline which makes use of a number of agents for solving optimization problems by producing low cost, fast and robust solutions. The dragonfly algorithm (DA), a recently proposed swarm intelligence algorithm, is inspired by the dynamic and static swarming behaviors of dragonflies, and it has been found to have a higher performance in comparison to other swarm intelligence and evolutionary algorithms in numerous applications. There are only a few surveys about the dragonfly algorithm, and we have found that they are limited in certain aspects. Hence, in this paper, we present a more comprehensive survey about DA, its applications in various domains, and its performance as compared to other swarm intelligence algorithms. We also analyze the hybrids of DA, the methods they employ to enhance the original DA, their performance as compared to the original DA, and their limitations. Moreover, we categorize the hybrids of DA according to the type of problem that they have been applied to, their objectives, and the methods that they utilize. [ABSTRACT FROM AUTHOR]

In order to attain high manufacturing productivity, industry 4.0 merges all the available system and environment data that can empower the enabled-intelligent techniques. The use of data provokes the manufacturing self-awareness, reconfiguring the traditional manufacturing challenges. The current piece of research renders attention to new consideration in the Job Shop Scheduling (JSSP) based problems as a case study. In that field, a great number of previous research papers provided optimization solutions for JSSP, relying on heuristics based algorithms. The current study investigates the main elements of such algorithms to provide a concise anatomy and a review on the previous research papers. Going through the study, a new optimization scope is introduced relying on additional available data of a machine, by which the Flexible Job-Shop Scheduling Problem (FJSP) is converted to a dynamic machine state assignation problem. Deploying two-stages, the study utilizes a combination of discrete Particle Swarm Optimization (PSO) and a selection based algorithm followed by a modified local search algorithm to attain an optimized case solution. The selection based algorithm is imported to beat the ever-growing randomness combined with the increasing number of data-types. [ABSTRACT FROM AUTHOR]

This paper proposes a hybrid Rao-Nelder–Mead (Rao-NM) algorithm for image template matching is proposed. The developed algorithm incorporates the Rao-1 algorithm and NM algorithm serially. Thus, the powerful global search capability of the Rao-1 algorithm and local search capability of NM algorithm is fully exploited. It can quickly and accurately search for the high-quality optimal solution on the basis of ensuring global convergence. The computing time is highly reduced, while the matching accuracy is significantly improved. Four commonly applied optimization problems and three image datasets are employed to assess the performance of the proposed method. Meanwhile, three commonly used algorithms, including generic Rao-1 algorithm, particle swarm optimization (PSO), genetic algorithm (GA), are considered as benchmarking algorithms. The experiment results demonstrate that the proposed method is effective and efficient in solving image matching problems. [ABSTRACT FROM AUTHOR]

Localization is one of the essential problems in internet of things (IoT) and wireless sensor network (WSN) applications. However, most traditional range-free localization algorithms cannot fulfill the practical demand for high localization accuracy. Therefore, a localization algorithm based on an enhanced flower pollination algorithm (FPA) with Gaussian perturbation (EFPA-G) and the DV-Hop method is proposed.FPA is widely applied, but premature convergence still cannot be avoided. How to balance its global exploration and local exploitation capabilities still remains an outstanding problem. Therefore, the following improvement schemes are introduced. A search strategy based on Gaussian perturbation is proposed to solve the imbalance between the global exploration and local exploitation search capabilities. Meanwhile, to fully exploit the variability of population information, an enhanced strategy is proposed based on optimal individual and Lévy flight. Finally, in the experiments with 26 benchmark functions and WSN simulations, the former verifies that the proposed algorithm outperforms other state-of-the-art algorithms in terms of convergence and search capability. In the simulation experiment, the best value for the normalized mean squared error obtained by the most advanced algorithm, RACS, is 20.2650%, and the best value for the mean distance error is 5.07E+00. However, EFPA-G reached 19.5182% and 4.88E+00, respectively. It is superior to existing algorithms in terms of positioning, accuracy, and robustness. [ABSTRACT FROM AUTHOR]

This study investigates the ability of a new hybrid neuro-fuzzy model by combining the neuro-fuzzy (ANFIS) approach with the marine predators' algorithm (MPA) in predicting short-term (from 1 h ahead to 1 day ahead) significant wave heights. Data from two stations, Cairns and Palm Beach buoy, were used in assessing the considered methods. The ANFIS-MPA was compared with two other hybrid methods, ANFIS with genetic algorithm (ANFIS-GA) and ANFIS with particle swarm optimization (ANFIS-PSO), in predicting significant wave height for multiple lead times ranging from 1 h to 1 day. The multivariate adaptive regression spline was investigated in deciding the best input for prediction models. The ANFIS-MPA model generally offered better accuracy than the other hybrid models in predicting significant wave height in both stations. It improved the accuracy of ANFIS-PSO and ANFIS-GA by 8.3% and 11.2% in root mean square errors in predicting a 1 h lead time in the test period. [ABSTRACT FROM AUTHOR]

In this paper, the optimal dynamic scheduling of electric vehicles (EVs) in a parking lot (PL) is proposed to minimize the charging cost. In static scheduling, the PL operator can make the optimal scheduling if the demand, arrival, and departure time of EVs are known well in advance. If not, a static charging scheme is not feasible. Therefore, dynamic charging is preferred. A dynamic scheduling scheme means the EVs may come and go at any time, i.e., EVs' arrival is dynamic in nature. The EVs may come to the PL with prior appointments or not. Therefore, a PL operator requires a mechanism to charge the EVs that arrive with or without reservation, and the demand for EVs is unknown to the PL operator. In general, the PL uses the first-in-first serve (FIFS) method for charging the EVs. The well-known optimization techniques such as particle swarm optimization and shuffled frog leaping algorithms are used for the EVs' dynamic scheduling scheme to minimize the grid's charging cost. Moreover, a microgrid is also considered to reduce the charging cost further. The results obtained show the effectiveness of the proposed solution methods. [ABSTRACT FROM AUTHOR]

Agent-based algorithms, based on the collective behavior of natural social groups, exploit innate swarm intelligence to produce metaheuristic methodologies to explore optimal solutions for diverse processes in systems engineering and other sciences. Especially for complex problems, the processing time, and the chance to achieve a local optimal solution, are drawbacks of these algorithms, and to date, none has proved its superiority. In this paper, an improved swarm optimization technique, named Grand Tour Algorithm (GTA), based on the behavior of a peloton of cyclists, which embodies relevant physical concepts, is introduced and applied to fourteen benchmarking optimization problems to evaluate its performance in comparison to four other popular classical optimization metaheuristic algorithms. These problems are tackled initially, for comparison purposes, with 1000 variables. Then, they are confronted with up to 20,000 variables, a really large number, inspired in the human genome. The obtained results show that GTA clearly outperforms the other algorithms. To strengthen GTA's value, various sensitivity analyses are performed to verify the minimal influence of the initial parameters on efficiency. It is demonstrated that the GTA fulfils the fundamental requirements of an optimization algorithm such as ease of implementation, speed of convergence, and reliability. Since optimization permeates modeling and simulation, we finally propose that GTA will be appealing for the agent-based community, and of great help for a wide variety of agent-based applications. [ABSTRACT FROM AUTHOR]

Distributed, generation-based micro-grids are increasingly being used in the build-up of the modern power system. However, the protection of these micro-grids has many challenges. One of the important challenges is the coordination of directional overcurrent (DOC) relays. The optimization of the coordination of DOC relays is considered a nonlinear programming problem with pre-defined constrains. In this paper, the problem of the optimal coordination of DOC relays is solved using a multi-verse optimization (MVO) algorithm which is inspired from cosmology science. The proposed algorithm is tested by applying it to Institute of Electrical and Electronics Engineers (IEEE) 3 bus and IEEE 9 bus networks. The performance of the proposed algorithm is compared with the particle swarm optimization (PSO) algorithm when applied to both networks. All results show that the performance of the MVO algorithm is better than PSO in terms of its reduction of both the overall operating time (OT) of DOC relays and the computational burden of the computer solving the optimization problem. [ABSTRACT FROM AUTHOR]

This work presents an aerodynamic and structural optimization for a Droop Nose Leading Edge Morphing airfoil as a high lift device for the UAS-S45. The results were obtained using three optimization algorithms: coupled Particle Swarm Optimization-Pattern Search, Genetic Algorithm, and Black Widow Optimization algorithm. The lift-to-drag ratio was used as the fitness function, and the impact of the choice of optimization algorithm selection on the fitness function was evaluated. The optimization was carried out at various Mach numbers of 0.08, 0.1, and 0.15, respectively, and at the cruise and take-off flight conditions. All these optimization algorithms obtained effectively comparable lift-to-drag ratio results with differences of less than 0.03% and similar airfoil geometries and pressure distributions. In addition, an unsteady analysis of a Variable Morphing Leading Edge airfoil with a dynamic meshing scheme was carried out to study its flow behaviour at different angles of attack and the feasibility of leading-edge downward deflection as a stall control mechanism. The numerical results showed that the variable morphing leading edge reduces the flow separation areas over an airfoil and increases the stall angle of attack. Furthermore, a preliminary investigation was conducted into the design and sensitivity analysis of a morphing leading-edge structure of the UAS-S45 wing integrated with an internal actuation mechanism. The correlation and determination matrices were computed for the composite wing geometry for sensitivity analysis to obtain the parameters with the highest correlation coefficients. The parameters include the composite material qualities, thickness, ply angles, and the ply stacking sequence. These findings can be utilized to design the flexible skin optimization framework, obtain the target droop nose deflections for the morphing leading edge, and design an improved model. [ABSTRACT FROM AUTHOR]

Despite the vast benefits of integrating renewable energy sources (RES) with the utility grid, they pose stability and power quality problems when interconnected with the existing power system. This is due to the production of high voltages and current overshoots/undershoots during their injection or disconnection into/from the power system. In addition, the high harmonic distortion in the output voltage and current waveforms may also be observed due to the excessive inverter switching frequencies used for controlling distributed generator's (DG) power output. Hence, the development of a robust and intelligent controller for the grid-connected microgrid (MG) is the need of the hour. As such, this paper aims to develop a robust and intelligent optimal power flow controller using a grasshopper optimization algorithm (GOA) to optimize the dynamic response and power quality of the grid-connected MG while sharing the desired amount of power with the grid. To validate the effectiveness of proposed GOA-based controller, its performance in achieving the desired power sharing ratio with optimal dynamic response and power quality is compared with that of its precedent particle swarm optimization (PSO)-based controller under MG injection and abrupt load change conditions. The proposed controller provides tremendous system's dynamic response with minimum current harmonic distortion even at higher DG penetration levels. [ABSTRACT FROM AUTHOR]

At present, optimization algorithms are used extensively. One particular type of such algorithms includes random-based heuristic population optimization algorithms, which may be created by modeling scientific phenomena, like, for example, physical processes. The present article proposes a novel optimization algorithm based on Hooke's law, called the spring search algorithm (SSA), which aims to solve single-objective constrained optimization problems. In the SSA, search agents are weights joined through springs, which, as Hooke's law states, possess a force that corresponds to its length. The mathematics behind the algorithm are presented in the text. In order to test its functionality, it is executed on 38 established benchmark test functions and weighed against eight other optimization algorithms: a genetic algorithm (GA), a gravitational search algorithm (GSA), a grasshopper optimization algorithm (GOA), particle swarm optimization (PSO), teaching–learning-based optimization (TLBO), a grey wolf optimizer (GWO), a spotted hyena optimizer (SHO), as well as an emperor penguin optimizer (EPO). To test the SSA's usability, it is employed on five engineering optimization problems. The SSA delivered better fitting results than the other algorithms in unimodal objective function, multimodal objective functions, CEC 2015, in addition to the optimization problems in engineering. [ABSTRACT FROM AUTHOR]

Nowadays, many organizations and individual users are employing cloud services extensively due to their efficiency, reliability and low cost. A key aspect for cloud data centers is to achieve management methods to reduce energy consumption, increasing the profit and reducing the environmental impact, which is critical in the deployment of leading-edge technologies today such as blockchain and digital finances, IoT, online gaming and video streaming. In this review, various clustering, optimization, and machine learning methods used in cloud resource allocation to increase the energy efficiency and performance are analyzed, compared and classified. Specifically, on the one hand, we discuss how clustering methods and optimization techniques are widely applied in energy management due to their capacity to provide solutions for energy consumption reduction. On the other hand, we study how multi-objective optimization methods focus on reducing energy consumption as well as service level agreement (SLA) violation, and improving quality of services (QoS) simultaneously. Also, we discuss how optimization methods such as the firefly algorithm, whale optimization algorithm (WOA), particle swarm optimization (PSO) and genetic algorithm (GA) provide the highest performance in the field. Moreover, we analyze how machine learning methods such as deep neural network (DNN), random forest, and support vector machine (SVM) are applied to the prediction of energy consumption in the cloud, showing an accurate performance in this prediction. Nevertheless, we study how the existing methods still have limitations of low convergence, trap into local optima and overfitting. [ABSTRACT FROM AUTHOR]

This work deals with metaheuristic optimization algorithms to derive the best parameters for the Gaussian Adaptive PID controller. This controller represents a multimodal problem, where several distinct solutions can achieve similar best performances, and metaheuristics optimization algorithms can behave differently during the optimization process. Finding the correct proportionality between the parameters is an arduous task that often does not have an algebraic solution. The Gaussian functions of each control action have three parameters, resulting in a total of nine parameters to be defined. In this work, we investigate three bio-inspired optimization methods dealing with this problem: Particle Swarm Optimization (PSO), the Artificial Bee Colony (ABC) algorithm, and the Whale Optimization Algorithm (WOA). The computational results considering the Buck converter with a resistive and a nonlinear load as a case study demonstrated that the methods were capable of solving the task. The results are presented and compared, and PSO achieved the best results. [ABSTRACT FROM AUTHOR]

A modified electromagnetism-like mechanism (EM) algorithm is proposed to identify structural model parameters using modal data. EM is a heuristic algorithm, which utilizes an attraction–repulsion mechanism to move the sample points towards the optimal solution. In order to improve the performance of original algorithm, a new local search strategy, new charge and force calculation formulas, new particle movement and updating rules are proposed. The test results of benchmark functions show that the modified EM algorithm has better accuracy and faster convergence rate than the original EM algorithm and the particle swarm optimization (PSO) algorithm. In order to investigate the applicability of this approach in parameter identification of structural models, one numerical truss model and one experimental shear-building model are presented as illustrative examples. The identification results show that this approach can achieve remarkable parameter identification even in the case of large noise contamination and few measurements. The modified EM algorithm can also be used to solve other optimization problems. [ABSTRACT FROM AUTHOR]

Remote sensing with unmanned aerial vehicles (UAVs) facilitates photogrammetry for environmental and infrastructural monitoring. Models are created with less computational cost by reducing the number of photos required. Optimal camera locations for reducing the number of photos needed for structure-from-motion (SfM) are determined through eight mathematical set-covering algorithms as constrained by solve time. The algorithms examined are: traditional greedy, reverse greedy, carousel greedy (CG), linear programming, particle swarm optimization, simulated annealing, genetic, and ant colony optimization. Coverage and solve time are investigated for these algorithms. CG is the best method for choosing optimal camera locations as it balances number of photos required and time required to calculate camera positions as shown through an analysis similar to a Pareto Front. CG obtains a statistically significant 3.2 fewer cameras per modeled area than base greedy algorithm while requiring just one additional order of magnitude of solve time. For comparison, linear programming is capable of fewer cameras than base greedy but takes at least three orders of magnitude longer to solve. A grid independence study serves as a sensitivity analysis of the CG algorithms α (iteration number) and β (percentage to be recalculated) parameters that adjust traditional greedy heuristics, and a case study at the Rock Canyon collection dike in Provo, UT, USA, compares the results of all eight algorithms and the uniqueness (in terms of percentage comparisons based on location/angle metadata and qualitative visual comparison) of each selected set. Though this specific study uses SfM, the principles could apply to other instruments such as multi-spectral cameras or aerial LiDAR. [ABSTRACT FROM AUTHOR]

The global production of broiler meat was forecasted to be 97.8 MT in 2019. The cost fluctuations create risks in production. In order to have an effective management system, process uncertainty must be taken into account. This approach considers the process as an interval with fuzzy numbers and, for managing the risks, it uses the variable α, a parameter determined by the manager in an interval between 0 and 1. Then two algorithms, namely the multi-objective imperialist competitive algorithm (MOICA) and multi-objective particle swarm optimization (MOPSO), were compared and applied. Since the process of production has many activities and each activity has possible options, the process does not have a unique solution. Therefore, the objective function and its assigned weights in terms of time, cost, and quality can be applied to select the best solution from those obtained. A vast amount of uncertainty can be observed, and effective management necessitates dealing with these uncertainty issues. The MOPSO algorithm showed better performance than the MOICA algorithm in this problem. Based on fuzzy logic and influenced by the uncertainty condition (α = 0), time, cost, and quality in the MOPSO and the MOICA algorithms were 1793.8 h, $260,571.7, and 46.66%, and 1792.5 h, $260,585.7, and 51.19%, respectively. [ABSTRACT FROM AUTHOR]

The objective of this study is to focus on boric acid extraction from the mineral tincal, in order to determine the optimum conditions thanks to the ultrasonic-assisted extraction (UAE) technique (with the response surface methodology (RSM) for the first time), and artificial intelligence based swarm intelligence. Characterization of the tincal were done by using thermo-gravimetric assay (TG-DTA), X-ray diffraction (XRD), and Fourier transform infrared spectroscopy (FTIR) analyses. In detail, a central composite design (CCD) was used for determining the effects of different solvent/solid ratios, pH, extraction time, and extraction temperature on the yield, which was determined by the conductometric method. The optimum values regarding the best extraction process was calculated by using five different swarm intelligence techniques: Particle swarm optimization (PSO), cuckoo search (CS), genetic algorithms (GA), Differential evolution (DE), and the vortex optimization algorithm (VOA). In the study content, technical details regarding to background and applied experimental processes are given and the findings pointing an approximate 85–92% boron extraction from tincal ore are discussed generally. [ABSTRACT FROM AUTHOR]