Search

The lemur optimizer (LO) is a new metaheuristic algorithm that is inspired by the two primary locomotive patterns of the lemurs. However, LO suffers from improper balance between exploration and exploitation phases and sub-optimal solution stagnation due to poor exploration capability. To address these limitations, an improved lemur optimizer (ILO) with dynamic scaling factors and Gaussian mutation is introduced in this paper. The dynamic scaling factors balance the exploration and exploitation transition by dynamically adjusting their values throughout the optimization process. The proposed algorithm incorporates Gaussian mutation to induce diversity in the population and hence improve the exploration capability. The efficiency of the proposed approach is tested on 33 functions including classical benchmark and CEC-2019 test functions, and a comprehensive comparison is made with six other well-known metaheuristic algorithms. The simulation and statistical results indicate superior performance of ILO in achieving optimal solutions compared to the competing algorithms. The algorithm is further employed to address two constrained classical engineering design problems, demonstrating its robustness. [ABSTRACT FROM AUTHOR]

Traditional deterministic optimization algorithms are difficult to effectively solve many real-world nonlinear, complex, and high-dimensional optimization problems. The metaheuristic optimization algorithm has evolved in recent years into a kind of very popular algorithm due to its gradient-free and random nature. In this paper, a new metaheuristic algorithm, namely the literature research optimization algorithm (LRO), is proposed. In this algorithm, the literature research process is represented as a mathematical model, and new mechanisms are designed to help realize the global exploration and local exploitation. To evaluate the performance of LRO, three sets of test functions including the standard benchmark set, CEC2017, and CEC2019 are applied first, and then, six engineering problems are employed to further verify it. The Friedman test and Wilcoxon rank sum test are also used to statistically compare the proposed LRO algorithm with ten common existing metaheuristics. The results show that the LRO algorithm outperforms the comparison algorithms on most benchmark functions and that it also performs well in practical engineering applications. The LRO algorithm can provide superior solutions to most optimization problems compared to other metaheuristic algorithms and become a promising candidate for many real-life optimization problems. [ABSTRACT FROM AUTHOR]

The DWCS (dynamic wireless charging system) is an innovative electric vehicle charging solution that allows for charging while the vehicle is in motion without requiring direct physical contact. This groundbreaking technology has the capacity to significantly reduce the size of electric vehicle batteries and improve battery life if the DWCS is installed correctly along the route. However, using smaller batteries and improving battery life requires more DWCS stations to be installed along the route. This becomes more challenging due to the heterogeneity of battery vehicles, making finding the optimal compromise between batteries cost and charging infrastructure cost complex since each type has unique characteristics. To address this challenge, we present a quantitative analysis of the benefits of dynamic charging using a mathematical model that considers both batteries and charging infrastructure costs, considering the heterogeneity and life of battery vehicles. Our analysis focuses on identifying how much batteries cost can be reduced to decrease system costs while demonstrating the benefits of dynamic charging on battery life. In the resolution stage, we have created an innovative approach that utilizes multi-agent systems and reinforcement learning, explicitly Q-learning, within the framework of distributed artificial intelligence. [ABSTRACT FROM AUTHOR]

In wireless rechargeable sensor networks (WRSNs), due to data collection and charging, energy efficiency is a crucial factor because it affects the network's reliability and performance. Wireless energy transfer (WET) and wireless data collection (WDC) using separate vehicles are two promising new methods for improving the energy efficiency of sensors. We combine WET and WDC on a single mobile vehicle (MV) to charge and collect data from the sensors by traversing specified sojourn locations within the WRSNs. The objective of this work is to maximize data collection per unit of energy and reduce the number of inactive sensors. In order to accomplish this, we propose a novel scheme, namely DCPCS, that includes two heuristics and two optimization algorithms. We utilized the circle-covering algorithm to determine sojourn locations based on energy consumption rates and the location of sensors. The ant lion optimization (ALO) algorithm partitions the network into the minimum number of regions, assigns a MV to each region, and ensures balanced total energy consumption among the MVs. The ALO algorithm utilizes the first heuristic algorithm to validate the regions for the MVs. Subsequently, the discrete grey wolf optimization algorithm determines the optimal order of the sojourn locations for data collection and charging by a MV within a region. To accomplish our objectives, we propose a heuristic data collection and partial charging strategy to determine the sojourn times of the MVs at the sojourn locations. We conduct simulations and compare the results of the proposed DCPCS scheme with three existing schemes. The simulation outcomes demonstrate that our proposed scheme outperforms the other schemes in terms of various performance metrics. [ABSTRACT FROM AUTHOR]

This study fixates on determining the optimum laser input parameters that simultaneously meet the desired kerf width and depth during CO2 laser cutting of various polymethylmethacrylate (PMMA) sheets. It has three contributions. The first is to model the cutting process of PMMA by polynomial curve fitting as a function of laser power, laser speed, and standoff distance. R-squared (R2), adjusted R2 and root-mean-square error (RMSE) criteria were taken into account to measure the performance of the proposed model. The effect of laser parameters on the process is investigated by analysis of variance (ANOVA) and sensitivity analysis. The second is to optimize the derived nonlinear regression models using genetic algorithm (GA), particle swarm optimization (PSO), whale optimization algorithm (WOA) and ant lion optimization (ALO) metaheuristic methods and compare the performances of the algorithms. The third is to compare the adequacy of the optimization process with the artificial neural network (ANN). The investigations exhibited that the best-fitting polynomials are obtained with the R2 and adjusted R2 values of over 99% and 97%, respectively. ANOVA and sensitivity test revealed that the sensitivity of the laser power, which is the most effective parameter, was 150 at low powers and decreased to 0 as the power value increased. When the nozzle distance is 4.1, the proposed metaheuristics gave effective and sufficiently accurate results. PSO stood out in terms of both best cost value (3.49 × 10–8) and relative error value (0.19%). The relative error of the ANN was found as 3% in terms of kerf depth. [ABSTRACT FROM AUTHOR]

It is known that manually detecting heart conditions is often costly and time-consuming and any study regarding diagnose these conditions has a great importance. In this study, a metaheuristic optimization model has been developed to automate the detection of heart diseases with artificial intelligence compatible methods. In the proposed model, the feature set is selected to represent the best heart sound signals and heart disease diagnoses using machine learning algorithms with these feature sets. The proposed method has been tested on the Pascal dataset which consists of four classes. Firstly, an electronic-based filter model is used as low-pass filter and has great potential to use as a filtering for heart sound signals to decrease noise. Secondly, the statistical and acoustic feature vector extracted from the audio signals in the Pascal dataset is passed through particle swarm optimization (PSO), firefly algorithm (FA) and cuckoo search algorithm (CSA), and the most suitable feature vector is selected. After obtaining the most suitable feature vector with metaheuristic optimization algorithms and filtering method, heart disease diagnosis is performed using random forest (RF), K-nearest neighbor (K-NN), support vector machine (SVM) and Naive Bayes machine learning algorithms. [ABSTRACT FROM AUTHOR]

The implementation of flexible plate structures in industrial settings presents a multitude of benefits, including enhanced efficiency and greater flexibility in comparison with rigid structures. Notwithstanding these advantages, malleable plate structures are more prone to vibrations on account of their inadequate rigidity to endure significant levels of vibrational events. To address this issue, it is imperative to develop an accurate model and an efficient control system. This study aims to develop a model and active vibration control-based advanced firefly algorithm (AFA) for a flexible horizontal plate structure. Initially, input and output vibration data are collected based on the experimental rig designed with a flexible horizontal plate structure. The obtained data are later used to model the system using the system identification method utilizing a metaheuristic algorithm with linear auto-regressive with an exogenous model structure. This study introduces a novel AFA optimization, known as the variant of the firefly algorithm (FA), for the model identification of the flexible horizontal plate structure. Results indicated that the model of the flexible horizontal plate structure developed using AFA is better compared to FA. The smallest mean square error training is achieved from the best model by AFA at 1.1090 × 10−5 compared to FA. Subsequently, the developed model is implemented in a control scheme for vibration attenuation of the flexible horizontal plate structure. The proposed proportional–integral–derivative (PID) controller tuned by AFA works well to stop unwanted vibrations. It works better than the PID-FA controller and other control algorithms, reducing vibrations by up to 98.9%. [ABSTRACT FROM AUTHOR]

Dams are among the most important civil engineering structures, but they are also susceptible to damage that can endanger their stability and functionality. It is therefore crucial to detect any damage in dams to ensure their long-term integrity and overall safety. In light of this, the present study proposes a new two-stage method for identifying damage in concrete gravity dams. In the first stage, a modal curvature-based damage index (MCBDI) is presented to detect the location of suspected damaged zones along the height of the dam. The mode shape data collected from some measurement points on the downstream side of the dam is used to accomplish this task. In the second stage, the pathfinder algorithm (PFA) as a powerful meta-heuristic optimization technique is applied to determine the severity of potentially damaged zones by minimizing an objective function specified in terms of natural frequencies and mode shapes. The capability and effectiveness of the proposed method are evaluated by implementing two numerical simulation examples of concrete gravity dams under both noise-free and noisy conditions. The results obtained suggest that the proposed two-stage method, comprising the MCBDI indicator and the PFA algorithm, represents an accurate and efficient approach for localizing and quantifying damage in concrete gravity dams. [ABSTRACT FROM AUTHOR]

Predicting the drill penetration rate is a fundamental requirement in mining operations, profoundly impacting both the cost-effectiveness of mining activities and strategic mine planning. Given the intricate web of factors influencing rotary drilling performance, the necessity for advanced modeling techniques becomes evident. To this end, the hybrid extreme gradient boosting (XGBoost) was utilized to gauge the penetration rate of rotary drilling machines, utilizing random search, grid search, Harris Hawk optimization (HHO), and the dragonfly algorithm (DA) as metaheuristic algorithms. Our research draws from extensive data collected in copper mine case studies, encompassing both field and investigational data. This dataset incorporates critical material properties, such as tensile strength (TS), uniaxial compressive strength (UCS), as well as vital rock-mass characteristics including joint direction (JD), joint spacing (JS), and bit diameter (D). Our investigation evaluates the reliability of these prediction methods using various performance indicators, including mean absolute error (MAE), root mean square error (RMSE), average absolute relative error (AARE), and coefficient of determination (R2). The multivariate analysis reveals that the HHO-XGB model stands out, demonstrating superior prediction accuracy (MAE: 0.457; RMSE: 2.19; AARE: 2.29; R2: 0.993) compared to alternative models. Furthermore, our sensitivity analysis emphasizes the substantial impact of uniaxial compressive strength and tensile strength on the drill penetration rate. This underlines the importance of considering these material properties in mining operations. In conclusion, our research offers robust models for forecasting the penetration rate of similar rock formations, providing invaluable insights that can significantly enhance mining operations and planning processes. [ABSTRACT FROM AUTHOR]

Structural engineering is a field with several optimization approaches. Concordantly, the goal in optimum design is the generation of the lightest, cheapest, or most sustainable engineering design for any type of structure or structural member. In this study, some optimization approaches were presented for structures including 25 and 72-bar space trusses. To find the smallest weights were ensured for both structures. Optimization approaches related to metaheuristics and modifications of them were developed by inspired by features of nature, abilities of lives and mechanisms of chemical/physical processes. The employed three techniques that are flower pollination algorithm, Jaya algorithm and the hybrid algorithm developed via them were compared to each other through some parameters by calculating different statistical expressions containing the best, average and standard deviation of the objective function. Also, the performance of whole metaheuristic versions is evaluated with some literature researches to find the most effective one to minimize the structural weights. These results have observed that the all versions of proposed metaheuristic algorithms are generally successful, and effective to minimize the objective function comparison to algorithms used in previous studies. Moreover, the best values of objective functions can be decreased at the 0.02–0.06 rates as minimum and maximum compared to the previous studies and algorithms, respectively. [ABSTRACT FROM AUTHOR]

Word Sense Disambiguation (WSD) is a key step for many natural language processing tasks such as information search, automatic translation, and sentiment analysis. WSD is the process that identifies appropriate senses of ambiguous words in the text. With the increasing number of words to be disambiguated in large amount of text data, WSD becomes very challenging and that is why an exhaustive search for the best set of senses may be unpractical. Recently, several metaheuristic approaches have been proposed for different complex optimization problems and have achieved good results. Therefore, in order to improve the WSD process, in this paper, the WSD problem is modeled as a combinatorial optimization problem, and the Discrete Student Psychology-Based Optimization (DSPBO) metaheuristic is proposed and used to selecting appropriate senses. A DSPBO-based WSD is proposed to disambiguate more ambiguous words together in function to their contexts in the target text, and a Lesk-based fitness function is used to guide the DSPBO metaheuristic to optimize the general semantic similarity of selected senses. The proposed approach is evaluated and compared to several recent WSD approaches on the well-known corpuses SensEval-2, SensEval-3, SemEval-2007, SemEval-13, and SemEval-15. The comparison is made in terms of F-Measure, precision, and recall. Experiments show a significant improvement both over existing knowledge lexicon-based approaches and metaheuristic-based approaches, with a higher F-measure of 84.21%, 83.33%, 87.5%, 77.58%, and 81.08% on SensEval-2, SensEval-3, SemEval-2007, SemEval-13, and SemEval-15, respectively. [ABSTRACT FROM AUTHOR]

Due to the complex nature of processing units and the variability in raw oil properties, traditional first-principle methods are not always effective in predicting the output of these units without adapting them. Data-driven models have become popular alternatives because they are more adaptable to the variability in processing units, especially when large amounts of data are available. Feature selection is a crucial aspect of data-driven modeling, especially for processing units. It involves identifying and selecting the most important variables influencing the unit's output. The selected variables are then used as inputs to the model, which helps to reduce noise and improve model accuracy. Accordingly, this article presents a hybrid method that combines feature selection with data-driven modeling to create an accurate model for predicting the output of a Residue Fluid Catalytic Cracking (RFCC) process unit. The Metaheuristic Ant Colony Optimizer (MACO) algorithm was used to extract the best subset of features from the collected data, which were then used with the Artificial Immune Genetic Algorithm with Local Vaccination (AIGA-LV) method to select the optimal features. Finally, the Random Forest (RF) method was used to predict the products of the RFCC process unit. The data were collected from a refinery plant in central Iran for nine months. The proposed model was efficient in feature selection and flexible in handling different scenarios and products. It improved the accuracy to 99.17% at its best and improved the primary model by 3–20%. Therefore, this method could be useful for predicting the output of various industrial processes, especially the RFCC and FCC process units. [ABSTRACT FROM AUTHOR]

This study addresses a permutation flow shop scheduling problem to minimize a linear combination of earliness, tardiness, and assigned due dates. The production environment under study has several real-world applications, such as manufacturing, industrial maintenance, integrated production–distribution systems, potential disruptive environments, and other scenarios in which the due date is defined during sales negotiations with the customer. Four mixed-integer linear programming formulations were proposed and computationally evaluated, and their results were employed to measure the solution quality of the metaheuristics. Due to the complexity of the tackled problem, four coronavirus optimization algorithms based on recent optimization literature were implemented to solve it. A statistical analysis based on ANOVA (analysis of variance) and Tukey tests was performed to evaluate if the difference between the metaheuristics is statistically significant. The computational results demonstrated the superiority of the proposed metaheuristic Covid, especially in large-sized instances, outperforming an algorithm based on an iterated greedy algorithm (IGA) that we implemented for this problem. [ABSTRACT FROM AUTHOR]

A quadcopter is underactuated where there are 6° of motion with only four rotors to control all six motions. Varying the speed of the four rotors can produce thrust, roll, pitch and yaw torque which results in specific movements of the quadcopter. This paper presents the dynamic modeling of a quadcopter, which derived using Newton–Euler formalism and Proportional–integral–derivative (PID) controller for a quadcopter unmanned aerial vehicle. The PID controller is employed in this study due to its simplicity and easy to design. However, it is relatively difficult to determine the optimal tuning gains for PID controller which requires an ample of time with consideration of quadcopter dynamics and nonlinearities. There are several traditional methods for PID gains tuning such as manual tuning and Ziegler–Nichols (ZN-PID) methods, but both are time-wasting with unreliable results specifically for quadcopter system. This work proposes PID gains optimization using Particle Swarm Optimization (PSO) algorithm (PSO–PID) for quadcopter to reduce tuning effort with optimal results. This meta-heuristic algorithm is implemented to provide the optimal PID gains for altitude and attitude stabilization through setup iterations and populations with fixed boundaries. PSO performance is evaluated using several index performances which are IAE, ISE, ITAE and ITSE. The results obtained confirm that the PSO meta-heuristic algorithm works acceptably good with all index performances, especially ITSE, in identifying optimal PID gains for stabilization and trajectory tracking of a quadcopter. It is also proven that PSO–PID controller is better than ZN-PID controller in escape maneuvering of roll motion during wind disturbance occurrence. [ABSTRACT FROM AUTHOR]

In this paper, new methods for increasing the efficiency of photovoltaic pumping systems are presented (PVPS). A feasible implementation of battery-free PVPS, as well as a cost-effective design, has been proposed. The variation of the PV power causes its behaviour to transit permanently between the characteristics of constant current sources and constant voltage sources, which is studied in this paper from a new perspective. The inconsistency of PV generator behaviour reveals an unexpected phenomenon in the operation of induction motors (IM). To overcome the effects of fluctuating PV behaviour on IM operation, a modified fractional order MPPT controller (FO-MPPT) has been proposed. FO-MPPT has improved as a result of the use of several metaheuristics approaches, including the Grey Wolf (GWO), Anti-lion (ALO), and Whale Optimizer (WOA). A comparison of the proposed FO-MPPT and conventional MPPT techniques is carried out. The steady-state power, rise time, and efficiency are used as three measures to demonstrate the reliability of the proposed controller, demonstrating that the FO-MPPT outperforms traditional MPPT techniques. The optimal FO-ability MPPT's to regulate nonlinear and unpredictable dynamic loads is demonstrated. [ABSTRACT FROM AUTHOR]

Solar power generation (SPG) is uncertain and less efficient under unequal shading conditions. It has a highly nonlinear power versus voltage (P–V) curve that has multiple peaks which are categorized as a global maximum power point (GMPP) and local maximum power point (LMPP). It requires a better tracking method that continuously tracks the GMPP instead of the LMPP. Many conventional methods are unable to track the GMPP. The meta-heuristic methods are the better solutions for this problem. However, still there is a need to improve the performance in terms of tracking speed, tracking efficiency and better exploration and exploitation phases, which are the essential parameters in intelligent optimization. This paper proposed a new hybrid meta-heuristic technique, arithmetic-based grey wolf optimization (AGWO), a combination of arithmetic optimization algorithm (AOA) and grey wolf optimization (GWO). The first one is mainly for the exploration phase, and the other is for the exploitation phase; together, the combination enhances the search behaviour of the algorithm. The proposed AGWO was executed in MATLAB/Simulink and evaluated with a real-time simulator to validate its performance. This is compared with other relevant methods like AOA, GWO, particle swarm optimization (PSO) and perturb and observe (P&O) methods. The results indicated that the suggested AGWO technique shows better improvement in tracking efficiency of about 16.41% more than P&O during pattern 1. Also, it shows an excellent tracking speed as compared with PSO is 60.42% raise during pattern 3. [ABSTRACT FROM AUTHOR]

Photovoltaic panels (PVs) are solar panels that turn sunlight into electricity. Tracking the maximum power point (MPP) of PVs is especially important for economic issues. The most popular maximum power point tracking techniques are perturb and observation, hill climbing, constant voltage, parasitic capacitance, and incremental conductance (INC). However, these techniques give oscillated results about the MPP that causes low accuracy, especially in partial shading conditions. This paper is discussing the enhancement of photovoltaic energy system performance using several metaheuristic optimization algorithms. Using MATLAB SIMULINK, a comparative analysis of several algorithms for tracking MPP of PV systems under partially shadowed conditions was conducted. The metaheuristic optimization algorithms that are used in this paper are particle swarm optimization (PSO), cuckoo search algorithm (CSA), grey wolf optimization (GWO), and whale optimization algorithm (WOA). The results show that using WOA and GWO achieved the best efficiency in tracking MPP, whereas, using PSO and CSA achieved lower efficiency in tracking MPP. The MPP of the PV system was not tracked by INC under the partial shaded conditions. [ABSTRACT FROM AUTHOR]

Forecasting Solar Power is an important aspect for power trading companies. It helps in energy bidding, planning, and control. The challenge in forecasting is to predict nonlinear data, which can be fulfilled by the computation technique and machine learning model. ML models have high accuracy for time-series forecasting, but their accuracy is poor for nonlinear forecasting. To enhance the ML model for nonlinear prediction, an optimization algorithm is used for training. This paper presents how the computation technique is incorporated into the machine learning model and compared it with the conventional model. CSA-ANN and SOA-ANN models are developed and forecast solar power for a-day-ahead, three-day-ahead, and a week-ahead solar power generation by considering time, irradiation, and temperature as input parameters for the model. The models are compared with ANN, DE-ANN, and PSO-ANN since these models are widely used. Upon comparison, ANN gives the best result for short-term prediction but is unable to predict midterm and long-term predictions, whereas this problem is overcome by SOA-ANN, which is done by changing its training algorithm, and its performance is measured via statistical parameters such as MAE, MSE, MAPE, and R2. The percentage improvement of SOA-ANN is obtained with these statistical parameters as 6.54%, 16.05%, 1.67%, and 3.61%. Hence, SOA-ANN gives best result as compared to other models. [ABSTRACT FROM AUTHOR]

Marine Predators Algorithm (MPA) is a recent efficient metaheuristic algorithm that is enlightened by the biological behavior of ocean predators and prey. This algorithm simulates the Levy and Brownian movements of prevalent foraging strategy and has been applied to many complex optimization problems. However, the algorithm has defects such as a low diversity of the solutions, ease into the local optimal solutions, and decreasing convergence speed in dealing with complex problems. A modified version of this algorithm called ODMPA is proposed based on the tent map, the outpost mechanism, and the differential evolution mutation with simulated annealing (DE-SA) mechanism. The tent map and DE-SA mechanism are added to enhance the exploration capability of MPA by increasing the diversity of the search agents, and the outpost mechanism is mainly used to improve the convergence speed of MPA. To validate the outstanding performance of the ODMPA, a series of global optimization problems are selected as the test sets, including the standard IEEE CEC2014 benchmark functions, which are the authoritative test set, three well-known engineering problems, and photovoltaic model parameters tasks. Compared with some famous algorithms, the results reveal that ODMPA has achieved better performance than its counterparts in CEC2014 benchmark functions. And in solving real-world optimization problems, ODMPA could get higher accuracy than other metaheuristic algorithms. These practical results demonstrate that the mechanisms introduced positively affect the original MPA, and the proposed ODMPA can be a widely effective tool in tackling many optimization problems. [ABSTRACT FROM AUTHOR]

Semantic segmentation acts as a major role in classifying the remote sensing images into oceanic ice, vegetation, roads, vehicles, houses, and more for offering high precision at the pixel level. In recent studies, convolutional neural network (CNN) has accomplished superior efficiency in the semantic segmentation of images. Even though various deep techniques and architectures have been utilized for enhancing the accuracy, it suffers from classifying the confused classes. Due to the optical conditions and remote sensing information, the sub-decimeter aerial imagery segmentation is challenging while achieving fine-grained semantic segmentation outcomes. The core goal of this task is to adopt the latest Adaptive DeepLabv3 strategy for enhanced semantic segmentation of aerial images. In Adaptive Deeplabv3, the involvement of both the encoder-decoder structure and spatial pyramid pooling module with adaptiveness by a hybrid meta-heuristic algorithm makes faster and stronger segmentation performance within less search space and reduced computation time. The relevant parameters of DeepLabv3 are tuned or optimized by the hybrid meta-heuristic algorithm based on Genetic Inspired Red Deer Algorithm (G-RDA). The enhanced segmentation is employed concerning a fitness function with precision and accuracy. Finally, the experimental analysis of the suggested Adaptive DeepLabv3 strategy for semantic segmentation of aerial images proves its competitive solution when evaluated over conventional approaches. [ABSTRACT FROM AUTHOR]

In this study, a hardware design that runs the PSO algorithm at the micro level is carried out using FPGA technology, which facilitates prototyping and testing of integrated circuit systems. In this way, a high rate of acceleration has been achieved for swarm algorithms that perform stochastic search and whose calculation time is not suitable for real-time systems. For this, the inverse kinematics problem, which is used in the robotics field and which forms the basis of the robot control system, is solved for a 7-joint serial robot manipulator. Since the study aims to compare software-based calculations and hardware-based calculations, the results are presented in a comparative way with the results obtained with Matlab software. The tests have been performed in the study revealed two important situations. Firstly, the biggest handicap of algorithms such as PSO that reach a result by searching in a certain solution space is that the solution times are not suitable for real-time applications. The other is that FPGA can be used as a prototyping device for real-time applications due to its speed and hardware-based running. Because according to the test results, FPGA has accelerated the calculations up to 1000 times. [ABSTRACT FROM AUTHOR]

In this study, we proposed a new metaheuristic algorithm called Electrical Search Algorithm (ESA). The proposed algorithm is based on the movement of electricity in high-resistive areas such as wood, glass, and gases. ESA has a unique initialization scheme that only one agent initializes at the lower and upper bounds of the search space, which creates structures called poles. After that, ESA uses unique exploration and exploitation strategies to search. The search mechanism is based on electrons moving to opposite poles. ESA differs from other metaheuristics compared to its initialization scheme, pole search mechanism, and update strategy of the best solutions. ESA was tested with the "100-Digit Challenge" benchmark functions in the IEEE-CEC-2019, four well-known benchmark functions, and an np-hard clustering problem. For the clustering problem, we used four well-known datasets: Iris, Wine, Seeds, and Hepatitis C Virus. ESA was compared with seven different metaheuristic algorithms on these well-known benchmark functions, and the results of the clustering problem were compared with the K-Means algorithm. Additionally, Friedman Signed Rank and post hoc Wilcoxon Test were run to show the significance of the results. In all of the well-known benchmark functions, ESA either offered the best results or similar results to other compared algorithms. The score of the ESA on the IEEE-CEC-2019 benchmark functions shows us that even with the minor evaluation numbers, ESA can achieve similar results to the competing algorithms. Results show that ESA has a robust mechanism for not trapping in local points and moves slow but persistent rate. [ABSTRACT FROM AUTHOR]

How to steadily find satisfactory solutions for high-dimensional multimodal and composition optimization problems is still a challenging issue. To fight against this pain-point problem, we propose sardine optimization algorithm (SOA) with agile locality and globality strategies for real optimization problems. Inspired by the survival philosophy of sardines, SOA simulates the transformation, migration, reproduction, elimination, and spread of sardines. As a varied-population-size optimization algorithm, the features of SOA are summarized as two key points. (i) Agile locality and globality strategies use adjacent and corresponding period ratios to control the local and global search behaviors. To our best knowledge, these strategies are a new technical road to balance exploration and exploitation efforts. (ii) Besides, SOA uses unique search operators based on the center movement of sardine schools. Specifically, when the center of one sardine school moves in these search operators, all sardines in this school also move in the same direction. This mobility style is different from most meta-heuristic algorithms, as far as we know. We used all unconstrained optimization problems in the CEC2013 test suite and six real-world constrained optimization problems as our benchmarks. SOA outperforms eight algorithms (like the two winning algorithms of CEC2013 and CEC2014), especially for high-dimension multimodal and composition optimization problems. For instance, Wilcoxon results between SOA and the two winning algorithms of CEC2013 and CEC2014 for 90-dimensional unconstrained optimization problems of the CEC2013 test suite are 16:10 and 18:8. The coding of SOA can be downloaded from "https://github.com/1654402787/SOA" (unzip password: soasoasoa1357). [ABSTRACT FROM AUTHOR]

Estimating the parameters of photovoltaic (PV) models accurately is vital to increase the effectiveness of PV systems. During the past few years, many approaches have been developed to solve this problem. However, due to the presence of nonlinearity and multi-modality in the problem, the estimated parameters are usually not very accurate and reliable. Therefore, this paper proposes a novel hybrid algorithm called chaos game optimization-least squares (CGO-LS) algorithm. The novelty of CGO-LS is that it adopts a cascade estimation strategy based on parameter decomposition. By the aid of this decomposition, CGO-LS combines a sophisticated nonlinear optimization capability of chaos game optimization (CGO) and the power of the optimal linear least squares (LS) estimator. LS focuses directly on estimating linear parameters, thus reducing the workload of CGO and helping to increase its convergence speed. To validate the performance of CGO-LS, it is employed to estimate the parameters of four PV models, including single-diode, double-diode, three-diode models, and PV module model. The results obtained by CGO-LS are compared with those of CGO, six state-of-the-art metaheuristics, their hybridized versions with LS, as well as some reported results in the literature. The overall results show that CGO-LS possesses superior estimation performance and excellent robustness in emulating experimental datasets. [ABSTRACT FROM AUTHOR]

Blind and visually impaired people experience difficulty positioning themselves in unfamiliar areas; they may become disoriented and need help from another person. Referred to as 'localization', this task is one of the significant challenges frequently encountered by blind people in unfamiliar indoor and outdoor environments. This paper aims to design and implement an energy-efficient localization system (EELS) for the blind and visually impaired based on a Zigbee wireless network and an artificial neural network (ANN) used in indoor environments. The ANN algorithm was adopted to improve localization accuracy for the blind and visually impaired. The ANN was fused independently with six metaheuristic algorithms, namely backtracking search algorithm (BSA), crow search algorithm (CSA), gravitational search algorithm (GSA), slime mould algorithm (SMA), particle swarm optimization (PSO) and multi-verse optimizer-ANN (MVO), to determine the optimal neurons and learning rate of ANN. Consequently, the ANN's performance was optimized and localization errors were improved. In addition, the power consumption of the EELS was minimized based on a proposed energy-efficient localization algorithm (EELA). The experimental results in indoor environments verified the potential of MVO-ANN in improving localization errors, achieving mean errors of 0.55 and 0.65 m in testing and validating phases, respectively. In addition, the EELS achieved a power savings of 63% based on EELA. Moreover, the EELS had better localization accuracy and power savings than other systems reported in the literature. [ABSTRACT FROM AUTHOR]

With the development of websites and social networks, Internet users generate a massive amount of comments and information on the Web. Sentiment analysis, also called opinion mining, offers an opportunity to mine the people's sentiments and emotions from the textual comments. In the last decade, sentiment analysis has been applied in research areas such as recommendation and support systems and has become an area of interest for many researchers. Therefore, many studies have been carried out on English, while other languages, such as Arabic, received less attention. Increasingly, sentiment analysis researchers use machine learning due to its excellent performance. However, the generated models are black boxes and non-interpretable by the users. The rule-based classification is a promising approach for generating interpretable models. This work proposes a classification rule-based Arabic sentiment analysis approach together with a new binary equilibrium optimization metaheuristic algorithm as an optimization method for classification rule generation from Arabic documents. The proposed approach has been experimented on the Opinion Corpus for Arabic (OCA) and generates a classification model of thirteen rules. The comparison results with state-of-the-art methods show that the proposed approach outperforms all other white-box models regarding classification accuracy. [ABSTRACT FROM AUTHOR]

Different sports require different set of equipment with different specifications and configurations. The oar-shaft optimal configurations and mechanical characteristics are important problem in rowing sport engineering. The oar-shaft design has a major impact on the effectiveness, performance, and safety of rowing technique. In this paper, the grey wolf optimizer is proposed for tackling the problem of determining the optimal design of the oar-shaft, with continuous design variables, so that all constraints consisting of the stress factor-of-safety (FoS) and target deflection ( Δ t ) are completely satisfied. The efficiency of the proposed GWO algorithm for solving the oar-shaft optimization is demonstrated through sculling and sweeps oar-shaft construction with two type of shafts (Ultra Light and Ultra Light Nano) of different stiffness. For experimental results, we established dataset of 31 shafts' cases using the BRACA-SPORT stiffness options. The experimental result demonstrates that the oar-shaft optimal designs for minimizing the oar-shaft rotational inertia identified with the GWO met the oar-shaft deflection and stress constraints, and the feasible ranges of design parameters—radius, inboard and outboard setting, length and tube wall thickness. The optimal results of 31 shafts construction show that the deflection is an important for minimizing rotational inertia of the shaft and blade about the oarlock. While a lot of studies have focused on various aspects of the shaft construction, this paper initially addressed the problem of oar-shaft optimization in metaheuristics terms for the first time. [ABSTRACT FROM AUTHOR]

Since the past few years, several metaheuristic algorithms, inspired by the natural processes, have been introduced to solve different complex optimization problems. Studying and comparing the convergence, computational burden and statistical significance of those metaheuristics are helpful for future algorithmic development and their applications. This paper focuses on comparing the optimization performance of classical dragonfly algorithm (DA) and its seven different variants, i.e., hybrid memory-based dragonfly algorithm with differential evolution (DADE), quantum-behaved and Gaussian mutational dragonfly algorithm (QGDA), memory-based hybrid dragonfly algorithm (MHDA), chaotic dragonfly algorithm (CDA), biogeography-based Mexican hat wavelet dragonfly algorithm (BMDA), hybrid Nelder–Mead algorithm and dragonfly algorithm (INMDA) and hybridization of dragonfly algorithm and artificial bee colony (HDA) while solving 80 CEC-2021 benchmark problems. It is observed that the convergence rates of different variants of DA algorithm vary, and the corresponding computational times for such variations are also evaluated. This paper finally ranks DA and its variants according to their convergence efficiency and Friedman test. The DADE, QGDA, BMDA and DA evolve out as the most efficient algorithms for solving the considered CEC-2021 benchmark problems. [ABSTRACT FROM AUTHOR]

The increasing demand on electrical power consumption all over the world makes the need of stable and reliable electrical power grids is indispensable. However, one of hostile obstacles which delays reaching out to that desired goal is occurrence of faults. Despite to fact that dozens of studies have been put forward to detect electrical faults, these studies still suffer from several downsides such as validation and automation. In this paper, an electrical fault detection system based on the concept of anomaly detection is presented. The main salient advantages of the proposed system are overcoming the limitations of existed counterpart systems and its compatibility with real-world power grids. To enhance the performance of the proposed system, two vital stages are involved in its design prior to training, namely, data preprocessing and pre-training. Whereas the former is to prepare raw signals to be modeled, the latter is dedicated for model's hyperparameter selection using the particle swarm optimization metaheuristic. Moreover, two well-known anomaly detection models, namely, One-Class Support Vector Machines and principal component analysis are utilized to validate the proposed system as well as real-time data (VSB dataset) are used to train and test models. Finally, the experimental results and discussion emphasize that there is a performance improvement in detecting of electrical faults when using the proposed system. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of optimal location and sizing of distributed generators (DGs) in direct current (DC) grids. To solve it, we propose an optimization approach with an objective function that aims to reduce power losses due to energy transport, while considering all the constraints that represent DC grids in a distributed generation environment. For the mathematical formulation of the problem, we used a mixed-integer nonlinear programming (MINLP) model, which allowed us to evaluate the impact of all possible configurations (i.e., location and size of DGs in the DC network) on the objective function and the constraints. The solution method proposed here is a master–slave strategy that implements a hybrid solution methodology that combines a genetic algorithm (GA) and the vortex search algorithm (VSA). The GA is in charge of solving the location problem in the master stage, and the VSA is responsible for sizing the DGs in the slave stage. To evaluate the effectiveness and robustness of the proposed GA/VSA methodology, we employed two test systems (i.e., 21 and 69 buses) considering a maximum penetration of distributed generation equal to 40% of the power generated by the slack buses. Furthermore, we also implemented nine other hybrid methodologies based on metaheuristic techniques (proposed in the literature for solving the problem addressed here) to make comparisons. All the solution methods used and proposed in this paper are based on sequential programming to avoid the need for specialized software and thus reduce the complexity and cost of the solutions. The effectiveness of the proposed solution was evaluated in two scenarios: (1) peak power demand and (2) variation in power generation and demand associated with photovoltaic generation and user demand in Medellín, Colombia. The results demonstrate that the GA/VSA methodology achieved the best results in terms of solution quality and processing times in all the test scenarios proposed in this study. [ABSTRACT FROM AUTHOR]

One of the main challenges in power converters is to adopt a convenient controller that is designed with an appropriate approach. In terms of controllers, linear and nonlinear types are available. Nonlinear controllers may be good for achieving dynamic capabilities; however, designing them involves undesirable complexity. Thus, alternative linear counterparts are desirable to achieve optimum performance. Fractional-order proportional-integral derivative (FOPID) controller stands as a good choice for this purpose since it is a more capable version of one of widely adopted linear controller known as PID. Therefore, in this study, a FOPID controller was used to achieve optimum performance for a buck converter. To obtain the best performance, a novel hybridized metaheuristic algorithm, which combines both Lévy flight distribution and simulated annealing algorithms (LFDSA), was utilized. The developed algorithm involves a balanced structure in terms of explorative and exploitative phases, which was confirmed via performing related analysis on unimodal and multimodal benchmark functions. Non-parametric statistical test has also showed the better capability of the proposed algorithm. Due to its enhanced capability, the proposed algorithm helped achieving optimum values of FOPID parameters such that a better closed-loop output voltage control performance of the buck converter in terms of time and frequency domain responses as well as disturbance rejection have been achieved. The proposed LFDSA-based FOPID controller also tested against other capable and reported state-of-the-art algorithm and the results have also verified the superior capability of the LFDSA over other approaches. [ABSTRACT FROM AUTHOR]

Mobile wireless sensor networks (MWSNs) have revolutionized observing and tracking. Accurately locating mobile sensor nodes remains a challenging task. The precisely identifying event source location is very important. To locate mobile sensor nodes there is a need of developing an efficient localization method. Most of the researcher have used multiple anchor nodes for localization. Therefore, in resource constraint networks reducing number of anchor nodes is an open research issue. The main idea of this work is to propose a localization method using single anchor node. To achieve this, a novel coordinated auto-localization algorithm with particle swarm optimization (PSO) is introduced to enhance localization and tracking of mobile sensor nodes. A mathematical framework has been developed which uses parallel coordinate system to identify location and PSO to track movement pattern of sensor nodes. PSO minimized localization error by refining position accuracy through iterative convergence. It achieves 10% reduction in localization error and a 25% increase in correctly localized nodes, with an overall tracking precision of 80%. Comparative analysis with different techniques like mobile anchor positioning with mobile anchor & neighbor, fish swarm optimization algorithm, DV-hop localization, and autonomous groups particle swarm optimization shows that this method reduces the average localization error to 10% and improves localization efficiency by reducing the required time by 16% compared to other techniques. A pairwise Wilcoxon rank test with a 95% confidence interval shows the proposed method’s superior performance, with a mean of 2.6321E-18 and standard deviation of 3.2705E-19, compared to other metaheuristic algorithms. [ABSTRACT FROM AUTHOR]

Machining CFRP with WEDM is extremely challenging and produces kerf of poor quality. Therefore, the present research venture is intended to improve the kerf quality produced in WEDM of woven CFRP through a machine learning-based metaheuristic algorithm. Two ensemble-based machine learning algorithms i.e., the Random Forest (RF), and Adaptive Boosting algorithm (AdaBoost) have been used to model the kerf width. The performance of RF is found to be superior to AdaBoost in terms of generalization prowess as the box plot corresponding to the predicted KW by RF closely resembles the box plot of experimental KW whereas the box plot corresponding to the predicted KW by AdaBoost has a varying distribution with the box-plot of experimental KW. Furthermore, the kerf width optimization has been conducted using a broad range of optimization techniques from nature-inspired to mathematically driven approaches such as the Moth flame optimizer (MFO), Grey Wolf optimizer, Chimp optimization algorithm, and sine cosine algorithm in an attempt to compare the computational performance of the algorithms. It has been revealed that MFO discovered the minimum KW (global optimum solution) and exhibited rapid convergence as compared to its counterparts. The optimal results are Ton = 26 microsecs, Toff = 50 microsecs, I = 7A, and V = 70 V. Additionally, the proposed optimization's durability has been examined using the traditional desirability approach. The percentage improvement in KW through the proposed optimization as compared to the desirability approach is 5.6%. Lastly, FESEM images are provided for varying process parametric conditions. [ABSTRACT FROM AUTHOR]

The simultaneous size and shape optimization of truss structures is a notoriously challenging optimization problem due to its NP-hard nature, highly nonlinear design constraints, and conflicting design variables. In this study, a novel human-based metaheuristic algorithm, called the Heap-based Optimizer (HBO), is presented for the configuration optimization of truss structures. Inspired by the Corporate Rank Hierarchy (CRH) concept, search agents are logically arranged into a hierarchy based on their fitness values. Solutions then compete with one another for promotions within the hierarchy by efficiently interacting with each other in a formal way. Overall, the proposed method is characterized by its robust exploration and ability to escape local optima. The effectiveness of the proposed technique was tested using five challenging truss benchmarks. The overall weight of the structure was minimized as an objective function, and the finite element procedure was coded in MATLAB. The selected variables for the problem included both member cross-sectional areas and nodal coordinates of the joints. Normal stresses, Euler buckling stresses, and nodal displacement limitations were moreover considered as design constraints. For statistical significance, the Friedman ranking test was implemented to extract clear winners from the comparison process. Results point to the clear superiority of HBO compared to all other state-of-the-art methods. Specifically, HBO designs were (on average) 7.3% lighter than those previously reported in literature. A comparison of computational speed furthermore demonstrated HBO’s rapid convergence capabilities, with an estimated 35.5% reduction in computational effort. Further research into HBO as an efficient structural optimizer is therefore highly recommended. [ABSTRACT FROM AUTHOR]

This study applies the cuckoo optimization algorithm (COA), inspired by the brood reproduction technique of cuckoo birds, to interpret magnetic anomalies of 2D dipping dyke-like structures. The primary issue addressed is the need for accurate delineation and explanation of dyke parameters, which are crucial for visualizing dyke propagation (important for volcanic hazard assessment), tracing mineralized zones associated with dykes, and understanding their geodynamic significance. Our method identifies dyke parameters at the minimum value of the suggested objective function, ensuring the best fit. The proposed COA method was tested on both noise-free numerical magnetic datasets and datasets with varying levels of random noise (5%, 10%, and 20%), as well as real-case datasets from China and the UK. A comparative analysis with particle swarm optimization (PSO) and genetic algorithm (GA) methods was conducted to evaluate the efficiency and consistency of COA. The results demonstrate that COA aligns well with existing geological and geophysical information, offering superior accuracy and robustness compared to traditional techniques. This study provides a novel and effective approach for subsurface characterization, advancing the precision of geological and geophysical interpretations. [ABSTRACT FROM AUTHOR]

The traveling salesman problem (TSP), a challenging NP-hard problem, often necessitates the use of metaheuristic algorithms to find approximate solutions. These include techniques such as ant colony optimization (ACO), particle swarm optimization (PSO), artificial bee colony (ABC), genetic algorithm (GA), simulated annealing (SA), and Tabu search (TS). In this study, we explore a variant called the Close Enough TSP, characterized by dynamic targets within the communication ranges of cluster head (CH) nodes. This scenario requires data collection by an unmanned aerial vehicle (UAV), which need not directly overfly each node. We propose a new method that blends metaheuristic algorithms with geometric heuristics to create a near-optimal UAV flight path. Our evaluation, aimed at reducing travel distance across scenarios involving 20, 52, 100, and 150 target areas, shows that the ABC algorithm converges effectively toward optimal solutions. The results validate the efficiency of our approach, underlining its potential for UAV-driven data gathering and related areas. [ABSTRACT FROM AUTHOR]

This paper presents a Chaos–Infused Moth–Flame Optimizer (CI-MFO). The parent algorithm is modified to account for deviations in search agent (moth) flight trajectory and variations in the flame orientation for an enhanced balance between exploration and exploitation tendencies. Actual photographic evidence showing light traces of such phototactic moths in-flight has been used to model flight path deviations using Chaos Theory. This approach considers their intelligence and erratic flight behavior (when subjected to excessive crowding). The performance of the developed CI-MFO algorithm is investigated comprehensively using a suite of fifty-eight benchmarking functions, including seven unimodal, six multimodal, ten fixed-dimension multimodal, six CEC-2005 hybrid-composite, and twenty-nine CEC-2017 hybrid-composite functions. The proposed algorithm's effectiveness is tested against several classical algorithms and some modified metaheuristic optimization algorithms in terms of obtained mean optima and standard deviations, and scalability analysis is also performed. The paper concludes by solving several real-world problems and comparing the proposed algorithm's performance against several reported algorithms. The proposed algorithm exhibited a substantially better solution-finding ability. [ABSTRACT FROM AUTHOR]

Spotted hyena optimization (SHO) is one of the newly evolved swarm-based metaheuristic optimization methods based on the social life cycle of hyenas. In recent times SHO is being applied to various engineering applications as well as to solve real-life complications. In this paper, we have hybridized SHO with quadratic approximation operator (QAO), termed as QASHO. The proposed QASHO has been scrutinized to enhance the exploitation ability, aiming to achieve global optimum, as QAO performs better within the local confinement region. Furthermore, the proposed approach shows improved strength in terms of escaping from the local minima trap, as in each iteration we discard some of the worst individuals by some suitable ones. To validate the proficiency of the proposed QASHO approach, 28 standard problems have been preferred in connection with IEEE-CEC-2017. The outcome observed from the suggested method has also equated with contemporary metaheuristic approaches. To prove the statistical significance, a nonparametric test has also been accomplished. Additionally as a real-life application, the suggested approach QASHO has utilized to train wavelet higher-order neural networks (HONN) by choosing datasets from the UCI store. The above correlations reveal that QASHO can deal with complex optimization tasks. [ABSTRACT FROM AUTHOR]

This paper demonstrates the development of a novel metaheuristic algorithm which has an enhanced diversification and intensification features and aims to construct an efficient mechanism via the developed algorithm to control a magnetic object suspension. Manta ray foraging optimization (MRFO) algorithm together with generalized opposition-based learning (GOBL) technique and Nelder–Mead (NM) simplex search method is used to define the general frame of the developed algorithm. The developed novel algorithm (Ob-MRFONM) employs the NM method for better intensification whereas integrates the GOBL technique for better diversification. The constructed Ob-MRFONM algorithm is confirmed to have enhanced capabilities via evaluations against well-known unimodal and multimodal benchmark functions. The developed algorithm is also utilized to reach optimum values of a real PID plus second-order derivative (PIDD2) controller employed in a magnetic object suspension system to demonstrate the capability of the Ob-MRFONM algorithm for such a complex real-world engineering problem. It is worth noting that this paper is the first report in the literature that demonstrates the application of a real PIDD2 controller in a magnetic object suspension system. To reach better capability, a new objective function is used for minimization. Then, the proposed approach is comparatively evaluated in terms of statistical and nonparametric statistical analysis, convergence, transient response, disturbance rejection, controller effort and effects of the noise signal. The demonstrated results also confirm the highly competitive capability of the proposed algorithm for the complex magnetic object suspension system. The nonlinear model of the system is also used in this study in order to validate the linearized model. [ABSTRACT FROM AUTHOR]

Optimization techniques are widely used to solve variety of problems related to the fields of engineering, statistics, finance, etc. In this article, a new optimization algorithm named criminal search optimization algorithm (CSOA) has been proposed. This proposed algorithm is inspired by policemen and replicates the strategies and intelligence used by a team of the policemen to catch a criminal for a crime. The performance of CSOA is validated using two suites of benchmark functions (CEC-2005 and CEC-2020). Further, the proposed method is used to solve a multi-objective real-world optimization problem, i.e. a combined emission economic dispatch problem. To evaluate the performance of the proposed method, five test cases have been considered in this study. The results obtained are compared with other existing well-known optimization methods to show the superiority of the proposed CSOA method. [ABSTRACT FROM AUTHOR]

Lightweight electric motor topologies used in electric transport applications require high-power density structures. Selection of suitable core material with appropriate physical geometry is important at design stage to reduce the core losses and the weight of the motor. This paper presents a novel hybrid metaheuristic optimization technique termed as Aquila-Grasshopper-Optimization (AGO), inspired by the hunting behavior of Aquila and swarming behavior of grasshoppers in search of the food source. This technique merges the merits of both Aquila optimizer (AO) and Grasshopper optimization algorithm (GOA) to create a balance between exploration and exploitation stages. By introducing the concept of normalization of the distances between the solutions (swarms) of the optimization problem at both narrowed and expanded exploration and exploitation stages of AO, the population is searched thoroughly to avoid the possibility of converging to any local optimum and to increase the convergence rate to reach the global optima. The performance of the proposed technique is first tested for various benchmark functions and thereafter implemented to the real-world problem to optimize the motor geometry of Surface Inset Permanent Magnet Synchronous Motor (SI-PMSM) to reduce the overall core losses. Two different core materials are employed in the core of the motor to study the influence of core material on the performance of the motor. In addition, the study is expanded by providing suitable trench cuts in motor core so that weight of the motor can be reduced substantially for lightweight motor applications. It is established that the variation in properties of core material can increase the power density and AGO technique can minimize core losses to obtain optimized motor core geometry thereby improving the overall electromagnetic performance of the motor. [ABSTRACT FROM AUTHOR]

Global optimization solves real-world problems numerically or analytically by minimizing their objective functions. Most of the analytical algorithms are greedy and computationally intractable (Gonzalez in Handbook of approximation algorithms and metaheuristics: contemporary and emerging applications, vol. 2. CRC Press, Boca Raton, 2018). Metaheuristics are generally nature-inspired optimization algorithms. They numerically find a near-optimal solution for optimization problems in a reasonable amount of time. We propose a novel metaheuristic algorithm for global optimization. It is based on the shooting and jumping behaviors of the archerfish for hunting aerial insects. We name our proposed algorithm the archerfish hunting optimizer (AHO). The AHO algorithm has two parameters (the swapping angle and the attractiveness rate) to set. We execute the AHO algorithm using five different values for each parameter. In all, we perform 25 simulations for four distinct values of the search space dimension (i.e., 5, 10, 15, and 20). We run the Friedman test to determine the best values of parameters for each dimension. We perform three different comparisons to validate the proposed algorithm's performance. First, AHO is compared to 12 recent metaheuristic algorithms (the accepted algorithms for the 2020's competition on single-objective bound-constrained numerical optimization) on ten test functions of the benchmark CEC 2020 for unconstrained optimization. The experimental results are evaluated using the Wilcoxon signed-rank test. Experimental outcomes show that the AHO algorithm, in terms of robustness, convergence, and quality of the obtained solution, is significantly competitive compared to state-of-the-art methods. Second, the performance of AHO and three recent metaheuristic algorithms is evaluated using five engineering design problems taken from the benchmark CEC 2020 for non-convex constrained optimization. The obtained results are ranked using the ranking scheme detailed in the corresponding paper, and the obtained ranks illustrate that AHO is very competitive when opposed to the considered algorithms. Finally, the performance of AHO in solving five engineering design problems is assessed and compared to several well-established state-of-the-art algorithms. We analyzed the obtained numerical results in detail. These results show that the AHO algorithm is significantly better than, or at least comparable to the considered algorithms with very efficient performance in solving many optimization problems. The statistical indicators illustrate that the AHO algorithm has a high ability to significantly outperform the well-established optimizers. [ABSTRACT FROM AUTHOR]

Data available online is growing day by day that leads to a tough competition among data publishers to attract the largest possible audience. Obtaining reliable prediction related to future popularity of the online content becomes a concern to the publishers. In this paper, a novel nature-inspired metaheuristic framework is proposed that shortlists the prominent features of the video to participate in making predictions. Fitness of the set of selected features is calculated using mean square error of the deviation between predicted and actual popularity. The exhaustive examinations on standard benchmark parameters is performed on the datasets, i.e., Facebook 2015, Top and random datasets of YouTube. The performance of the proposed algorithms, namely particle swarm optimization with support vector regression (PSO-SVR), bat algorithm with support vector regression (BA-SVR), dragonfly algorithm with support vector regression (DA-SVR) and existing prediction method support vector regression (SVR), is compared. DA-SVR outperforms SVR, PSO-SVR and BA-SVR in terms of coefficient of regularization (R2) score and number of features selected. [ABSTRACT FROM AUTHOR]

This paper proposes a novel metaheuristic Child Drawing Development Optimization (CDDO) algorithm inspired by the child's learning behavior and cognitive development using the golden ratio to optimize the beauty behind their art. The golden ratio was first introduced by the famous mathematician Fibonacci. The ratio of two consecutive numbers in the Fibonacci sequence is similar, and it is called the golden ratio, which is prevalent in nature, art, architecture, and design. CDDO uses golden ratio and mimics cognitive learning and child's drawing development stages starting from the scribbling stage to the advanced pattern-based stage. Hand pressure width, length and golden ratio of the child's drawing are tuned to attain better results. This helps children with evolving, improving their intelligence and collectively achieving shared goals. CDDO shows superior performance in finding the global optimum solution for the optimization problems tested by 19 benchmark functions. Its results are evaluated against more than one state-of-art algorithms such as PSO, DE, WOA, GSA, and FEP. The performance of the CDDO is assessed, and the test result shows that CDDO is relatively competitive through scoring 2.8 ranks. This displays that the CDDO is outstandingly robust in exploring a new solution. Also, it reveals the competency of the algorithm to evade local minima as it covers promising regions extensively within the design space and exploits the best solution. [ABSTRACT FROM AUTHOR]

This article presents a novel hybrid control scheme for speed control of Brushless DC (BLDC) motor by simultaneously controlling BLDC motor reference current and inverter DC bus voltage. A fractional-order PID (FOPID) controller is employed to control BLDC motor reference current while a fuzzy logic controller manipulates the inverter DC bus voltage. A modified harmony search (HS) metaheuristic technique is developed for FOPID controller parameters tuning. Three different operating conditions are applied to test the motor, including no-load operation, varying load operation, and varying speed operation to verify the proposed controller's effectiveness. Furthermore, the proposed hybrid control strategy has been compared to Fuzzy-based and FOPID-based speed control schemes. The obtained results confirm that the proposed control scheme provides better and accurate speed control over a wide range of speeds. Also, the proposed controller decreases the torque ripples under different operating conditions. [ABSTRACT FROM AUTHOR]

In this paper, the pattern synthesis of concentric elliptical antenna arrays (CEAAs) for effective antenna design is presented. The current amplitudes and angular positions values of the CEAA elements are optimally found by using the Marine Predators Algorithm (MPA) in order to obtain the optimum radiation pattern. The MPA method is a novel nature-based optimization algorithm that is inspired by ocean predators foraging strategy. The antenna arrays with reduced maximum side lobe levels (MSL) and narrowed half power beam widths (HPBW) are an efficient antenna with optimum radiation pattern to suppress interference and increase directionality. In addition, CEAAs with different numbers of elements are examined to show the flexibility and performance of the proposed algorithm in this study. For 18-element CEAA, MPA obtained the HPBW between 29.18° and 31.34°, and MSL between − 20.70 and − 25.89 dB. For 30-element CEAA, the optimum values of HPBW are between 20.13° and 21.38°, and the optimum values of MSL are between − 18.90 and − 20.50 dB. The results of pattern synthesis obtained by MPA are compared with other metaheuristic optimization methods, and it is seen that the results of the proposed method are generally better. [ABSTRACT FROM AUTHOR]

Given the diversity of styles and sizes in apparel, marker planning which aims to arrange and move all the pattern parts of garments onto a long thin paper before the cutting process is a very important process for the apparel industry. In order to decrease the wastage of fabric after the cutting process, the marker layout essentially needs to be as compact as possible. In this paper, hybrid heuristics are proposed to conduct and achieve an optimized marker layout and length. First, a moving heuristic is presented as a new packing method to arrange and move the patterns without overlapping; here, an initial marker is presented to calculate the length. This heuristic considers multiple rotated angles and flipping positions of the patterns in order to obtain more diverse arrangements. With different arrangements, there is a higher chance of achieving an optimized marker layout and length. To further improve the solution received from the moving heuristic, soft computing algorithms are taken into account, including the genetic algorithm (GA), simulated annealing (SA), and hybrid genetic algorithm-simulated annealing (HGASA) to achieve a shorter length in the marker layout. The best marker length can be obtained from HGASA, which can save almost 28% of the length. Special cases with specific combinations in rotated angles are considered so that the industry can make informed choices on the algorithms suitable to address the marker planning problem. [ABSTRACT FROM AUTHOR]

The marine predator algorithm is a new nature-inspired metaheuristic algorithm that mimics biological interaction between marine predators and prey. It has been also stated from the literature that this algorithm can solve many real-world optimization problems which made it a new popular optimization technique for the researchers. However, there is still a deficiency in the marine predator algorithm such as the inability to produce a diverse initial population with high productivity, lack of quick escaping of the local optimization, and lack of widely and broadly exploration of the search space. In the present study, a developed version of this algorithm is proposed based on the opposition-based learning method, chaos map, self-adaptive of population, and switching between exploration and exploitation phases. The simulations are performed using MATLAB environment on standard test functions including CEC-06 2019 tests and a real-world optimization problem based on PID control applied to a DC motor to evaluate the performance of the suggested algorithm. The simulation results are compared with the original marine predator algorithm and five state-of-the-art optimization algorithms namely Particle Swarm Optimization, Grasshopper Optimization Algorithm, JAYA Algorithm, Equilibrium optimizer Algorithm, Whale Optimization Algorithm, Differential Search Algorithm, and League Championship Algorithm. Eventually, the simulation results proved that the suggested algorithm has better results compared with other algorithms for the studied case studies. [ABSTRACT FROM AUTHOR]

A novel nature-inspired metaheuristic optimization algorithm, called the quantum firefly algorithm, is proposed in this paper. The algorithm imitates (a) the social behaviour of fireflies mating in nature, (b) laws of quantum physics, and (c) laws of natural evolution. The algorithm combines the powers of two well-known algorithms: the firefly algorithm and the quantum genetic algorithm. The proposed quantum firefly algorithm's performance is tested on 15 mathematical test functions and one structural design problem. The obtained results show that the quantum firefly algorithm is very competitive compared to the firefly algorithm and the quantum genetic algorithm. [ABSTRACT FROM AUTHOR]

Feature transformation methods can be used effectively to improve the performance of the machine learning techniques, such as classification and clustering, via enhancing the discrimination among the samples. Additionally, binary representation of features may be necessary in several circumstances. For example, distributed systems may have limited bandwidth, storage, and energy that necessitate rough quantization of the measurements. This paper proposes a random projection-based feature transformation method that maps the data points from the original space into a new binary space. In the proposed transformation method, the random projection process is formulated as an optimization problem. A modified chaotic version of the sine–cosine algorithm is used to find the optimal random projector. The performance of the proposed transformation method is validated using 15 standard UCI benchmark datasets against the linear discernment analysis. Moreover, it is compared to other metaheuristic-based feature reduction methods including the gray wolf optimizer, the genetic algorithm, and the antlion optimizer. The obtained results assure the superiority of the proposed transformation method in terms of classification accuracy and storage requirements. [ABSTRACT FROM AUTHOR]