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Large-scale optimization (LSO) problems among photovoltaic (PV) and concentrated solar power (CSP) systems are attracting increasing attention as they help improve the energy dispatch efficiency of PV and CSP systems to minimize power costs. Therefore, it is necessary and urgent to systematically analyze and summarize various LSO methods to showcase their advantages and disadvantages, ensuring the efficient operation of hybrid energy systems comprising different PV and CSP systems. This paper compares and analyzes the latest LSO methods for PV and CSP systems based on meta-heuristic algorithms (i.e., Particle Swarm Optimization, Genetic Algorithm, Enhanced Gravitational Search Algorithm, and Grey Wolf Optimization), numerical simulation and stochastic optimization methods (i.e., Constraint Programming, Linear Programming, Dynamic Programming Optimization Algorithm, and Derivative-Free Optimization), and machine learning-based AI methods (Double Grid Search Support Vector Machine, Long Short-Term Memory, Kalman Filter, and Random Forest). An in-depth analysis and A comparison of the essence and applications of these algorithms are conducted to explore their characteristics and suitability for PV and CSP or hybrid systems. The research results demonstrate the specificities of different LSO algorithms, providing valuable insights for researchers with diverse interests and guiding the selection of the most appropriate method as the solution algorithm for LSO problems in various PV and CSP systems. This also offers useful references and suggestions for extracting research challenges in LSO problems of PV and CSP systems and proposing corresponding solutions to guide future research development. [ABSTRACT FROM AUTHOR]

The increasing public demand for fresh products has catalyzed the requirement for cold chain logistics distribution systems. However, challenges such as temperature control and delivery delays have led a significant product loss and increased costs. To improve the current situation, a novel approach to optimize cold chain logistics distribution for fresh products will be presented in the paper, utilizing a hybrid Tabu-Grey wolf optimizer (TGWO) algorithm. The proposed hybrid approach combines Tabu search (TS) and Grey wolf optimizer (GWO), employing TS for exploration and GWO for exploitation, aiming to minimize distribution costs in total and establish efficient vehicle scheduling schemes considering various constraints. The effectiveness of the TGWO algorithm is demonstrated through experiments and case studies compared to other heuristic algorithms. Comparative analysis against traditional optimization methods, including Particle swarm optimization (PSO), Whale optimization algorithm (WOA), and original GWO, highlights its superior efficiency and solution quality. This study contributes theories by demonstrating the efficacy of hybrid optimization techniques in complex supply chain networks and dynamic market environments. The practical implication lies in the implementation of TGWO to bolster distribution efficiency, cost reduction, and product quality maintenance throughout the logistics process, offering valuable insights for operational and strategic improvements by decision-makers. However, the study has limitations in generalizability and assumptions, suggesting future research areas including exploring new search operators, applying additional parameters, and using the algorithm in diverse real-life scenarios to improve its effectiveness and applicability. [ABSTRACT FROM AUTHOR]

In recent years, due to the rapid growth of the world's population, the demand for agricultural products and food is growing increasingly. Therefore, the agricultural supply chain optimization has been grabbed by researchers to reduce food security concerns. On the other hand, the production amount of farmers is affected by various factors, including environmental conditions. In this paper, a supply chain network is investigated by developing a Mixed-Integer Linear Programming (MILP) model to effectively improve economic objectives under uncertainty. Then, a scenario-based robust optimization approach is employed to deal with the uncertainty. One of the novelities of our paper is considering weather conditions and economic fluctuations in different scenarios. The effectiveness of the proposed mathematical model has been confirmed by a real case study of dates farms. Dates and its by-products have a significant role in GDP, job creation, export, and the creation of various packaging and processing. Moreover, three meta-heuristic algorithms including Whale Optimization Algorithm (WOA), Particle Swarm Optimization (PSO), and a hybrid algorithm based on them (WOA–PSO) are adapted to deal with the NP-hardness of the problems. Moreover, the parameters of the proposed algorithms are improved by the Taguchi method, and to achieve more exact measurements, sensitivity analysis is performed. Finally, the numerical results confirmed that the accuracy of the hybrid algorithm was between 1.9 and 2.8%. Therefore, this approach could be practical and efficient for solving large-sized problems. The obtained outcomes demonstrated that the planned model provides tactical considerations for the related managers. [ABSTRACT FROM AUTHOR]

Purpose: Advancement in optimization of VLSI circuits involves reduction in chip size from micrometer to nanometer level as well as fabrication of a billions of transistors in a single die where global routing problem remains significant with a trade-off of power dissipation and interconnect delay. This paper aims to solve the increased complexity in VLSI chip by minimization of the wire length in VLSI circuits using a new approach based on nature-inspired meta-heuristic, invasive weed optimization (IWO). Further, this paper aims to achieve maximum circuit optimization using IWO hybridized with particle swarm optimization (PSO). Design/methodology/approach: This paper projects the complexities of global routing process of VLSI circuit design in mapping it with a well-known NP-complete problem, the minimum rectilinear Steiner tree (MRST) problem. IWO meta-heuristic algorithm is proposed to meet the MRST problem more efficiently and thereby reducing the overall wire-length of interconnected nodes. Further, the proposed approach is hybridized with PSO, and a comparative analysis is performed with geosteiner 5.0.1 and existing PSO technique over minimization, consistency and convergence against available benchmark. Findings: This paper provides high performance–enhanced IWO algorithm, which keeps in generating low MRST value, thereby successful wire length reduction of VLSI circuits is significantly achieved as evident from the experimental results as compared to PSO algorithm and also generates value nearer to geosteiner 5.0.1 benchmark. Even with big VLSI instances, hybrid IWO with PSO establishes its robustness over achieving improved optimization of overall wire length of VLSI circuits. Practical implications: This paper includes implications in the areas of optimization of VLSI circuit design specifically in the arena of VLSI routing and the recent developments in routing optimization using meta-heuristic algorithms. Originality/value: This paper fulfills an identified need to study optimization of VLSI circuits where minimization of overall interconnected wire length in global routing plays a significant role. Use of nature-based meta-heuristics in solving the global routing problem is projected to be an alternative approach other than conventional method. [ABSTRACT FROM AUTHOR]

Antennas play a crucial role in designing an efficient communication system. However, reducing the maximum sidelobe level (SLL) of the beam pattern is a crucial challenge in antenna arrays. Pattern synthesis in smart antennas is a major area of research because of its widespread application across various radar and communication systems. This paper presents an effective technique to minimize the SLL and thus improve the radiation pattern of the linear antenna array (LAA) using the chaotic inertia-weighted Wild Horse optimization (IERWHO) algorithm. The wild horse optimizer (WHO) is a new metaheuristic algorithm based on the social behavior of wild horses. The IERWHO algorithm is an improved Wild Horse optimization (WHO) algorithm that combines the concepts of chaotic sequence factor, nonlinear factor, and inertia weights factor. In this paper, the method is applied for the first time in antenna array synthesis by optimizing parameters such as inter-element spacing and excitation to minimize the SLL while keeping other constraints within the boundary limits, while ensuring that the performance is not affected. For performance evaluation, the simulation tests include 12 benchmark test functions and 12 test functions to verify the effectiveness of the improvement strategies. According to the encouraging research results in this paper, the IERWHO algorithm proposed has a place in the field of optimization. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to develop a multi-objective problem for scheduling the operations of trucks entering and exiting cross-docks where the number of unloaded or loaded products by trucks is fuzzy logistic. The first objective function minimizes the maximum time to receive the products. The second objective function minimizes the emission cost of trucks. Finally, the third objective function minimizes the number of trucks assigned to the entrance and exit doors. Design/methodology/approach: Two steps are implemented to validate and modify the proposed model. In the first step, two random numerical examples in small dimensions were solved by GAMS software with min-max objective function as well as genetic algorithms (GA) and particle swarm optimization. In the second step, due to the increasing dimensions of the problem and computational complexity, the problem in question is part of the NP-Hard problem, and therefore multi-objective meta-heuristic algorithms are used along with validation and parameter adjustment. Findings: Therefore, non-dominated sorting genetic algorithm (NSGA-II) and non-dominated ranking genetic algorithm (NRGA) are used to solve 30 random problems in high dimensions. Then, the algorithms were ranked using the TOPSIS method for each problem according to the results obtained from the evaluation criteria. The analysis of the results confirms the applicability of the proposed model and solution methods. Originality/value: This paper proposes mathematical model of truck scheduling for a real problem, including cross-docks that play an essential role in supply chains, as they could reduce order delivery time, inventory holding costs and shipping costs. To solve the proposed multi-objective mathematical model, as the problem is NP-hard, multi-objective meta-heuristic algorithms are used along with validation and parameter adjustment. Therefore, NSGA-II and NRGA are used to solve 30 random problems in high dimensions. [ABSTRACT FROM AUTHOR]

The sand cat swarm optimization algorithm (SCSO) is a novel metaheuristic algorithm that has been proposed in recent years. The algorithm optimizes the search ability of individuals by mimicking the hunting behavior of sand cat groups in nature, thereby achieving robust optimization performance. It is characterized by few control parameters and simple operation. However, due to the lack of population diversity, SCSO is less efficient in solving complex problems and is prone to fall into local optimization. To address these shortcomings and refine the algorithm's efficacy, an improved multi-strategy sand cat optimization algorithm (IMSCSO) is proposed in this paper. In IMSCSO, a roulette fitness–distance balancing strategy is used to select codes to replace random agents in the exploration phase and enhance the convergence performance of the algorithm. To bolster population diversity, a novel population perturbation strategy is introduced, aiming to facilitate the algorithm's escape from local optima. Finally, a best–worst perturbation strategy is developed. The approach not only maintains diversity throughout the optimization process but also enhances the algorithm's exploitation capabilities. To evaluate the performance of the proposed IMSCSO, we conducted experiments in the CEC 2017 test suite and compared IMSCSO with seven other algorithms. The results show that the IMSCSO proposed in this paper has better optimization performance. [ABSTRACT FROM AUTHOR]

The emergence of microgrids arises from the growing integration of Renewable Energy Resources (RES) and Energy Storage Systems (ESSs) into Distribution Networks (DNs). Effective integration, coordination, and control of Multiple Microgrids (MMGs) whereas navigating the complexities of energy transition within this context poses a significant challenge. The dynamic operation of MMGs is a challenge faced by the traditional distributed hierarchical control techniques. The application of Artificial Intelligence (AI) techniques is a promising way to improve the control and dynamic operation of MMGs in future smart DNs. In this paper, an innovative hybrid optimization technique that originates from Cheetah Optimization (CHO) and Particle Swarm Optimization (PSO) techniques is proposed, known as HYCHOPSO. Extensive benchmark testing validates HYCHOPSO's superiority over CHO and PSO in terms of convergence performance. The objective for this hybridization stems from the complementary strengths of CHO and PSO. CHO demonstrates rapid convergence in local search spaces, while PSO excels in global exploration. By combining these techniques, the aim is to leverage their respective advantages and enhance the algorithm's overall performance in addressing complex optimization problems. The contribution of this paper offering a unique approach to addressing optimization challenges in microgrid systems. Through a comprehensive comparative study, HYCHOPSO is evaluated against various metaheuristic optimization approaches, demonstrating superior performance, particularly in optimizing the design parameters of Proportional-Integral (PI) controllers for hierarchical control systems within microgrids. This contribution expands the repertoire of available optimization methodologies and offers practical solutions to critical challenges in microgrid optimization, enhancing the efficiency, reliability, and sustainability of microgrid operations. HYCHOPSO achieves its optimal score within fewer than 50 iterations, unlike CHO, GWO, PSO, Hybrid-GWO-PSO, and SSIA-PSO, which stabilize after around 200 iterations. Across various benchmark functions, HYCHOPSO consistently demonstrates the lowest mean values, attains scores closer to the optimal values of the benchmark functions, underscoring its robust convergence capabilities.the proposed HYCHOPSO algorithm, paired with a PI controller for distributed hierarchical control, minimizes errors and enhances system reliability during dynamic MMG operations. Using HYCHOPSO framework, an accurate power sharing, voltage/frequency stability, seamless grid-to-island transition, and smooth resynchronization are achieved. This enhances the real application's reliability, flexibility, scalability and robustness. [ABSTRACT FROM AUTHOR]

The popularity of the average current mode (ACM) controlled boost type power factor correction (PFC) topologies is increasing due to their suitability for power quality problems. Proportional-Integral (PI) controller method is generally used in ACM controlled boost PFC circuits which include two controllers. However, the most important complexity of ACM controlled PFC circuits is tuning the coefficients of the PI controllers optimally. Therefore, this paper proposes the optimal tuning of PI coefficients used in ACM controlled boost PFC circuits using different meta-heuristics algorithms. First, the proposed ACM controller-based boost PFC topology is analyzed in MATLAB/Simulink software by using variable loads. Then the simulation results of the Cuckoo Optimization Algorithm (COA) based ACM controlled boost PFC converter are compared with the results determined via Ziegler-Nichols (ZN), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Imperialistic Competitive Algorithm (ICA), and Invasive Weed Optimization (IWO). Finally, the experimental verification of the topology has been done using a 600 W prototype and eZdsp F28335. As COA showed better results among other evolutionary algorithms used in this paper, we used COA parameters to observe the performance of the proposed tuning method. The experimental studies have been done under different load variations similar to the simulation studies. [ABSTRACT FROM AUTHOR]

Path-based clustering algorithms typically generate clusters by optimizing a benchmark function. Most optimization methods in clustering algorithms often offer solutions close to the general optimal value. This study achieves the global optimum value for the criterion function in a shorter time using the minimax distance, Maximum Spanning Tree "MST", and meta-heuristic algorithms, including Genetic Algorithm "GA" and Particle Swarm Optimization "PSO". The Fast Path-based Clustering "FPC" algorithm proposed in this paper can find cluster centers correctly in most datasets and quickly perform clustering operations. The FPC does this operation using MST, the minimax distance, and a new hybrid meta-heuristic algorithm in a few rounds of algorithm iterations. This algorithm can achieve the global optimal value, and the main clustering process of the algorithm has a computational complexity of O (k² × n). However, due to the complexity of the minimum distance algorithm, the total computational complexity is O (n²). Experimental results of FPC on synthetic datasets with arbitrary shapes demonstrate that the algorithm is resistant to noise and outliers and can correctly identify clusters of varying sizes and numbers. In addition, the FPC requires the number of clusters as the only parameter to perform the clustering process. A comparative analysis of FPC and other clustering algorithms in this domain indicates that FPC exhibits superior speed, stability, and performance. [ABSTRACT FROM AUTHOR]

Recently, Japan's discharge of wastewater from the Fukushima nuclear disaster into the ocean has attracted widespread attention. To effectively address the challenge of separating uranium, the focus is on finding a healthy and environmentally friendly way to adsorb uranium using biochar. In this paper, a BP neural network is combined with each of the four meta-heuristic algorithms, namely Particle Swarm Optimization (PSO), Differential Evolution (DE), Cheetah Optimization (CO) and Fick's Law Algorithm (FLA), to construct four prediction models for the uranium adsorption capacity in the treatment of radioactive wastewater with biochar: PSO-BP, DE-BP, CO-BP, FLA-BP. The coefficient of certainty (R2), error rate and CEC test set are used to judge the accuracy of the model based on the BP neural network. The results show that the Fick's Law Algorithm (FLA) has a better search ability and convergence speed than the other algorithms. The importance of the input parameters is quantitatively assessed and ranked using XGBoost in order to analyze which parameters have a greater impact on the predictions of the model, which indicates that the parameters with the greatest impact are the initial concentration of uranium (C0, mg/L) and the mass percentage of total carbon (C, %). To sum up, four prediction models can be applied to study the adsorption of uranium by biochar materials during actual experiments, and the advantage of Fick's Law Algorithm (FLA) is more obvious. The method of model prediction can significantly reduce the radiation risk caused by uranium to human health during the actual experiment and provide some reference for the efficient treatment of uranium wastewater by biochar. [ABSTRACT FROM AUTHOR]

In this paper, a permanent magnet synchronous machine (PMSM) with an auxiliary winding (AW) on the rotor is analyzed by two-dimensional approach. This PMSM with AW (AWPMSM) can be used in many applications such as propulsion system, aircraft and traction because it includes rotor flux control capability. First, the magnetic field in different parts of AWPMSM is calculated based on Maxwell equations. Then, as a consequence of the magnetic field, the torque components, including cogging, reluctance, electromagnetic and instantaneous torque are computed. Next, torque-speed characteristic has been investigated. This AWPMSM can be located in the flux weakening mode in two ways, first one is to attenuate the rotor field by changing the direction of the AW field and the other one is to adjust the armature current angle, both of them have been investigated. After that, the overload capability and temperature effects have been analyzed. Finally, using the meta-heuristic algorithms such as genetic algorithm, particle swarm optimization, differential evolution and teaching learn base optimization the dimensions of AWPMSM and the initial angle of the rotor are determined in such a way that the torque-to-volume ratio is maximized. The influences of the type of armature winding and the magnetization patterns have also been investigated. The results obtained by the two-dimensional method have been confirmed numerically. [ABSTRACT FROM AUTHOR]

Various metaheuristic algorithms inspired by nature have been designed to deal with a variety of practical optimization problems. As an excellent metaheuristic algorithm, the improved particle swarm optimization algorithm based on grouping (IPSO) has strong global search capabilities. However, it lacks a strong local search ability and the ability to solve constrained discrete optimization problems. This paper focuses on improving these two aspects of the IPSO algorithm. Based on IPSO, we propose an improved particle swarm optimization algorithm based on variable neighborhood search (VN-IPSO) and design a 0-1 integer programming solution with constraints. In the experiment, the performance of the VN-IPSO algorithm is fully tested and analyzed using 23 classic benchmark functions (continuous optimization), 6 knapsack problems (discrete optimization), and 10 CEC2017 composite functions (complex functions). The results show that the VN-IPSO algorithm wins 18 first places in the classic benchmark function test set, including 6 first places in the solutions for seven unimodal test functions, indicating a good local search ability. In solving the six knapsack problems, it wins four first places, demonstrating the effectiveness of the 0-1 integer programming constraint solution and the excellent solution ability of VN-IPSO in discrete optimization problems. In the test of 10 composite functions, VN-IPSO wins first place four times and ranks the first in the comprehensive ranking, demonstrating its excellent solving ability for complex functions. [ABSTRACT FROM AUTHOR]

Optimization challenges effectively mirror numerous complex real‐world problems. These are typically addressed using two methodologies: approximate methods and exact methods. Within the approximate category, meta‐heuristic methods are prominent. These algorithms employ systematic principles to avoid local optima or prevent their occurrence altogether. The evolution of computing technology has spurred the development of various meta‐heuristic algorithms designed for precise and expedient resolution of optimization issues. This paper conducts a comparative analysis of meta‐heuristic algorithms applied to structural optimization. It encompasses 11 distinct meta‐heuristic optimization algorithms: atomic orbital search algorithm, chaos game optimization, energy valley optimizer, firefly algorithm, genetic algorithm, gray wolf optimization, harmony search, material generation algorithm, particle swarm optimization, social network search, and whale optimization algorithm. The efficacy of these algorithms is assessed through their application to structural problems such as trusses, cantilever‐cracked beams, welded beams, tubular columns, and I‐shaped beams. Evaluation criteria include computational efficiency, accuracy, and statistical analysis based on reports among the 50 iterations. The findings confirm the effectiveness of the algorithms, with particle swarm optimization and social network search frequently delivering superior results across most problem sets. [ABSTRACT FROM AUTHOR]

The Marine Predators Algorithm (MPA) is a swarm intelligence algorithm developed based on the foraging behavior of the ocean's predators. This algorithm has drawbacks including, insufficient population diversity, leading to trapping in local optima and poor convergence. To mitigate these drawbacks, this paper introduces an enhanced MPA based on Adaptive Sampling with Maximin Distance Criterion (AM) and the horizontal and vertical crossover operators – i.e. Adaptive Crossover-based MPA (AC-MPA). The AM approach is used to generate diverse and well-distributed candidate solutions. Whereas the horizontal and vertical crossover operators maintain the population diversity during the search process. The performance of AC-MPA was tested using 51 benchmark functions from CEC2017, CEC2020, and CEC2022, with varying degrees of dimensionality, and the findings are compared with those of its basic version, variants, and numerous well-established metaheuristics. Additionally, 11 engineering optimization problems were utilized to verify the capabilities of the AC-MPA in handling real-world optimization problems. The findings clearly show that AC-MPA performs well in terms of its solution accuracy, convergence, and robustness. Furthermore, the proposed algorithm demonstrates considerable advantages in solving engineering problems, proving its effectiveness and adaptability. [ABSTRACT FROM AUTHOR]

Feature selection (FS) is a significant dimensionality reduction technique in machine learning and data mining that is adept at managing high-dimensional data efficiently and enhancing model performance. Metaheuristic algorithms have become one of the most promising solutions in FS owing to their powerful search capabilities as well as their performance. In this paper, the novel improved binary walrus optimizer (WO) algorithm utilizing the golden sine strategy, elite opposition-based learning (EOBL), and population regeneration mechanism (BGEPWO) is proposed for FS. First, the population is initialized using an iterative chaotic map with infinite collapses (ICMIC) chaotic map to improve the diversity. Second, a safe signal is obtained by introducing an adaptive operator to enhance the stability of the WO and optimize the trade-off between exploration and exploitation of the algorithm. Third, BGEPWO innovatively designs a population regeneration mechanism to continuously eliminate hopeless individuals and generate new promising ones, which keeps the population moving toward the optimal solution and accelerates the convergence process. Fourth, EOBL is used to guide the escape behavior of the walrus to expand the search range. Finally, the golden sine strategy is utilized for perturbing the population in the late iteration to improve the algorithm's capacity to evade local optima. The BGEPWO algorithm underwent evaluation on 21 datasets of different sizes and was compared with the BWO algorithm and 10 other representative optimization algorithms. The experimental results demonstrate that BGEPWO outperforms these competing algorithms in terms of fitness value, number of selected features, and F1-score in most datasets. The proposed algorithm achieves higher accuracy, better feature reduction ability, and stronger convergence by increasing population diversity, continuously balancing exploration and exploitation processes and effectively escaping local optimal traps. [ABSTRACT FROM AUTHOR]

The optimization and allocation of transport cost savings among stakeholders are two important issues that influence the satisfaction of information providers, drivers and passengers in ridesharing recommendation systems. For optimization issues, finding optimal solutions for nonconvex constrained discrete ridesharing optimization problems poses a challenge due to computational complexity. For the allocation of transport cost savings issues, the development of an effective method to allocate cost savings in ridesharing recommendation systems is an urgent need to improve the acceptability of ridesharing. The hybridization of different metaheuristic approaches has demonstrated its advantages in tackling the complexity of optimization problems. The principle of the hybridization of metaheuristic approaches is similar to a marriage of two people with the goal of having a happy ending. However, the effectiveness of hybrid metaheuristic algorithms is unknown a priori and depends on the problem to be solved. This is similar to a situation where no one knows whether a marriage will have a happy ending a priori. Whether the hybridization of the Firefly Algorithm (FA) with Particle Swarm Optimization (PSO) or Differential Evolution (DE) can work effectively in solving ridesharing optimization problems needs further study. Motivated by deficiencies in existing studies, this paper focuses on the effectiveness of hybrid metaheuristic algorithms for solving ridesharing problems based on the hybridization of FA with PSO or the hybridization of FA with DE. Another focus of this paper is to propose and study the effectiveness of a new method to allocate ridesharing cost savings to the stakeholders in ridesharing systems. The developed hybrid metaheuristic algorithms and the allocation method have been compared with examples of several application scenarios to illustrate their effectiveness. The results indicate that hybridizing FA with PSO creates a more efficient algorithm, whereas hybridizing FA with DE does not lead to a more efficient algorithm for the ridesharing recommendation problem. An interesting finding of this study is very similar to what happens in the real world: "Not all marriages have happy endings". [ABSTRACT FROM AUTHOR]

The techniques for solving complex optimization problems using nature inspired metaheuristic algorithms are widely accepted. Nature inspired methods use nature derived approaches to offer an efficient solution within polynomial time. This paper presents analytics of some of the significant nature inspired metaheuristic algorithms. It elaborates on the principles and concepts that are used in these algorithms representing their similarities, variations, and exceptions. The taxonomical classification and analytics presented in this paper list the nature derived phenomenon used to develop a wide variety of nature-inspired techniques. The algorithms are classified as per the type of agents used, search techniques, sub-optimization methods, type of constraints, and nature of problems. The survey comprehends the use of control parameters like exploration and convergence applicable to these algorithms and their domain specifications. The sources of nature inspiration are also presented with their variants. It establishes the analytics required to choose a specific nature-inspired heuristic algorithm for smart farming and related applications. Metaheuristic algorithms like Particle Swarm optimization, Ant colony optimization, Whale optimization, Firefly optimization, etc. have contributed significantly in assisting smart farming methods for better productivity of crops. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is constantly evolving. The variety of IoT applications has caused new demands to emerge on users' part and competition between computing service providers. On the one hand, an IoT application may exhibit several important criteria, such as deadline and runtime simultaneously, and it is confronted with resource limitations and high energy consumption on the other hand. This has turned to adopting a computing environment and scheduling as a fundamental challenge. To resolve the issue, IoT applications are considered in this paper as a workflow composed of a series of interdependent tasks. The tasks in the same workflow (at the same level) are subject to priorities and deadlines for execution, making the problem far more complex and closer to the real world. In this paper, a hybrid Particle Swarm Optimization and Simulated Annealing algorithm (PSO–SA) is used for prioritizing tasks and improving fitness function. Our proposed method managed the task allocation and optimized energy consumption and makespan at the fog-cloud environment nodes. The simulation results indicated that the PSO–SA enhanced energy and makespan by 5% and 9% respectively on average compared with the baseline algorithm (IKH-EFT). [ABSTRACT FROM AUTHOR]

In this paper, a novel Moth-Flame Optimization (MFO) algorithm, namely MFO algorithm enhanced by Multiple Improvement Strategies (MISMFO) is proposed for solving parameter optimization in Multi-Kernel Support Vector Regressor (MKSVR), and the MISMFO-MKSVR model is further employed to deal with the software effort estimation problems. In MISMFO, the logistic chaotic mapping is applied to increase initial population diversity, while the mutation and flame number phased reduction mechanisms are carried out to improve the search efficiency, as well the adaptive weight adjustment mechanism is used to accelerate convergence and balance exploration and exploitation. The MISMFO model is verified on fifteen benchmark functions and CEC 2020 test set. The results show that the MISMFO has advantages over other meta-heuristic algorithms and MFO variants in terms of convergence speed and accuracy. Additionally, the MISMFO-MKSVR model is tested by simulations on five software effort datasets and the results demonstrate that the proposed model has better performance in software effort estimation problem. The Matlab code of MISMFO can be found at https://github.com/loadstar1997/MISMFO. [ABSTRACT FROM AUTHOR]

This paper introduces a novel meta-heuristic algorithm named Rhinopithecus Swarm Optimization (RSO) to address optimization problems, particularly those involving high dimensions. The proposed algorithm is inspired by the social behaviors of different groups within the rhinopithecus swarm. RSO categorizes the swarm into mature, adolescent, and infancy individuals. Due to this division of labor, each category of individuals employs unique search methods, including vertical migration, concerted search, and mimicry. To evaluate the effectiveness of RSO, we conducted experiments using the CEC2017 test set and three constrained engineering problems. Each function in the test set was independently executed 36 times. Additionally, we used the Wilcoxon signed-rank test and the Friedman test to analyze the performance of RSO compared to eight well-known optimization algorithms: Dung Beetle Optimizer (DBO), Beluga Whale Optimization (BWO), Salp Swarm Algorithm (SSA), African Vultures Optimization Algorithm (AVOA), Whale Optimization Algorithm (WOA), Atomic Retrospective Learning Bare Bone Particle Swarm Optimization (ARBBPSO), Artificial Gorilla Troops Optimizer (GTO), and Harris Hawks Optimization (HHO). The results indicate that RSO exhibited outstanding performance on the CEC2017 test set for both 30 and 100 dimension. Moreover, RSO ranked first in both dimensions, surpassing the mean rank of the second-ranked algorithms by 7.69% and 42.85%, respectively. Across the three classical engineering design problems, RSO consistently achieves the best results. Overall, it can be concluded that RSO is particularly effective for solving high-dimensional optimization problems. [ABSTRACT FROM AUTHOR]

Brushless direct current (BLDC) motors are widely used in dynamic applications because of advantages such as high efficiency, wide speed range and low maintenance requirements. The classical Proportional-Integral (PI) control method is generally used for speed control of BLDC motor drivers. Although this method is easy to apply, the determined controller coefficients are generally constant and hence insufficient in dynamic changes. For this reason, methods that respond faster to dynamic changes, such as fuzzy-PI, have been proposed in the literature. Although the rule-based fuzzy controller increases its response ability to dynamic changes, determined rule-based coefficients affects the system performance completely. Therefore, the determination of the rule base values of the fuzzy controller is critical. In this paper, meta-heuristic Cuckoo Optimization Algorithm (COA) is proposed to determine the rule base values of the fuzzy controller for BLDC motor. Additionally, the rule-based table values of the fuzzy controller used for BLDC motor is determined using other meta-heuristic algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Imperialist Competitive Algorithm (ICA), Invasive Weed Optimization (IWO) and the results are compared. Finally, experimental studies for Pittman44 series BLDC motor are also carried out and the results are obtained. [ABSTRACT FROM AUTHOR]

The challenges associated with conventional methods of COVID-19 detection have prompted the exploration of alternative approaches, including the analysis of lung X-ray images. This paper introduces a novel algorithm designed to identify abnormalities in X-ray images indicative of COVID-19 by combining the maximally stable extremal regions (MSER) method with metaheuristic algorithms. The MSER method is efficient and effective under various adverse conditions, utilizing symmetry as a key property to detect regions despite changes in scaling or lighting. However, calibrating the MSER method is challenging. Our approach transforms this calibration into an optimization task, employing metaheuristic algorithms such as Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Firefly (FF), and Genetic Algorithms (GA) to find the optimal parameters for MSER. By automating the calibration process through metaheuristic optimization, we overcome the primary disadvantage of the MSER method. This innovative combination enables precise detection of abnormal regions characteristic of COVID-19 without the need for extensive datasets of labeled training images, unlike deep learning methods. Our methodology was rigorously tested across multiple databases, and the detection quality was evaluated using various indices. The experimental results demonstrate the robust capability of our algorithm to support healthcare professionals in accurately detecting COVID-19, highlighting its significant potential and effectiveness as a practical and efficient alternative for medical diagnostics and precise image analysis. [ABSTRACT FROM AUTHOR]

Summary: Metaheuristic algorithms have special effects in solving optimization problems in real life and have become the focus of researchers. The sparrow search algorithm (SSA) is a newly proposed swarm‐based metaheuristic algorithm that has shown excellent optimization performance. Although compared with other algorithms, SSA shows good performance, the original SSA algorithm still has problems such as weak optimization ability, leading to falling into the local optimum, and being unable to balance exploration and exploitation well. Therefore, this paper proposes an improved SSA using chaotic opposition‐based learning and hybrid updating rules (CHSSA). First, chaotic opposition‐based learning is proposed to improve the diversity of the population. Second, two strategies, including adaptive weights and spiral search, are adopted to update the position. Finally, to evaluate the performance of the proposed CHSSA, this paper uses 23 benchmark functions, IEEE CEC 2017 functions and 4 practical engineering optimization problems to evaluate the algorithm performance. The experimental results show that compared with other advanced optimization algorithms, CHSSA has the characteristics of fast convergence speed, high search accuracy, and strong robustness. [ABSTRACT FROM AUTHOR]

The prospect of the energy transition is exciting and sure to benefit multiple aspects of daily life. However, various challenges, such as planning, business models, and energy access are still being tackled. Energy Communities have been gaining traction in the energy transition, as they promote increased integration of Renewable Energy Sources (RESs) and more active participation from the consumers. However, optimization becomes crucial to support decision making and the quality of service for the effective functioning of Energy Communities. Optimization in the context of Energy Communities has been explored in the literature, with increasing attention to metaheuristic approaches. This paper contributes to the ongoing body of work by presenting the results of a benchmark between three classical metaheuristic methods—Differential Evolution (DE), the Genetic Algorithm (GA), and Particle Swarm Optimization (PSO)—and three more recent approaches—the Mountain Gazelle Optimizer (MGO), the Dandelion Optimizer (DO), and the Hybrid Adaptive Differential Evolution with Decay Function (HyDE-DF). Our results show that newer methods, especially the Dandelion Optimizer (DO) and the Hybrid Adaptive Differential Evolution with Decay Function (HyDE-DF), tend to be more competitive in terms of minimizing the objective function. In particular, the Hybrid Adaptive Differential Evolution with Decay Function (HyDE-DF) demonstrated the capacity to obtain extremely competitive results, being on average 3% better than the second-best method while boasting between around 2× and 10× the speed of other methods. These insights become highly valuable in time-sensitive areas, where obtaining results in a shorter amount of time is crucial for maintaining system operational capabilities. [ABSTRACT FROM AUTHOR]

To address the anti-swing issue of the payload in bridge cranes, Proportional–Integral–Derivative (PID) control is a commonly used method. However, parameter tuning of the PID controller relies on empirical knowledge and often leads to system overshoot. This paper proposes an Improved Sparrow Search Algorithm (ISSA) to optimize the gains of PID controllers, alleviating adverse effects on payload oscillation and trolley positioning during the operation of overhead cranes. First, tent map chaos mapping is introduced to initialize the sparrow population, enhancing the algorithm's global search capability. Then, by integrating sine and cosine concepts along with nonlinear learning factors, the updating mechanism of discoverer positions is dynamically adjusted, expediting the solving process. Finally, the Lévy flight strategy is employed to update follower positions, thereby enhancing the algorithm's local escape capability. Additionally, a fitness function containing overshoot penalties is proposed to address overshoot issues. Simulation results indicate that the overshoot rates of all algorithms remain less than 3%. Moreover, compared with the Sparrow Search Algorithm (SSA), Particle Swarm Optimization (PSO), Simulated Annealing (SA), and Whale optimization Algorithm (WOA), the optimized PID control system with the ISSA algorithm exhibits superior control performance and possesses certain robustness and adaptability. [ABSTRACT FROM AUTHOR]

The software-defined network paradigm, ensembled with a wireless sensor network, has emerged as a promising phenomenon to enable multi-tasking, re-configuration, and scalability. Termed the software-defined wireless sensor network (SDWSN), it divides the network into two planes: data and control. The data plane consists of software-defined sensor nodes (SDSN) that sense monitoring activity and generate data. On the other hand, the control plane has controller/control Nodes (CN) which collect data from SDSN, perform data aggregation, and then transmit toward the sink node. These CNs consume more amount of energy as compared to SDSNs as they perform multiple tasks. Following this scenario, this paper proposes an energy-efficient multi-objective optimization approach to solve the CNs placement problem through a meta-heuristic algorithm by considering the nodes' location, energy, and load distribution. This paper presents a particle swarm optimization-based controller placement and re-placement (PSO-CPR) algorithm for SDWSN. The PSO-CPR elects SDSNs to become CN based on their distance, residual energy, and capacity in the network. Moreover, the placement of a CN rotates within the cluster to avoid its failure and balance energy consumption. The simulation results show improved CN placement with respect to the state-of-the-art algorithms in terms of average delay by 23.5–37.4%, energy consumption by 18.6–32.6%, and probabilistic load distribution by 17.7–54.1%. Moreover, the comparative study also indicates that PSO-CPR achieve promising result by reducing packet loss by 14.4–27.5% and network re-clustering period by 32.3–68.3% and enhance the network lifetime by 22.6–42.5%. [ABSTRACT FROM AUTHOR]

The growth optimizer (GO) is a novel metaheuristic algorithm designed to tackle complex optimization problems. Despite its advantages of simplicity and high efficiency, GO often encounters localized stagnation when dealing with discretized, high-dimensional, and multi-constraint problems. To address these issues, this paper proposes an enhanced version of GO called CODGBGO. This algorithm incorporates three strategies to enhance its performance. Firstly, the Circle-OBL initialization strategy is employed to enhance the quality of the initial population. Secondly, an exploration strategy is implemented to improve population diversity and the algorithm's ability to escape local optimum traps. Finally, the exploitation strategy is utilized to enhance the convergence speed and accuracy of the algorithm. To validate the performance of CODGBGO, it is applied to solve the CEC2017, CEC2020, 18 feature selection problems, and 4 real engineering optimization problems. The experiments demonstrate that the novel CODGBGO algorithm effectively addresses the challenges posed by complex optimization problems, offering a promising approach. [ABSTRACT FROM AUTHOR]

The Snake Optimizer (SO) is an advanced metaheuristic algorithm for solving complicated real-world optimization problems. However, despite its advantages, the SO faces certain challenges, such as susceptibility to local optima and suboptimal convergence performance in cases involving discretized, high-dimensional, and multi-constraint problems. To address these problems, this paper presents an improved version of the SO, known as the Snake Optimizer using Sobol sequential nonlinear factors and different learning strategies (SNDSO). Firstly, using Sobol sequences to generate better distributed initial populations helps to locate the global optimum solution faster. Secondly, the use of nonlinear factors based on the inverse tangent function to control the exploration and exploitation phases effectively improves the exploitation capability of the algorithm. Finally, introducing learning strategies improves the population diversity and reduces the probability of the algorithm falling into the local optimum trap. The effectiveness of the proposed SNDSO in solving discretized, high-dimensional, and multi-constraint problems is validated through a series of experiments. The performance of the SNDSO in tackling high-dimensional numerical optimization problems is first confirmed by using the Congress on Evolutionary Computation (CEC) 2015 and CEC2017 test sets. Then, twelve feature selection problems are used to evaluate the effectiveness of the SNDSO in discretized scenarios. Finally, five real-world technical multi-constraint optimization problems are employed to evaluate the performance of the SNDSO in high-dimensional and multi-constraint domains. The experiments show that the SNDSO effectively overcomes the challenges of discretization, high dimensionality, and multi-constraint problems and outperforms superior algorithms. [ABSTRACT FROM AUTHOR]

This paper introduces a novel metaheuristic technique, a COOT‐based algorithm, to determine the optimal Distributed Generation (DG) allocation within a loop‐configured network. This method significantly narrows the optimization gap by leveraging a COOT‐based algorithm, ensuring accelerated convergence and resultant global optima. The core incentive for employing this technique is to substantially mitigate power loss, curtail voltage deviation, and bolster system stability in a loop distribution network. To attain optimal outcomes, the elaborated COOT and improved grey wolf optimization improved grey wolf optimization (IGWO) algorithms were executed on IEEE bus‐33 and 69 mesh distribution networks (MDNs) under varying power factors. The derived mathematical results effectively underscore accomplishing the stipulated objectives: a marked reduction in voltage deviation and power loss coupled with an augmentation in system stability. Notably, at unity, incorporating DGs resulted in a paramount reduction in power loss, attaining a decrease of 78% and 85% for bus‐33 and 69 MDNs, respectively. Moreover, an impressive decrease in power loss by 94% and 98% was observed at the optimal power factor for both MDNs. A comparative evaluation of the results accentuates that the proposed COOT and IGWO algorithms eclipse other documented research in performance, showcasing superior efficiency on a techno‐economic basis. [ABSTRACT FROM AUTHOR]

Studies indicate cardiac arrhythmia is one of the leading causes of death in the world. The risk of a stroke may be reduced when an irregular and fast heart rate is diagnosed. Since it is non-invasive, electrocardiograms are often used to detect arrhythmias. Human data input may be error-prone and time-consuming because of these limitations. For early detection of heart rhythm problems, it is best to use deep learning models. In this paper, a hybrid bio-inspired algorithm has been proposed by combining whale optimization (WOA) with adaptive particle swarm optimization (APSO). The WOA is a recently developed meta-heuristic algorithm. APSO is used to increase convergence speed. When compared to conventional optimization methods, the two techniques work better together. MIT-BIH dataset has been utilized for training, testing and validating this model. The recall, accuracy, and specificity are used to measure efficiency of the proposed method. The efficiency of the proposed method is compared with state-of-art methods and produced 98.25 % of accuracy. [ABSTRACT FROM AUTHOR]

Featured Application: The proposed method can be utilized to determine whether an aircraft encountered turbulence during or after flight, rather than relying on EDR estimation to ascertain turbulence encounters. By integrating swarm intelligence and machine learning and adopting a data-driven approach to turbulence identification, the method addresses previous challenges encountered in turbulence identification, thereby enhancing the efficacy of aviation safety. This approach demonstrates a certain degree of applicability in improving aviation safety. Turbulence is a significant operational aviation safety hazard during all phases of flight. There is an urgent need for a method of airline turbulence identification in aviation systems to avoid turbulence hazards to aircraft during flight. Integrating flight data and machine learning significantly enhances the efficacy of turbulence identification. Nevertheless, present studies encounter issues including unstable model performance, challenges in data feature extraction, and parameter optimization. Hence, it is imperative to propose a superior approach to enhance the accuracy of turbulence identification along airline. The paper presents a combined swarm intelligence and machine learning model based on data mining for identifying airline turbulence. Based on the theory of swarm-intelligence-based optimization algorithm, the optimal parameters of Categorical Boosting (CatBoost) are obtained by introducing the whale optimization algorithm (WOA), and the corresponding WOA-CatBoost fusion model is established. Then, the Recursive Feature Elimination algorithm (RFE) is used to eliminate the data with lower feature weights, extract the effective features of the data, and the combination with the WOA brings robust optimization effects, whereby the accuracy of CatBoost increased by 11%. The WOA-CatBoost model can perform accurate turbulence identification from QAR data, comparable to that with established EDR approaches and outperforms traditional machine learning models. This discovery highlights the effectiveness of combining swarm intelligence and machine learning algorithms in turbulence monitoring systems to improve aviation safety. [ABSTRACT FROM AUTHOR]

In today's dynamic business landscape, organizations heavily rely on cloud computing to leverage the power of virtualization and resource sharing. Service composition plays a vital role in cloud computing, combining multiple cloud services to fulfill complex user requests. Service composition in cloud computing presents several challenges. These include service heterogeneity, dynamic service availability, QoS (Quality of Service) constraints, and scalability issues. Traditional approaches often struggle to handle these challenges efficiently, leading to suboptimal resource utilization and poor service performance. This work presents a fuzzy-based strategy for composing cloud services to overcome these obstacles. The fact that service composition is NP-hard has prompted the use of a range of metaheuristic algorithms in numerous papers. Therefore, Particle Swarm Optimization (PSO) has been applied in this paper to solve the problem. Implementing a fuzzy-based PSO for service composition requires defining the fuzzy membership functions and rules based on the specific service domain. Once the fuzzy logic components are established, they can be integrated into the PSO algorithm. The simulation results have shown the high efficiency of the proposed method in decreasing the latency, cost, and response time. [ABSTRACT FROM AUTHOR]

This paper explores the advantages of the Two Degree of Freedom Proportional-Integral-Derivative (2DOF_PID) controller in tracking the reference signal and rejecting the disturbance signal at the same time. Three types of metaheuristic optimization algorithms are employed for tuning the controller's parameters which are Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Dragonfly Algorithm (DA). These three algorithms have in common that they combine the exploratory concept (global search) and the exploitative concept (local search) in order to reach the optimal global solution. The effectiveness of these algorithms was taken advantage of to improve the performance of the control system that contains the controller. Second and third order plants were adopted for the purpose of testing, evaluating, and comparing the performance of the control system. This aim was accomplished by using each of the optimization algorithms for each plant. The simulation results showed the superiority of the DA in terms of obtaining the lowest value of the Integral Absolute Error (IAE) as the cost function. [ABSTRACT FROM AUTHOR]

Drilling operation has a direct impact on the quality of the production. Insufficiently controlling the cutting force during the drilling process leads to the risk of early drill failure. Typically, the selection of the drilling parameters is determined based on machining-data handbook where the experience and skill of the operator are required. This paper presents an optimal framework to control the cutting force of the drilling process. A mathematical model that captures complex drilling dynamics between cutting force and feed rate based on system identification is used. Then, a Proportional-Integral-Derivative (PID) controller is proposed to control the cutting force. Taking advantage of up-to-date swam-based optimization technique, an Enhance Bat Algorithm (EBA) approach is used to tune the design variables of the PID controller based on the Integral Absolute Error (IAE) criterion. The results are compared with another two swam optimization, the Particle Swarm Optimization (PSO) and the Whale Optimization Algorithm (WOA). The comparison reveals that EBA can give better results in terms of improving time domain specifications and reducing error performance indices. [ABSTRACT FROM AUTHOR]

The high dimensionality of hyperspectral images (HSIs) brings significant redundancy to data processing. Band selection (BS) is one of the most commonly used dimensionality reduction (DR) techniques, which eliminates redundant information between bands while retaining a subset of bands with a high information content and low noise. The wild horse optimizer (WHO) is a novel metaheuristic algorithm widely used for its efficient search performance, yet it tends to become trapped in local optima during later iterations. To address these issues, an enhanced wild horse optimizer (IBSWHO) is proposed for HSI band selection in this paper. IBSWHO utilizes Sobol sequences to initialize the population, thereby increasing population diversity. It incorporates Cauchy mutation to perturb the population with a certain probability, enhancing the global search capability and avoiding local optima. Additionally, dynamic random search techniques are introduced to improve the algorithm search efficiency and expand the search space. The convergence of IBSWHO is verified on commonly used nonlinear test functions and compared with state-of-the-art optimization algorithms. Finally, experiments on three classic HSI datasets are conducted for HSI classification. The experimental results demonstrate that the band subset selected by IBSWHO achieves the best classification accuracy compared to conventional and state-of-the-art band selection methods, confirming the superiority of the proposed BS method. [ABSTRACT FROM AUTHOR]

Feature selection (FS) is a crucial preprocessing step that aims to eliminate irrelevant and redundant features, reduce the dimensionality of the feature space, and enhance clustering efficiency and effectiveness. FS is categorized as NP-Hard due to the high number of existing solutions. Various metaheuristic methods have been developed to address the FS problem, yielding promising results. Particularly, particle swarm optimization (PSO), an evolutionary computing (EC) approach guided by swarm intelligence, has gained widespread adoption owing to its implementation simplicity and potential for global search. This paper analyzes several variants of PSO algorithms and introduces a new FS method called HPSO. The proposed approach utilizes an asynchronously adaptive inertia weight and an improved constriction factor. Additionally, it incorporates a chaotic map and a MAD fitness function with a feature count penalty to tackle the clustering FS problem. The efficiency of the developed method is evaluated against the genetic algorithm (GA) and well-known variants of PSO algorithms, including PSOs with fixed inertia weights, PSOs with improved inertia weights, PSOs with fixed constriction factors, PSOs with improved constriction factors, PSOs with adaptive inertia weights, and PSO's includes advanced learning exemplars and sophisticated structure topologies. This paper assesses two different reference text data sets, Reuters-21578 and Webkb. In comparison with competitive methods, the proposed HPSO method achieves higher clustering precision and selects a more informative feature set. [ABSTRACT FROM AUTHOR]

Quantum-behaved Particle Swarm Optimization (QPSO) is a meta-heuristic optimization algorithm, which is widely used in many research fields and practical problems due to its flexibility and low computational cost. However, the existing QPSO algorithms and their variants still have problems such as insufficient search capabilities, lack of adaptivity, and prone to stagnation. This paper proposes a novel ensemble algorithm, ACQPSOW-HGS, based on Quantum-behaved Particle Swarm Optimization (QPSO) and Hunger Games Search (HGS). By combining three improvements and introducing three hybrid strategies, our algorithm has made a comprehensive development, effectively improving the stability and solution accuracy in a large number of test functions and the parameter identification application, which is superior compared with many existing algorithms. First, we design the Weibull distribution random number generation operator, the distance-guided adaptive control technique, and the chaotic update mechanism to deal with the weak randomness, insufficient adaptability, and susceptibility to stagnation of the original QPSO algorithm, respectively. Integrating the above improvements, ACQPSOW is proposed as an improved variant of QPSO. Second, the proposed ensemble algorithm ACQPSOW-HGS is built on ACQPSOW and HGS and combined with specific hybrid strategies to add population diversity and improve search efficiency, including the Selection-Crossover-Mutation mechanism, the elite local search mechanism, and the information exchange mechanism. Finally, the experiments, on 23 benchmark functions and the IEEE CEC 2017 test suite, demonstrate that ACQPSOW-HGS outperforms comparison algorithms in terms of convergence speed and solution accuracy through non-parametric statistical tests. Moreover, ACQPSOW-HGS was applied to the fractional-order hyper-chaotic financial system for parameter identification to illustrate the applicability and robustness in solving real-world problems. [ABSTRACT FROM AUTHOR]

The relentless evolution of communication systems, driven by the demands of 5G and the impending 6G networks, necessitates heightened data rates and spectral efficiency. orthogonal frequency division multiplexing (OFDM), a form of multicarrier modulation employed in multi-input multioutput (MIMO) systems, stands as a pivotal technology. Yet, OFDM grapples with challenges, notably the peak-to-average power ratio (PAPR) issue. Selective mapping (SLM) has been a favored technique for mitigating PAPR in OFDM, albeit challenged by computational complexities in its pursuit of discovering optimal phase factors. This paper pioneers a transformative approach by integrating metaheuristic algorithms genetic algorithm (GA), particle swarm optimization (PSO), and the innovative fireworks algorithm (FWA) into SLM for PAPR reduction while minimizing computational complexity. Simulation results not only affirm the efficacy of SLM-based techniques but also spotlight the potential of metaheuristic algorithms in addressing PAPR challenges in modern communication systems. The study transcends single-antenna systems, extending to MIMOOFDM systems based on WiMAX standards, validating the efficacy of these techniques in multi-antenna configurations. Crucially, the FWA, proposed for the first time in this paper, emerges as a robust candidate, striking an enviable balance between computational efficiency and performance, achieving a notable PAPR reduction with a favorable search number. [ABSTRACT FROM AUTHOR]

The dung beetle optimization (DBO) algorithm, a swarm intelligence-based metaheuristic, is renowned for its robust optimization capability and fast convergence speed. However, it also suffers from low population diversity, susceptibility to local optima solutions, and unsatisfactory convergence speed when facing complex optimization problems. In response, this paper proposes the multi-strategy improved dung beetle optimization algorithm (MDBO). The core improvements include using Latin hypercube sampling for better population initialization and the introduction of a novel differential variation strategy, termed "Mean Differential Variation", to enhance the algorithm's ability to evade local optima. Moreover, a strategy combining lens imaging reverse learning and dimension-by-dimension optimization was proposed and applied to the current optimal solution. Through comprehensive performance testing on standard benchmark functions from CEC2017 and CEC2020, MDBO demonstrates superior performance in terms of optimization accuracy, stability, and convergence speed compared with other classical metaheuristic optimization algorithms. Additionally, the efficacy of MDBO in addressing complex real-world engineering problems is validated through three representative engineering application scenarios namely extension/compression spring design problems, reducer design problems, and welded beam design problems. [ABSTRACT FROM AUTHOR]

Association rule mining utilizing metaheuristic algorithms is a prominent area of study in the field of data mining. However, when working with extensive data, conventional metaheuristic algorithms exhibit limited search efficiency and face challenges in deriving high-quality rules in multi-objective association rule mining. In order to tackle this issue, a novel approach called the adaptive Weibull distribution sparrow search algorithm is introduced. This algorithm leverages the adaptive Weibull distribution to improve the traditional sparrow search algorithm's capability to escape local optima and enhance convergence during different iterations. Secondly, an enhancement search strategy and a multidirectional learning strategy are introduced to expand the search range of the population. This paper empirically evaluates the proposed method under real datasets and compares it with other leading methods by using three association rule metrics, namely, support, confidence, and lift, as the fitness function. The experimental results show that the quality of the obtained association rules is significantly improved when dealing with datasets of different sizes. [ABSTRACT FROM AUTHOR]

In addressing the challenges associated with low convergence accuracy and unstable optimization results in the original gazelle optimization algorithm (GOA), this paper proposes a novel approach incorporating chaos mapping termed multi-strategy particle swarm optimization with gazelle optimization algorithm (MPSOGOA). In the population initialization stage, segmented mapping is integrated to generate a uniformly distributed high-quality population which enhances diversity, and global perturbation of the population is added to improve the convergence speed in the early iteration and the convergence accuracy in the late iteration. By combining particle swarm optimization (PSO) and GOA, the algorithm leverages individual experiences of gazelles, which improves convergence accuracy and stability. Tested on 35 benchmark functions, MPSOGOA demonstrates superior performance in convergence accuracy and stability through Friedman tests and Wilcoxon signed-rank tests, surpassing other metaheuristic algorithms. Applied to engineering optimization problems, including constrained implementations, MPSOGOA exhibits excellent optimization performance. [ABSTRACT FROM AUTHOR]

To overcome the disadvantages of premature convergence and easy trapping into local optimum solutions, this paper proposes an improved particle swarm optimization algorithm (named NDWPSO algorithm) based on multiple hybrid strategies. Firstly, the elite opposition-based learning method is utilized to initialize the particle position matrix. Secondly, the dynamic inertial weight parameters are given to improve the global search speed in the early iterative phase. Thirdly, a new local optimal jump-out strategy is proposed to overcome the "premature" problem. Finally, the algorithm applies the spiral shrinkage search strategy from the whale optimization algorithm (WOA) and the Differential Evolution (DE) mutation strategy in the later iteration to accelerate the convergence speed. The NDWPSO is further compared with other 8 well-known nature-inspired algorithms (3 PSO variants and 5 other intelligent algorithms) on 23 benchmark test functions and three practical engineering problems. Simulation results prove that the NDWPSO algorithm obtains better results for all 49 sets of data than the other 3 PSO variants. Compared with 5 other intelligent algorithms, the NDWPSO obtains 69.2%, 84.6%, and 84.6% of the best results for the benchmark function ( f 1 - f 13 ) with 3 kinds of dimensional spaces (Dim = 30,50,100) and 80% of the best optimal solutions for 10 fixed-multimodal benchmark functions. Also, the best design solutions are obtained by NDWPSO for all 3 classical practical engineering problems. [ABSTRACT FROM AUTHOR]

In this research paper, the intelligent learning abilities of the gray wolf optimization (GWO), multi-verse optimization (MVO), moth fly optimization, particle swarm optimization (PSO), and whale optimization algorithm (WOA) metaheuristic techniques and the response surface methodology (RSM) has been studied in the prediction of the mechanical properties of self-healing concrete. Bio-concrete technology stimulated by the concentration of bacteria has been utilized as a sustainable structural concrete for the future of the built environment. This is due to the recovery tendency of the concrete structures after noticeable structural failures. However, it requires a somewhat expensive exercise and technology to create the medium for the growth of the bacteria needed for this self-healing ability. The method of data gathering, analysis and intelligent prediction has been adopted to propose parametric relationships between the bacteria usage and the concrete performance in terms of strength and durability. This makes is cheaper to design self-healing concrete structures based on the optimized mathematical relationships and models proposed from this exercise. The performance of the models was tested by using the coefficient of determination (R2), root mean squared errors, mean absolute errors, mean squared errors, variance accounted for and the coefficient of error. At the end of the prediction protocol and model performance evaluation, it was found that the classified metaheuristic techniques outclassed the RSM due their ability to mimic human and animal genetics of mutation. Furthermore, it can be finally remarked that the GWO outclassed the other methods in predicting the concrete slump (Sl) with R2 of 0.998 and 0.989 for the train and test, respectively, the PSO outclassed the rest in predicting the flexural strength with R2 of 0.989 and 0.937 for train and test, respectively and the MVO outclassed the others in predicting the compressive strength with R2 of 0.998 and 0.958 for train and test, respectively. [ABSTRACT FROM AUTHOR]

This paper addresses optimization problems subject to an objective function's evaluation‐number constraint arbitrarily given in advance. These problems are practical for black‐box objective functions which cost much money and/or time per evaluation because money and/or time constraints are typically applicable in real‐world projects. This paper suggests a new solution strategy that can be used to adapt metaheuristics to evaluation‐number constraints arbitrarily given in advance. The proposed strategy updates the setting parameters of a target metaheuristic algorithm for every generation by solving a fixed small‐size optimization problem related to the evaluation‐number constraint. It has the advantage of being insensitive to the effects of dimensionality increases in principle. The effectiveness of the proposed strategy is confirmed by numerical experiments with two well‐known metaheuristics, i.e., the particle swarm optimization algorithm and the differential evolution algorithm. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

A key component of industrial applications is the direct current (DC) motor. Hence, due to their superior features and performance, brushless DC (BLDC) motors are more suitable for fractional kilowatt motor's applications. Albeit, for the purpose of controlling the speed of BLDC motor easily, it is quite difficult for obtaining the best controlling performance through the use of the conventional approaches of tuning. In order to search the proportional--integral--derivative (PID) tuning parameters optimally for the different controllers taken into consideration, the use of modern bio-inspired metaheuristic technique called ant colony optimization (ACO) algorithm is employed. This paper particularly discusses and presents on the tuning parameters of kp; ki; and kd. The performance of traditional controller and novel approach is compared, analyzed, and presented. BLDC motor is buildup in MATLAB, and the usage, importance, efficiency, and strength of the proposed approach are validated against traditional tuning methods. The obtained result shows better performance of the proposed system with the aid of the proposed controllers for different speed trajectories of the drive when compared with that of the classical PID controllers. ACO seems to be one of the most effective tuning techniques of PID controllers. This research has significant impact on modern control applications. [ABSTRACT FROM AUTHOR]

Detecting hate speech automatically in social media forensics has emerged as a highly challenging task due to the complex nature of language used in such platforms. Currently, several methods exist for classifying hate speech, but they still suffer from ambiguity when differentiating between hateful and offensive content and they also lack accuracy. The work suggested in this paper uses a combination of the Whale Optimization Algorithm (WOA) and Particle Swarm Optimization (PSO) to adjust the weights of two Multi-Layer Perceptron (MLPs) for neutrosophic sets classification. During the training process of the MLP, the WOA is employed to explore and determine the optimal set of weights. The PSO algorithm adjusts the weights to optimize the performance of the MLP as fine-tuning. Additionally, in this approach, two separate MLP models are employed. One MLP is dedicated to predicting degrees of truth membership, while the other MLP focuses on predicting degrees of false membership. The difference between these memberships quantifies uncertainty, indicating the degree of indeterminacy in predictions. The experimental results indicate the superior performance of our model compared to previous work when evaluated on the Davidson dataset. [ABSTRACT FROM AUTHOR]

This paper addresses the critical role of supercapacitors as energy storage systems with a specific focus on their modeling and identification. The lack of a standardized and efficient method for identifying supercapacitor parameters has a definite effect on widespread adoption of supercapacitors, especially in high-power density applications like electric vehicle regenerative braking. The study focuses on parameterizing the Zubieta model for supercapacitors, which involves identifying seven parameters using a hybrid metaheuristic gradient-based optimization (MGBO) approach. The effectiveness of the MGBO method is compared to the existing particle swarm optimization (PSO) and to the following algorithms proposed and developed in this work: 'modified MGBO' (M-MGBO) and two PSO variations—one combining PSO and M-MGBO and the other incorporating a local escaping operator (LCEO) with PSO. Metaheuristic- and gradient-based algorithms are both affected by problems associated with locally optimal results and with issues related to enforcing constraints/boundaries on solution values. This work develops the above-mentioned innovations to the MGBO and PSO algorithms for addressing such issues. Rigorous experimentation considering various types of input excitation provides results indicating that hybrid PSO-MGBO and PSO-LCEO outperform traditional PSO, showing improvements of 51% and 94%, respectively, while remaining comparable to M-MGBO. These hybrid approaches effectively estimate Zubieta model parameters. The findings highlight the potential of hybrid optimization strategies in enhancing precision and effectiveness in supercapacitor model parameterization. [ABSTRACT FROM AUTHOR]

One of the main concepts in group technology (GT) is the cellular manufacturing system (CMS) with three main problems of cell formation (CF), cell layout (CL), and cell scheduling (CS). This paper studies the cell layout problem (CLP), aiming to find the optimal layout of machines within each cell (intracellular layout) and the optimal layout of cells in each workshop (intercellular layout). To adapt to reality, the dimensions of the cells and machines (inside each cell) were considered unequal, and also the cells and machines could rotate. We believe that a cellular layout that assumes unequal dimensions of the cells and machines can be used for batch production. This kind of production has a wide variety of low to medium demand. Furthermore, a cellular layout can be applied in CMSs and also in noncontinuous industries that have a job shop layout. Our main contribution is considering the possibility of rotating the cells and machines inside the cells. For this purpose, a mixed nonlinear programming model was developed to solve the CLP with the minimum cost of intracellular and intercellular material flows. The proposed nonlinear model was first converted into a linear model, and then a problem was generated and solved with GAMS software to validate the resulting linear model. This model finds the best layout of cells within the workshop and the best layout of machines inside each cell. Then, because of the NP-hardness of the CLP and the fact that even exact methods cannot solve large-scale examples in an acceptable computational time, an imperialist competitive algorithm (ICA) was designed and used to solve the problem. To evaluate the efficiency of the proposed algorithm, its numerical results in small dimensions were compared with the results of GAMS software. In large dimensions, 30 random problems were created, and the results of ICA were compared with the results of the particle swarm optimization (PSO) algorithm and genetic algorithm (GA). Finally, the parameters of the three meta-heuristic algorithms were set by the Taguchi method. Numerical results indicated that ICA was superior to both the PSO algorithm and GA. It could also achieve efficient solutions in a shorter computational time. [ABSTRACT FROM AUTHOR]

Infrared imagery is extensively used in defense, remote sensing and medical applications. While the infrared images have many advantages over RGB images, the details in these images are usually blurred which in turn leads to some difficulties for human operators. In this paper, a new method based on Laplacian of Gaussian scale-space and local variance is presented to improve the visual quality of the infrared images. At the first step, the Gaussian scale-space is constructed by convolving the original image with different Gaussian kernels. Then, the two-dimensional Laplacian kernels are convolved with the Gaussian scale-space to achieve details with both positive as well as negative contrasts. The weighted details are added to the original image to deblur the dim areas. At the final step, to increase the dynamic range of the image and have better visual quality, the local variance of the image is also added to the output of the previous step. Since finding optimum weighting coefficients is a difficult task empirically, here, we use a population-based meta-heuristic optimization algorithm called particle swarm optimization (PSO) to find the optimum values for weighting coefficient values. Beside qualitative comparison, Structural Similarity (SSIM) and second-derivative-like measure of enhancement (SDME) are used to quantitatively investigate the images quality. The proposed method outperforms the baseline algorithms in both qualitative and quantitative perspectives. [ABSTRACT FROM AUTHOR]