Search

This document is an editorial for a special issue of the journal AIMS Mathematics on the topic of discrete optimization. The issue includes 21 papers covering a range of subjects, including molecular trees, network systems, variational inequality problems, scheduling, image restoration, spectral clustering, integral equations, convex functions, graph products, optimization algorithms, air quality prediction, humanitarian planning, inertial methods, neural networks, transportation problems, emotion identification, fixed-point problems, structural engineering design, single machine scheduling, and ensemble learning. The papers present new theoretical results, algorithms, and applications in these areas. The guest editor expresses gratitude to the journal staff and reviewers and hopes that readers will find inspiration for their own research. [Extracted from the article]

The paper [[10]] considers an assignment problem and some modifications which can be converted to routing, distribution, or scheduling problems. This special issue of I Algorithms i is dedicated to recent developments of scheduling algorithms and new applications. References 1 Werner F., Burtseva L., Sotskov Y. Special Issue on Algorithms for Scheduling Problems. For this problem, a hybrid metaheuristic algorithm is presented which combines a genetic algorithm with a so-called spotted hyena optimization algorithm. [Extracted from the article]

A supply chain is an order of activities engaged which circulates, assembles and handles the products to move the benefits from a dealer under the control of the last customer. It is an interconnected compound network controlled by supply and demand. Cyber security in SCM is one of the segment of its estimates of protection which primarily gives attention in managing the essential virtual protection which comprises of system software of information technology. In the existing system, cloud services must need extra applications and assistances to locate, govern and protect data which initiate extra supply chain contributors. The manufacturing process data will mislead the manufacturing process in this system based on errors which are done manually. To Store and Maintain data in a protected manner, most algorithms such as DES (Data Encryption Standard) algorithm has disadvantages and threats which seems to be an upper hand for the hackers who are working to steal the data all around. In this paper, for securing the private and secret data, we applied and executed AES (Advanced Encryption Standard) algorithm and MD5 (Message-Digest algorithm 5) in supply chain management. We apply PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyzes) approach in domain of supply chain management, data security, and cyber security to screen the various methods and algorithms which are published in various journal papers and to select a unique and best approach to be used in supply chain management and its security. This method is basically a kind of literature survey to select a best topic for doing a project or a research paper. [ABSTRACT FROM AUTHOR]

This paper shows the implementation of the Moth–Flame Optimization algorithm in a Cascade-H multilevel inverter with five and seven levels to determine the optimal switching sequence of the inverter's semiconductor devices. The algorithm was coded in Matlab software, and the obtained switching sequences were implemented in a Cascade-H multilevel inverter laboratory prototype, where the output voltage waveform was obtained using a digital oscilloscope. The experimental Total Harmonic Distortion was obtained using a power quality analyzer. The experimental results show the improvement of the Total Harmonic Distortion in the voltage output. These results were compared with other papers in the literature with different metaheuristic methods concerning the same modulation. These findings demonstrate the feasibility of employing the Moth–Flame Optimization Algorithm to significantly reduce the Total Harmonic Distortion, obtaining a lower value than most analyzed papers. [ABSTRACT FROM AUTHOR]

The advent of the Internet of Things (IoT) has revolutionised our interaction with the environment, facilitating seamless connections among sensors, actuators, and humans. Efficient task scheduling stands as a cornerstone in maximising resource utilisation and ensuring timely task execution in IoT systems. The implementation of efficient task scheduling methodologies can yield substantial enhancements in productivity and cost-effectiveness for IoT infrastructures. To that end, this paper presents the IoT-mist bat-inspired algorithm (IMBA), designed specifically to optimise resource allocation in IoT environments. IMBA's efficacy lies in its ability to elevate user service quality through enhancements in task completion rates, load distribution, network utilisation, processing time, and power efficiency. Through comparative analysis, IMBA demonstrates superiority over traditional methods, such as fuzzy logic and round-robin algorithms, across all performance metrics. [ABSTRACT FROM AUTHOR]

Software usability is usually used in reference to the hierarchical software usability model by researchers and is an important aspect of user experience and software quality. Thus, evaluation of software usability is an essential parameter for managing and regulating a software. However, it has been difficult to establish a precise evaluation method for this problem. A large number of usability factors have been suggested by many researchers, each covering a set of different factors to increase the degree of user friendliness of a software. Therefore, the selection of the correct determining features is of paramount importance. This paper proposes an innovative metaheuristic algorithm for the selection of most important features in a hierarchical software model. A hierarchy-based usability model is an exhaustive interpretation of the factors, attributes, and its characteristics in a software at different levels. This paper proposes a modified version of grey wolf optimisation algorithm (GWO) termed as modified grey wolf optimization (MGWO) algorithm. The mechanism of this algorithm is based on the hunting mechanism of wolves in nature. The algorithm chooses a number of features which are then applied to software development life cycle models for finding out the best among them. The outcome of this application is also compared with the conventional grey wolf optimization algorithm (GWO), modified binary bat algorithm (MBBAT), modified whale optimization algorithm (MWOA), and modified moth flame optimization (MMFO). The results show that MGWO surpasses all the other relevant optimizers in terms of accuracy and produces a lesser number of attributes equal to 8 as compared to 9 in MMFO and 12 in MBBAT and 19 in MWOA. [ABSTRACT FROM AUTHOR]

Fog computing aims to mitigate data communication delay by deploying fog nodes to provide servers in the proximity of users and offload resource-hungry tasks that would otherwise be sent to distant cloud servers. In this paper, we propose an effective fog device deployment algorithm based on a new metaheuristic algorithm–search economics–to solve the optimization problem for the deployment of fog computing systems. The term "effective" in this paper refers to that the developed algorithm can achieve better performance in terms of metrics such as lower latency and less resource usage. Compared with conventional metaheuristic algorithms, the proposed algorithm is unique in that it first divides the solution space into a set of regions to increase search diversity of the search and then allocates different computational resources to each region according to its potential. To verify the effectiveness of the proposed algorithm, we compare it with several classical fog computing deployment algorithms. The simulation results indicate that the proposed algorithm provides lower network latency and higher quality of service than the other deployment algorithms evaluated in this study. [ABSTRACT FROM AUTHOR]

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

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]

Load forecasting is a research hotspot in academia; in the context of new power systems, the prediction and determination of load reserve capacity is also important. In order to adapt to new forms of power systems, a day-ahead automatic generation control (AGC) reserve capacity demand prediction method based on the Fourier transform and the attention mechanism combined with a bidirectional long and short-term memory neural network model (Attention-BiLSTM) optimized by an improved whale optimization algorithm (IWOA) is proposed. Firstly, based on the response time, Fourier transform is used to refine the distinction between various types of load reserve demand, and the power of the AGC reserve band is calculated using Parseval's theorem to obtain the reserve capacity demand sequence. The maximum mutual information coefficient method is used to explore the relevant influencing factors of the AGC reserve sequence concerning the data characteristics of the AGC reserve sequence. Then, the historical daily AGC reserve demand sequences with relevant features are input into the Attention-BiLSTM prediction model, and the improved whale algorithm is used to automatically find the optimal hyperparameters to obtain better prediction results. Finally, the arithmetic simulation results show that the model proposed in this paper has the best prediction performance with the upper (0.8810) and lower (0.6651) bounds of the coefficient of determination ( R 2 ) higher than the other models, and it has the smallest mean absolute percentage error ( M A P E ) and root mean square error ( R M S E ). [ABSTRACT FROM AUTHOR]

Conventionally, researchers have favored the model-based control scheme for controlling gantry crane systems. However, this method necessitates a substantial investment of time and resources in order to develop an accurate mathematical model of the complex crane system. Recognizing this challenge, the current paper introduces a novel data-driven control scheme that relies exclusively on input and output data. Undertaking a couple of modifications to the conventional marine predators algorithm (MPA), random average marine predators algorithm (RAMPA) with tunable adaptive coefficient to control the step size (CF) has been proposed in this paper as an enhanced alternative towards fine-tuning data-driven multiple-node hormone regulation neuroendocrine-PID (MnHR-NEPID) controller parameters for the multi-input–multi-output (MIMO) gantry crane system. First modification involved a random average location calculation within the algorithm's updating mechanism to solve the local optima issue. The second modification then introduced tunable CF that enhanced search capacity by enabling users' resilience towards attaining an offsetting level of exploration and exploitation phases. Effectiveness of the proposed method is evaluated based on the convergence curve and statistical analysis of the fitness function, the total norms of error and input, Wilcoxon's rank test, time response analysis, and robustness analysis under the influence of external disturbance. Comparative findings alongside other existing metaheuristic-based algorithms confirmed excellence of the proposed method through its superior performance against the conventional MPA, particle swarm optimization (PSO), grey wolf optimizer (GWO), moth-flame optimization (MFO), multi-verse optimizer (MVO), sine-cosine algorithm (SCA), salp-swarm algorithm (SSA), slime mould algorithm (SMA), flow direction algorithm (FDA), and the formally published adaptive safe experimentation dynamics (ASED)-based methods. [ABSTRACT FROM AUTHOR]

The parameter extraction of Photovoltaic (PV) cell and module is a necessary to simulate and evaluate the performance of the PV system. The parameter extraction is a complex and challenging task due to its non-linear nature. Researchers are used several metaheuristic algorithms to solve the non-linear problem of parameter extraction. However, the demand for most accurate and reliable methods is increasing to get precise estimation of parameters. In this paper, a novel hybrid optimization algorithm is proposed based on the Spotted-Hyena optimization (SHO) and Ant Lion Optimization (ALO). The hybrid method is called as Spotted Hyena - Ant Lion (SH-AL) optimization. The optimization algorithm is applied in two stages. In stage 1, essential parameters are identified and extracted using SHO and passed to stage 2. In stage 2, identified parameters are optimized using ALO for accurate model of PV cell. Different type of PV cells such as thin film, mono and multi crystalline are examined under various irradiance conditions to extract the parameters. The proposed algorithm is validated by comparing the results with other algorithms and proposed algorithm is proved its superiority. [ABSTRACT FROM AUTHOR]

In the current complex cyber environment, malicious attacks take various forms and constantly change. To overcome the problems of low detection efficiency and high false alarm rate in traditional intrusion detection techniques, a new method of network intrusion detection combining the variable scale chaos bat (MSCB) algorithm and BP neural network (BPNN) called MSCB-BPNN is proposed in this paper. In the proposed approach, a new MSCB algorithm is proposed to improve the BPNN by optimizing its thresholds and weights so as to prevent it from falling into local optimum. To verify the practical classification performance of the proposed method for intrusion detection, some experiments are carried out. In these experiments, the proposed approach is compared with BPNN, SA-BPNN, and GA-BPNN on the benchmark intrusion detection datasets KDD cup 99 and UNSW-NB15. The experimental results show that the accuracy of the proposed method can reach 99.4% and 99.8% accuracy on the KDD cup 99 training and test sets, respectively, while 89.0% and 93.9% accuracy on the UNSW-NB15 training and test sets, respectively, which are higher than BPNN, SA-BPNN, and GA-BPNN. Furthermore, the precision and recall ratio of MSCB-BPNN are also superior to other approaches. Thus, the proposed model has significant advantages over BPNN, SA-BPNN, and GA-BPNN methods. [ABSTRACT FROM AUTHOR]

Many-objective optimization problems are today ever more common. The decomposition-based approach stands out among the evolutionary algorithms used for their solution, with MOEA/D and its variations playing significant roles. MOEA/D variations seek to improve weight vector definition, improve the dynamic adjustment of weight vectors during the evolution process, improve the evolutionary operators, use alternative decomposition methods, and hybridize with other metaheuristics, among others. Although an essential topic for the success of MOEA/D depends on how well the weight vectors are defined when decomposing the problem, not as much research has been performed on this topic as on the others. This paper proposes using a new mathematical object called augmented covering arrays (ACAs) that enable a better sampling of interactions of M objectives using the least number of weight vectors based on an interaction level (strength), defined a priori by the user. The proposed method obtains better results, measured in inverted generational distance, using small to medium populations (up to 850 solutions) of 30 to 100 objectives over DTLZ and WFG problems against the traditional weight vector definition used by MOEA/D-DE and results obtained by NSGA-III. Other MOEA/D variations can include the proposed approach and thus improve their results. [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]

This research introduces innovative approaches to enhance intrusion detection systems (IDSs) by addressing critical challenges in existing methods. Various machine-learning techniques, including nature-inspired metaheuristics, Bayesian algorithms, and swarm intelligence, have been proposed in the past for attribute selection and IDS performance improvement. However, these methods have often fallen short in terms of detection accuracy, detection rate, precision, and F-score. To tackle these issues, the paper presents a novel hybrid feature selection approach combining the Bat metaheuristic algorithm with the Residue Number System (RNS). Initially, the Bat algorithm is utilized to partition training data and eliminate irrelevant attributes. Recognizing the Bat algorithm's slower training and testing times, RNS is incorporated to enhance processing speed. Additionally, principal component analysis (PCA) is employed for feature extraction. In a second phase, RNS is excluded for feature selection, allowing the Bat algorithm to perform this task while PCA handles feature extraction. Subsequently, classification is conducted using naive bayes, and k-Nearest Neighbors. Experimental results demonstrate the remarkable effectiveness of combining RNS with the Bat algorithm, achieving outstanding detection rates, accuracy, and F-scores. Notably, the fusion approach doubles processing speed. The findings are further validated through benchmarking against existing intrusion detection methods, establishing their competitiveness. [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]

Service-oriented architectures have become increasingly prevalent in internet application design, leading to a proliferation of services. Managing and composing these web services pose a classic non-deterministic polynomial-time hard problem. The web service composition problem (WSCP) based on the input–output model (IOM) involves the automatic composition of services without relying on a pre-defined workflow. Multi-objective evolutionary algorithms offer a solution that goes beyond simple weighted average calculations for quality of service, catering to diverse user needs in service composition. This paper introduces a multi-objective heuristic algorithm based on the SPEA2 called MSPEA2+. Specific modifications are incorporated in MSPEA2+ to support the breeding, crossover, and mutation processes, tailored for IOM-based WSCP. Validating the algorithm performance through finding suitable combination results for each task in the WSC-08 dataset, our experiments showed that MSPEA2+ has better iterative efficiency than existing multi-objective methods and outperforms them in terms of quality of service. MSPEA2+ achieved the lowest IGD value across all seven tasks, an improvement of 81.7% IGD value compared with the SPEA2 algorithm in WSC-0804. [ABSTRACT FROM AUTHOR]

The Fennec Fox algorithm (FFA) is a new meta-heuristic algorithm that is primarily inspired by the Fennec fox's ability to dig and escape from wild predators. Compared with other classical algorithms, FFA shows strong competitiveness. The "No free lunch" theorem shows that an algorithm has different effects in the face of different problems, such as: when solving high-dimensional or more complex applications, there are challenges such as easily falling into local optimal and slow convergence speed. To solve this problem with FFA, in this paper, an improved Fenna fox algorithm DEMFFA is proposed by adding sin chaotic mapping, formula factor adjustment, Cauchy operator mutation, and differential evolution mutation strategies. Firstly, a sin chaotic mapping strategy is added in the initialization stage to make the population distribution more uniform, thus speeding up the algorithm convergence speed. Secondly, in order to expedite the convergence speed of the algorithm, adjustments are made to the factors of the formula whose position is updated in the first stage, resulting in faster convergence. Finally, in order to prevent the algorithm from getting into the local optimal too early and expand the search space of the population, the Cauchy operator mutation strategy and differential evolution mutation strategy are added after the first and second stages of the original algorithm update. In order to verify the performance of the proposed DEMFFA, qualitative analysis is carried out on different test sets, and the proposed algorithm is tested with the original FFA, other classical algorithms, improved algorithms, and newly proposed algorithms on three different test sets. And we also carried out a qualitative analysis of the CEC2020. In addition, DEMFFA is applied to 10 practical engineering design problems and a complex 24-bar truss topology optimization problem, and the results show that the DEMFFA algorithm has the potential to solve complex problems. [ABSTRACT FROM AUTHOR]

The Manta Ray Foraging Optimization Algorithm (MRFO) is a metaheuristic algorithm for solving real-world problems. However, MRFO suffers from slow convergence precision and is easily trapped in a local optimal. Hence, to overcome these deficiencies, this paper proposes an Improved MRFO algorithm (IMRFO) that employs Tent chaotic mapping, the bidirectional search strategy, and the Levy flight strategy. Among these strategies, Tent chaotic mapping distributes the manta ray more uniformly and improves the quality of the initial solution, while the bidirectional search strategy expands the search area. The Levy flight strategy strengthens the algorithm's ability to escape from local optimal. To verify IMRFO's performance, the algorithm is compared with 10 other algorithms on 23 benchmark functions, the CEC2017 and CEC2022 benchmark suites, and five engineering problems, with statistical analysis illustrating the superiority and significance of the difference between IMRFO and other algorithms. The results indicate that the IMRFO outperforms the competitor optimization algorithms. [ABSTRACT FROM AUTHOR]

With the rapid development of the geometric modeling industry and computer technology, the design and shape optimization of complex curve shapes have now become a very important research topic in CAGD. In this paper, the Hybrid Artificial Hummingbird Algorithm (HAHA) is used to optimize complex composite shape-adjustable generalized cubic Ball (CSGC–Ball, for short) curves. Firstly, the Artificial Hummingbird algorithm (AHA), as a newly proposed meta-heuristic algorithm, has the advantages of simple structure and easy implementation and can quickly find the global optimal solution. However, there are still limitations, such as low convergence accuracy and the tendency to fall into local optimization. Therefore, this paper proposes the HAHA based on the original AHA, combined with the elite opposition-based learning strategy, PSO, and Cauchy mutation, to increase the population diversity of the original algorithm, avoid falling into local optimization, and thus improve the accuracy and rate of convergence of the original AHA. Twenty-five benchmark test functions and the CEC 2022 test suite are used to evaluate the overall performance of HAHA, and the experimental results are statistically analyzed using Friedman and Wilkerson rank sum tests. The experimental results show that, compared with other advanced algorithms, HAHA has good competitiveness and practicality. Secondly, in order to better realize the modeling of complex curves in engineering, the CSGC–Ball curves with global and local shape parameters are constructed based on SGC–Ball basis functions. By changing the shape parameters, the whole or local shape of the curves can be adjusted more flexibly. Finally, in order to make the constructed curve have a more ideal shape, the CSGC–Ball curve-shape optimization model is established based on the minimum curve energy value, and the proposed HAHA is used to solve the established shape optimization model. Two representative numerical examples comprehensively verify the effectiveness and superiority of HAHA in solving CSGC–Ball curve-shape optimization problems. [ABSTRACT FROM AUTHOR]

The ant system (AS) and scheduling problem are well-known concepts in literature. Ant algorithms have been known to be an effective tool for solving combinatorial optimization problems. Elitist AS (EAS), rank-based AS (RAS), ant colony system (ACS), and max-min AS (MMAS) are the variants of the AS algorithm; they are triggered by the different ways of updating the pheromone trail τ, computing the visibility η, and/or other parameters in the basic AS model. The main contribution of this article is twofold. First, the basic AS and its controlled parameters are presented, the key variants of the ant algorithms are explained, and major changes of each variant from the basic model are tracked. Second, sixty papers are collected between 2015 and 2020 based on a search strategy for tracking the implementation of different AS variants in solving scheduling problems. Numerous findings based on a statistical analysis of the collected papers are reported and discussed. This study will allow the researcher to understand the essence of the ant algorithm, recognize the fundamental differences in its five systems, and determine how each of them can be implemented. Tracking a sample of articles that apply an ant algorithm for a specific case study gives researchers new ideas on how to adjust the original model to fit their problem. [ABSTRACT FROM AUTHOR]

The vehicle routing problem with time windows (VRPTW) is a classical optimization problem. There have been many related studies in recent years. At present, many studies have generally analyzed this problem on the two-dimensional plane, and few studies have explored it on spherical surfaces. In order to carry out research related to the distribution of goods by unmanned vehicles and unmanned aerial vehicles, this study carries out research based on the situation of a three-dimensional sphere and proposes a three-dimensional spherical VRPTW model. All of the customer nodes in this problem were mapped to the three-dimensional sphere. The chimp optimization algorithm is an excellent intelligent optimization algorithm proposed recently, which has been successfully applied to solve various practical problems and has achieved good results. The chimp optimization algorithm (ChOA) is characterized by its excellent ability to balance exploration and exploitation in the optimization process so that the algorithm can search the solution space adaptively, which is closely related to its outstanding adaptive factors. However, the performance of the chimp optimization algorithm in solving discrete optimization problems still needs to be improved. Firstly, the convergence speed of the algorithm is fast at first, but it becomes slower and slower as the number of iterations increases. Therefore, this paper introduces the multiple-population strategy, genetic operators, and local search methods into the algorithm to improve its overall exploration ability and convergence speed so that the algorithm can quickly find solutions with higher accuracy. Secondly, the algorithm is not suitable for discrete problems. In conclusion, this paper proposes an improved chimp optimization algorithm (MG-ChOA) and applies it to solve the spherical VRPTW model. Finally, this paper analyzes the performance of this algorithm in a multi-dimensional way by comparing it with many excellent algorithms available at present. The experimental result shows that the proposed algorithm is effective and superior in solving the discrete problem of spherical VRPTW, and its performance is superior to that of other algorithms. [ABSTRACT FROM AUTHOR]

The location planning of relief distribution centres (DCs) is crucial in humanitarian logistics as it directly influences the disaster response and service to the affected victims. In light of the critical role of facility location in humanitarian logistics planning, the study proposes a two-stage relief distribution location problem. The first stage of the model determines the minimum number of relief DCs, and the second stage find the optimal location of these DCs to minimize the total cost. To address a more realistic situation, restrictions are imposed on the coverage area and capacity of each DCs. In addition, for optimally solving this complex NP-hard problem, a novel two-phase algorithm with exploration and exploitation phase is developed in the paper. The first phase of the algorithm i.e., exploration phase identifies a near-optimal solution while the second phase i.e. exploitation phase enhances the solution quality through a close circular proximity investigation. Furthermore, the comparative analysis of the proposed algorithm with other well-known algorithms such as genetic algorithm, pattern search, fmincon, multistart and hybrid heuristics is also reported and computationally tested from small to large data sets. The results reveal that the proposed two-phase algorithm is more efficient and effective when compared to the conventional metaheuristic methods. [ABSTRACT FROM AUTHOR]

Online platforms and social networking have increased in the contemporary years. They are now a major news source worldwide, leading to the online proliferation of Fake News (FNs). These FNs are alarming because they fundamentally reshape public opinion, which may cause customers to leave these online platforms, threatening the reputations of several organizations and industries. This rapid dissemination of FNs makes it imperative for automated systems to detect them, encouraging many researchers to propose various systems to classify news articles and detect FNs automatically. In this paper, a Fake News Detection (FND) methodology is presented based on an effective IBAVO-AO algorithm, which stands for hybridization of African Vultures Optimization (AVO) and Aquila Optimization (AO) algorithms, with an extreme gradient boosting Tree (Xgb-Tree) classifier. The suggested methodology involves three main phases: Initially, the unstructured FNs dataset is analyzed, and the essential features are extracted by tokenizing, encoding, and padding the input news words into a sequence of integers utilizing the GLOVE approach. Then, the extracted features are filtered using the effective Relief algorithm to select only the appropriate ones. Finally, the recovered features are used to classify the news items using the suggested IBAVO-AO algorithm based on the Xgb-Tree classifier. Hence, the suggested methodology is distinguished from prior models in that it performs automatic data pre-processing, optimization, and classification tasks. The proposed methodology is carried out on the ISOT-FNs dataset, containing more than 44 thousand multiple news articles divided into truthful and fake. We validated the proposed methodology's reliability by examining numerous evaluation metrics involving accuracy, fitness values, the number of selected features, Kappa, Precision, Recall, F1-score, Specificity, Sensitivity, ROC_AUC, and MCC. Then, the proposed methodology is compared against the most common meta-heuristic optimization algorithms utilizing the ISOT-FNs. The experimental results reveal that the suggested methodology achieved optimal classification accuracy and F1-score and successfully categorized more than 92.5% of news articles compared to its peers. This study will assist researchers in expanding their understanding of meta-heuristic optimization algorithms applications for FND. [ABSTRACT FROM AUTHOR]

Whenever a new supervised machine learning (ML) algorithm or solution is developed, it is imperative to evaluate the predictive performance it attains for diverse datasets. This is done in order to stress test the strengths and weaknesses of the novel algorithms and provide evidence for situations in which they are most useful. A common practice is to gather some datasets from public benchmark repositories for such an evaluation. But little or no specific criteria are used in the selection of these datasets, which is often ad-hoc. In this paper, the importance of gathering a diverse benchmark of datasets in order to properly evaluate ML models and really understand their capabilities is investigated. Leveraging from meta-learning studies evaluating the diversity of public repositories of datasets, this paper introduces an optimization method to choose varied classification and regression datasets from a pool of candidate datasets. The method is based on maximum coverage, circular packing, and the meta-heuristic Lichtenberg Algorithm for ensuring that diverse datasets able to challenge the ML algorithms more broadly are chosen. The selections were compared experimentally with a random selection of datasets and with clustering by k-medoids and proved to be more effective regarding the diversity of the chosen benchmarks and the ability to challenge the ML algorithms at different levels. [ABSTRACT FROM AUTHOR]

The Artificial Electric Field Algorithm (AEFA) stands out as a physics-inspired metaheuristic, drawing inspiration from Coulomb's law and electrostatic force; however, while AEFA has demonstrated efficacy, it can face challenges such as convergence issues and suboptimal solutions, especially in high-dimensional problems. To overcome these challenges, this paper introduces a modified version of AEFA, named mAEFA, which leverages the capabilities of Lévy flights, simulated annealing, and the Adaptive s-best Mutation and Natural Survivor Method (NSM) mechanisms. While Lévy flights enhance exploration potential and simulated annealing improves search exploitation, the Adaptive s-best Mutation and Natural Survivor Method (NSM) mechanisms are employed to add more diversity. The integration of these mechanisms in AEFA aims to expand its search space, enhance exploration potential, avoid local optima, and achieve improved performance, robustness, and a more equitable equilibrium between local intensification and global diversification. In this study, a comprehensive assessment of mAEFA is carried out, employing a combination of quantitative and qualitative measures, on a diverse range of 29 intricate CEC'17 constraint benchmarks that exhibit different characteristics. The practical compatibility of the proposed mAEFA is evaluated on five engineering benchmark problems derived from the civil, mechanical, and industrial engineering domains. Results from the mAEFA algorithm are compared with those from seven recently introduced metaheuristic algorithms using widely adopted statistical metrics. The mAEFA algorithm outperforms the LCA algorithm in all 29 CEC'17 test functions with 100% superiority and shows better results than SAO, GOA, CHIO, PSO, GSA, and AEFA in 96.6%, 96.6%, 93.1%, 86.2%, 82.8%, and 58.6% of test cases, respectively. In three out of five engineering design problems, mAEFA outperforms all the compared algorithms, securing second place in the remaining two problems. Results across all optimization problems highlight the effectiveness and robustness of mAEFA compared to baseline metaheuristics. The suggested enhancements in AEFA have proven effective, establishing competitiveness in diverse optimization problems. [ABSTRACT FROM AUTHOR]

In numerous scientific disciplines and practical applications, addressing optimization challenges is a common imperative. Nature-inspired optimization algorithms represent a highly valuable and pragmatic approach to tackling these complexities. This paper introduces Dendritic Growth Optimization (DGO), a novel algorithm inspired by natural branching patterns. DGO offers a novel solution for intricate optimization problems and demonstrates its efficiency in exploring diverse solution spaces. The algorithm has been extensively tested with a suite of machine learning algorithms, deep learning algorithms, and metaheuristic algorithms, and the results, both before and after optimization, unequivocally support the proposed algorithm's feasibility, effectiveness, and generalizability. Through empirical validation using established datasets like diabetes and breast cancer, the algorithm consistently enhances model performance across various domains. Beyond its working and experimental analysis, DGO's wide-ranging applications in machine learning, logistics, and engineering for solving real-world problems have been highlighted. The study also considers the challenges and practical implications of implementing DGO in multiple scenarios. As optimization remains crucial in research and industry, DGO emerges as a promising avenue for innovation and problem solving. [ABSTRACT FROM AUTHOR]

Metaheuristics are commonly employed as a means of solving many distinct kinds of optimization problems. Several natural-process-inspired metaheuristic optimizers have been introduced in the recent years. The convergence, computational burden and statistical relevance of metaheuristics should be studied and compared for their potential use in future algorithm design and implementation. In this paper, eight different variants of dragonfly algorithm, i.e. classical dragonfly algorithm (DA), 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) are applied to solve four industrial chemical process optimization problems. A fuzzy multi-criteria decision making tool in the form of fuzzy-measurement alternatives and ranking according to compromise solution (MARCOS) is adopted to ascertain the relative rankings of the DA variants with respect to computational time, Friedman's rank based on optimal solutions and convergence rate. Based on the comprehensive testing of the algorithms, it is revealed that DADE, QGDA and classical DA are the top three DA variants in solving the industrial chemical process optimization problems under consideration. [ABSTRACT FROM AUTHOR]

Metaheuristic algorithms, widely applied across various domains due to their simplicity and strong optimization capabilities, play a crucial role in problem-solving. While the Aquila Optimizer is recognized for its effectiveness, it often exhibits slow convergence rates and susceptibility to local optima in certain scenarios. To address these concerns, this paper introduces an enhanced version, termed Tent-enhanced Aquila Optimizer (TEAO). TEAO incorporates the Tent chaotic map to initialize the Aquila population, promoting a more uniform distribution within the solution space. To balance exploration and exploitation, novel formulas are proposed, accelerating convergence while ensuring precision. The effectiveness of the TEAO algorithm is validated through a comprehensive comparison with 14 state-of-the-art algorithms using 23 classical benchmark test functions. Additionally, to assess the practical feasibility of the approach, TEAO is applied to six constrained engineering problems and benchmarked against the performance of the same 14 algorithms. All experimental results consistently demonstrate that TEAO outperforms other advanced algorithms in terms of solution quality and stability, establishing it as a more competitive choice for optimization tasks. [ABSTRACT FROM AUTHOR]

This paper proposes a novel nature-inspired meta-heuristic algorithm, named Snow Geese Algorithm. It is inspired by the migratory behavior of snow geese and emulates the distinctive "Herringbone" and "Straight Line" shaped flight patterns observed during their migration. The algorithm is structured into three main phases for benchmark testing. In the first phase, the Snow Geese Algorithm's numerical results are compared with those of several classical meta-heuristic algorithms using the same test functions and original data from these algorithms. In the second phase, in order to minimize potential variations during the comparison, all algorithms undergo evaluation on a standardized testing platform. In the third phase, this paper applies the Snow Geese Algorithm to solve four widely recognized engineering optimization problems: the tubular column design, piston lever optimization design, reinforced concrete beam design and car side impact design. These real-world engineering problems serve as test cases to assess Snow Geese Algorithm problem-solving capabilities. The primary objective of the Snow Geese Algorithm is to provide an alternative perspective for tackling complex optimization problems. Please note that the complete source code for the Snow Geese Algorithm is publicly available at https://github.com/stones3421/SGA-project. • Snow Geese Algorithm: A novel meta-heuristic approach inspired by snow geese flight behavior, tackling optimization problems. • Two-stage Strategy: Mimics snow geese exploration and exploitation patterns, setting it apart from traditional methods. • Performance Evaluation: Multiple algorithms are assessed on different problems. • Algorithm Features: Experimental results validate the effectiveness of the Snow Geese Algorithm. • Practical Applications: Snow Geese Algorithm shows potential in engineering optimization, aiding accurate decision-making. [ABSTRACT FROM AUTHOR]

Population-based metaheuristics can be seen as a set of agents that smartly explore the space of solutions of a given optimization problem. These agents are commonly governed by movement operators that decide how the exploration is driven. Although metaheuristics have successfully been used for more than 20 years, performing rapid and high-quality parameter control is still a main concern. For instance, deciding the proper population size yielding a good balance between quality of results and computing time is constantly a hard task, even more so in the presence of an unexplored optimization problem. In this paper, we propose a self-adaptive strategy based on the on-line population balance, which aims for improvements in the performance and search process on population-based algorithms. The design behind the proposed approach relies on three different components. Firstly, an optimization-based component which defines all metaheuristic tasks related to carry out the resolution of the optimization problems. Secondly, a learning-based component focused on transforming dynamic data into knowledge in order to influence the search in the solution space. Thirdly, a probabilistic-based selector component is designed to dynamically adjust the population. We illustrate an extensive experimental process on large instance sets from three well-known discrete optimization problems: Manufacturing Cell Design Problem, Set covering Problem, and Multidimensional Knapsack Problem. The proposed approach is able to compete against classic, autonomous, as well as IRace-tuned metaheuristics, yielding interesting results and potential future work regarding dynamically adjusting the number of solutions interacting on different times within the search process. [ABSTRACT FROM AUTHOR]

Nowadays, there is a great deal of interest in the development of practical optimization models and intelligent solution algorithms for solving disassembly-line balancing problems. Based on the importance of energy efficiency of product disassembly and the trend for green remanufacturing, this paper develops a new optimization model for the energy-efficient disassembly-line balancing problem where the goal is to minimize the energy consumption generated during the disassembly-line operations. Since the proposed model is a complex optimization problem known as NP-hard, this study develops an improved metaheuristic algorithm based on the water cycle algorithm as a recently developed successful metaheuristic inspired by the natural water cycle phenomena of diversion, rainfall, confluence, and infiltration operations. A local search operator is added to the main algorithm to improve its performance. The proposed algorithm is validated by the exact solver and compared with other state-of-the-art and recent metaheuristic algorithms. A case study in a turbine reducer with different parameters is solved to show the applicability of this paper. Finally, our results confirm the high performance of the proposed improved water cycle algorithm and the efficiency of our sensitivity analyses during some sensitivity analyses. [ABSTRACT FROM AUTHOR]

In recent years, an increasing number of population-based metaheuristic algorithms have been proposed to solve the flexible job-shop scheduling problem (FJSP) according to its practicality and complexity. Most of these algorithms are single-population-based and hence are very susceptible to becoming trapped in local optimal values. Thus, multipopulation methods are widely used to improve these algorithms, resulting in multipopulation algorithms. These multipopulation algorithms have been widely studied in the context of single-layer complex networks recently. However, coupled networks used to control two (or more) algorithms simultaneously to get a better algorithm are always ignored in literature. Therefore, in this paper, using coupled scale-free networks (with different scaling exponents) to control the genetic algorithm (GA) and the invasive weed optimization (IWO) simultaneously, a multipopulation GA/IWO with coupled scale-free networks (MPGAIWO-SF) is proposed to solve FJSP. Then, we study how some parameters (e.g., subpopulation size, subpopulation number, and scaling exponent) of MPGAIWO-SF affect its performance, which is also ignored in literature. The simulation results illustrate that (1) the performance of MPGAIWO-SF is significantly improved compared with that of GA and IWO; (2) as the subpopulation size increases, the performance of MPGAIWO-SF first becomes better and then remains almost unchanged; (3) as the subpopulation number increases, the performance of MPGAIWO-SF first becomes better and then decreases rapidly; and (4) the performance of MPGAIWO-SF becomes worse slightly as the scaling exponent decreases. Finally, solving more FJSP instances, the MPGAIWO-SF with optimized parameters is compared with other related algorithms to verify its effectiveness. [ABSTRACT FROM AUTHOR]

This paper introduces a new variant of the Bees Algorithm (BA) called Bees Algorithm with 2-parameter (BA2), which is a population-based metaheuristic algorithm designed to solve continuous and combinatorial optimisation problems. The proposed algorithm simplified the BA's parameters by combining exploration and exploitation strategies while preserving the algorithm's core principles to efficiently search for optimal solutions. The paper provides a detailed description of the algorithm's core principles and its application to two engineering problems, the air-cooling system design (ACSD) and the printed circuit board assembly sequence optimisation (PASO). The results show that BA2 outperforms previous versions of the basic BA in terms of convergence speed and solution quality. However, the authors acknowledge that further research is needed to test the scalability and generalisability of the algorithm to larger and more diverse optimisation problems. Overall, this paper provides valuable insights into the potential of metaheuristics for solving real-world optimisation problems. [ABSTRACT FROM AUTHOR]

The conventional crow search (CS) algorithm is a swarm-based metaheuristic algorithm that has fewer parameters, is easy to apply to problems, and is utilized in various fields. However, it has a disadvantage, as it is easy for it to fall into local minima by relying mainly on exploitation to find approximations. Therefore, in this paper, we propose the advanced crow search (ACS) algorithm, which improves the conventional CS algorithm and solves the global optimization problem. The ACS algorithm has three differences from the conventional CS algorithm. First, we propose using dynamic A P (awareness probability) to perform exploration of the global region for the selection of the initial population. Second, we improved the exploitation performance by introducing a formula that probabilistically selects the best crows instead of randomly selecting them. Third, we improved the exploration phase by adding an equation for local search. The ACS algorithm proposed in this paper has improved exploitation and exploration performance over other metaheuristic algorithms in both unimodal and multimodal benchmark functions, and it found the most optimal solutions in five engineering problems. [ABSTRACT FROM AUTHOR]

The phasmatodea population evolution algorithm (PPE) is a recently proposed meta-heuristic algorithm based on the evolutionary characteristics of the stick insect population. The algorithm simulates the features of convergent evolution, population competition, and population growth in the evolution process of the stick insect population in nature and realizes the above process through the population competition and growth model. Since the algorithm has a slow convergence speed and falls easily into local optimality, in this paper, it is mixed with the equilibrium optimization algorithm to make it easier to avoid the local optimum. Based on the hybrid algorithm, the population is grouped and processed in parallel to accelerate the algorithm's convergence speed and achieve better convergence accuracy. On this basis, we propose the hybrid parallel balanced phasmatodea population evolution algorithm (HP_PPE), and this algorithm is compared and tested on the CEC2017, a novel benchmark function suite. The results show that the performance of HP_PPE is better than that of similar algorithms. Finally, this paper applies HP_PPE to solve the AGV workshop material scheduling problem. Experimental results show that HP_PPE can achieve better scheduling results than other algorithms. [ABSTRACT FROM AUTHOR]

The competition in the telecommunications industry is mainly reflected in the competition for the number of key customers. Therefore, how to effectively prevent the loss of large customers is a problem that many telecommunications companies are very concerned about. This paper establishes a customer churn prediction model based on the improved bat algorithm (IBA) optimized extreme learning machine (ELM), with the purpose of discovering potential churn customers in time and taking measures to retain them in advance. This paper uses the IBA to optimize the initial random weights of the ELM, so as to improve the prediction accuracy of the ELM. To overcome the shortcomings of the bat algorithm (BA) that the convergence speed is too slow in the early stage and difficult to converge in the later stage, this paper introduces the inertia weight into the speed update formula. To improve the population diversity and local search ability of the BA, this paper adds a chaotic local search method to balance the global search ability of the BA in the early stage. To control the search range of the BAs in the later stage, the lane flight formula is introduced into the position update formula to speed up the convergence speed of the algorithm. Then according to the result of function optimization, it can be seen that IBA has a significant improvement in convergence speed and accuracy. Finally, IBA was used to optimize ELM, and a customer churn prediction model was established. The empirical results show that the predictive model proposed in this paper can effectively identify lost customers and lay a foundation for improving the competitiveness of the telecommunications industry in the future. [ABSTRACT FROM AUTHOR]

In existing meta-heuristic algorithms, population initialization forms a huge part towards problem optimization. These calculations can impact variety and combination to locate a productive ideal arrangement. Especially, for perceiving the significance of variety and intermingling, different specialists have attempted to improve the presentation of meta-heuristic algorithms. Particle Swarm Optimization (PSO) algorithm is a populace-based, shrewd stochastic inquiry strategy that is motivated by the inherent honey bee swarm food search mechanism. Population initialization is an indispensable factor in the PSO algorithm. To improve the variety and combination factors, rather than applying the irregular circulation for the introduction of the populace, semi-arbitrary successions are more helpful. This examination presents a thorough overview of the different PSO initialization approaches which are dependent on semi-arbitrary successions systems. In this precise review, the best in class in the populace instatement is uncovered. The procedures are classified by utilizing a theoretical model that parts the cycle of populace introduction into two phases: that is, right now expressly or certainly utilized for reinstatement in every single present approach. The deliberate investigation unveils the potential examination zones of populace introduction and, furthermore, research holes, despite the fact that the fundamental center is to give the headings to future upgrade and advancement around there. This paper gives a deliberate study identified with this calculated model for the cutting edge of exploration, which is talked about in the predefined writing to date. The study is envisioned to be useful in examining the PSO algorithm in detail for the specialist. Likewise, the paper finds the proficiency of numerous quasi-random sequences (QRS) based on initialization approaches by looking at their exhibition analyzed for sixteen notable benchmark test problems. [ABSTRACT FROM AUTHOR]

Meta-heuristic algorithms have the advantages of resilience, global optimization capacity, and coding flexibility, making them helpful in tackling difficult optimization issues. The enhanced wind-driven optimization (CHTWDO) that was proposed in this paper coupled the chaotic map approach and the hyperbolic tangent with the T-distribution mutation method. The initial air particles are evenly distributed in the system space through a tent mapping strategy. Meanwhile, the variation probability of the hyperbolic tangent model and the T-distribution variation method are used to improve the comprehensive performance of the algorithm. In this way, the global search accuracy and the ability of avoiding the extreme value of the algorithm can be taken into account. Combining the three strategies, CHTWDO had higher global search accuracy and a stronger ability to jump out of local extremum. Comparing with the eight meta-heuristic algorithms (including WDO) and the single strategy improved WDO on 24 test functions, the experimental results show that CHTWDO with two improved strategies has better convergence precision and faster convergence speed. Statistical tests such as Friedman's and Wilcoxon's rank-sum tests are used to determine significant differences between these comparison algorithms. Finally, CHTWDO also obtains the best results on four classical optimization problems in engineering applications, which verifies its practicality and effectiveness. [ABSTRACT FROM AUTHOR]

This paper proposes a hybrid metaheuristic approach to optimize a duty cycle framework based on Seagull and Mayfly Optimization (HSMO-DC) Algorithm. This approach becomes crucial as current clustering protocols are unable to efficiently tune the clustering parameters in accordance to the diversification of varying WSNs. The proposed HSMO-DC primarily has two parts, where the first part takes care of the online cluster head selection and network communication using the seagull algorithm while the second part performs parameter optimization using the mayfly algorithm. The seagull is aimed at improving the energy distribution in the network through an effective bandwidth allocation procedure while reducing the total energy dissipation. Comparatively, with other clustering protocols, our proposed methods reveal an enhanced network lifetime with an improved network throughput and adaptability based on selected standard metric of performance measurement. [ABSTRACT FROM AUTHOR]

The Internet of Vehicles (IoV) is one of the developing paradigms that integrates the automotive industry with the Internet of Things (IoT). The evolution of traditional Vehicular Ad-hoc Networks (VANETs), which are a layered framework for Intelligent Transportation Systems (ITS), is employed to provide Quality of Service (QoS) to end users in hazardous situations. VANETs can set up ad-hoc networks and share information among themselves using Peer-to-Peer (P2P) communication. Dynamic properties in VANETs such as dynamic topology, node mobility, sparse vehicle distribution, and bandwidth constraints can have an impact on scalability, routing, and security. This can result in frequent link failures, instability, reliability, and QOS concerns, as well as the inherent complexity of NP-hard problems. Researchers have proposed several techniques to achieve stability; the most prominent one is clustering, which relies on mobility metrics. However, existing clustering techniques generate overwhelming clusters, resulting in greater resource consumption, communication overhead, and hop count, which may lead to increased latency. Therefore, the primary objective is to achieve stability by increasing cluster lifetime, which is accomplished by generating optimal clusters. A nature-inspired meta-heuristic algorithm titled African Vulture Optimization Based Clustering Algorithm (AVOCA) is implemented to achieve it. The proposed algorithm can achieve load optimization with efficient resource utilization by mitigating hidden node challenges and ensuring communication proficiency. By maintaining an equilibrium state between the exploration and exploitation phases, AVOCA avoids local optima. The paper explores a taxonomy of the techniques used in Cluster Head (CH) selection, coordination, and maintenance to achieve stability with lower communication costs. We evaluated the effectiveness of AVOCA using various network grid sizes, transmission ranges, and network nodes. The results show that AVOCA generates 40% less clusters when compared to the Clustering Algorithm Based on Moth-Flame Optimization for VANETs (CAMONET). AVOCA generates 45% less clusters when compared to Self-Adaptive Multi-Kernel Clustering for Urban VANETs (SAMNET), AVOCA generates 43% less clusters when compared to Intelligent Whale Optimization Algorithm (i-WOA) and AVOCA generates 38% less clusters when compared to Harris Hawks Optimization (HHO). The results show that AVOCA outperforms state-of-the-art algorithms in generating optimal clusters. [ABSTRACT FROM AUTHOR]

Optimizing the heliostat field aiming strategy is crucial for maximizing thermal power production in solar power tower (SPT) plants while adhering to operational constraints. Although existing approaches can yield highly optimal solutions, their considerable computational cost makes them unsuitable for real-time optimization in large-scale scenes. This study introduces an efficient, intelligent, real-time optimization method based on a meta-heuristic algorithm to effectively and reliably manage SPT plant operations under varying solar conditions, such as cloud shadowing variations. To minimize redundant calculations, the real-time optimization problem is framed in a way that captures the operational continuity of the heliostat, which can be utilized to streamline the solution process. The proposed method is tested in a simulation environment that includes a heliostat field, cylindrical receiver, and cloud movement model. The results demonstrate that the algorithm presented in this paper offers higher intercept efficiency, improved robustness, and reduced optimization time in more complex scenes. [ABSTRACT FROM AUTHOR]

Since the mention of the Fourth Industrial Revolution in 2016, quantum computers and quantum computing (QC) have emerged as key technologies. Many researchers are trying to realize quantum computers and quantum computing. In particular, most of the development and application of metaheuristics algorithms using quantum computing is focused on computer engineering fields. Cases in which the developed algorithm is applied to the optimal design of a building or the optimal design results presented by expanding the algorithm in various directions are very insufficient. Therefore, in this paper, we proposed four methods of adopting qubits to perform pitch adjusting in the optimization process of the QbHS (quantum-based harmony search) algorithm and applied it to TTO (truss topology optimization) using four methods to compare the results. The four methods of adopting qubits have the same or decreased number of qubits adopted as the number of iterations changes. As a result of applying TTO using four methods, convergence performance differed depending on the adoption method, and convergence performance was superior to conventional HS (harmony search) algorithms in all methods. The optimal design of structural engineering using such QC is expected to contribute to the revitalization of future technologies in the architectural field and the field of computer information systems. [ABSTRACT FROM AUTHOR]

Meta-heuristic algorithms have been extensively utilized in feature selection tasks because they can obtain the global optimal solution. However, the meta-heuristic algorithm will take too much time in the face of a large number of samples. Although most of the studies compromise to approximate optimal solutions for avoiding time-consuming problems, a new problem with reduced classification performance, especially classification stability, is then generated. Aiming to above problems, this paper proposes a new feature selection framework. First, this framework exploits a voting ensemble strategy to improve classification stability by reducing the impact of misclassified labels on the overall classification results. Second, the framework uses a data perturbation strategy to enhance classification accuracy. In particular, the data perturbation strategy is able to generate more neighborhood relationships in the dataset, which could reveal the distribution of various features of the samples. A voting ensemble of different feature distributions is capable of extracting more information from the dataset, then the initially misclassified samples are more likely to be returned to the correct classification. Third, the framework takes a random sampling accelerator into account to solve the problem of excessive time consumption by reducing the size of the search sample space. Finally, for the sake of verifying the effectiveness of the proposed framework, four meta-heuristic feature selection methods based on a neighborhood rough set are compared on 20 datasets. The experimental results indicate that our framework could improve classification performance and accelerate feature selection, particularly in confronting large sample sizes. [ABSTRACT FROM AUTHOR]

This paper investigates the performance of one of the latest metaheuristic swarm-based approaches called Slime Mould Algorithm (SMA). SMA is used here to solve the static bi-objective constrained Economic Emission Dispatch (EED) problem in the presence of renewable energy sources while considering the Valve-Point Effects (VPE). The SMA approach is applied to indicate the adequate optimal solutions for operating the committed thermal units under different operational constraints. The sought optimal solutions are the midpoint between cost saving and pollutant gas emission reduction. This study also examines the influence of integrating renewable energy sources (wind and solar) into the conventional power production network. SMA technique is applied on 3- and 6-unit IEEE test systems in several case studies. The simulation numerical results indicate that SMA has a high efficiency and a better performance compared to known state-of-the-art algorithms. The proposed approach was programmed and simulated in MATLAB. [ABSTRACT FROM AUTHOR]

Directional sensor networks (DSNs) are ad-hoc networks which are utilized in different industrial applications. Their usual engagements are to monitor and to perform the coverage of all specific targets in the observing fields permanently. These kinds of networks include numerous configurable directional sensors in which they can be utilized in one of the possible directions along with the one of their adjustable ranges. Although the energy harvesting methodology is being applied for these battery-hungry applications, the battery management and network lifetime maximization are still prominent challenges. In this paper, the network lifetime extension is formulated to a discrete optimization problem which is a famous non-deterministic polynomial time hardness (NP-Hard) problem. To solve this combinatorial problem, a discrete cuckoo search algorithm (D-CSA) is designed and is called in several rounds. A cover is a sub set of configured sensors capable of monitoring all targets in the observing field. In each round, the most efficient cover is constituted along with its activation time. In the determined activation time, the sensors in the cover are scheduled in wakeup mode whereas others are set in sleep mode to save energy. Despite other meta-heuristic algorithms, this proposed algorithm utilizes the novel defined discrete walking around procedures that makes to reach a good balance between exploration and exploitation in this complex search space. The proposed algorithm has been tested in different scenarios to be evaluated. The simulation results in the variety circumstances prove the superiority of the proposed algorithm is about 20.29%, 19.55%, 14.40%, 14.51%, 7.70% and 8.03% in term of average lifespan improvement against H-MNLAR, Hm-LifMax-BC, GA, ACOSC, H-GATS, and HDPSO algorithms, respectively. The results also show the high potential scalability of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Given the interest and complexity, of the hybrid flow shops (HFS) problem in industry, he has been extensively considered, the HFS, is a complex combinatorial problem supported in many original world applications. We consider a hybrid flow shop FH4(P3, P2)||Cmax to applied in this paper. In this papers we attempt to optimize the makespan which refers to the last task completion time by an adequate meta-heuristic algorithm based on electromagnetism mechanism (EM). We also present analysis on the performance of the EM-algorithm adapted to HFS scheduling problems. The electromagnetism-like mechanism method gave us efficient and fair results comparing to particle swarm and genetic algorithm. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) systems utilize maximum power point tracking (MPPT) controllers to optimize power output amidst varying environmental conditions. However, the presence of multiple peaks resulting from partial shading poses a challenge to the tracking operation. Under partial shade conditions, the global maximum power point (GMPP) may be missed by most traditional maximum power point tracker. The flower pollination algorithm (FPA) and particle swarm optimization (PSO) are two examples of metaheuristic techniques that can be used to solve the issue of failing to track the GMPP. This paper discusses and resolves all issues associated with using the standard FPA method as the MPPT for PV systems. The first issue is that the initial values of pollen are determined randomly at first, which can lead to premature convergence. To minimize the convergence time and enhance the possibility of detecting the GMPP, the initial pollen values were modified so that they were near the expected peak positions. Secondly, in the modified FPA, population fitness and switch probability values both influence swapping between two-mode optimization, which may improve the flower pollination algorithm's tracking speed. The performance of the modified flower pollination algorithm (MFPA) is assessed through a comparison with the perturb and observe (P&O) method and the standard FPA method. The simulation results reveal that under different partial shading conditions, the tracking time for MFPA is 0.24, 0.24, 0.22, and 0.23 s, while for FPA, it is 0.4, 0.35, 0.45, and 0.37 s. Additionally, the simulation results demonstrate that MFPA achieves higher MPPT efficiency in the same four partial shading conditions, with values of 99.98%, 99.90%, 99.93%, and 99.26%, compared to FPA with MPPT efficiencies of 99.93%, 99.88%, 99.91%, and 99.18%. Based on the findings from simulations, the proposed method effectively and accurately tracks the GMPP across a diverse set of environmental conditions. [ABSTRACT FROM AUTHOR]

In this paper, a point cloud coarse–fine registration method based on a new improved version of the whale optimization algorithm (NIWOA) and iterative closest point (ICP) algorithm is proposed; we use three strategies to improve the whale optimization algorithm (WOA). Firstly, circle chaotic mapping is used to initialize the whale population to increase the diversity of the population and make the search space more comprehensively explored. In addition, a Newton inertia weight is proposed to flexibly adjust the proportion of global exploration and local optimization in order to achieve the balance between the exploitation performance and exploration ability of the algorithm. At the same time, we introduce the nonlinear convergence factor that can adjust the size adaptively so that the algorithm can find the global optimal solution faster and more accurately, allowing it to avoid falling into the local optimal solution to a certain extent. The NIWOA algorithm is used to optimize the objective function of point cloud coarse registration to obtain the optimal coordinate transformation, and the rotation and translation operation is carried out on the registered point cloud. The obtained position is used as the initial pose of the ICP fine registration, and the final registration is achieved through ICP iteration. We conduct coarse registration experiments on multiple model point clouds and scene point clouds using the Stanford 3D Scanning Repository dataset and Princeton 3Dmatch dataset, respectively. The experimental results confirm that the NIWOA algorithm can not only find the initial position that is closer to the target point cloud, but also provide reliable initial values for the ICP algorithm. Meanwhile, the NIWOA algorithm combined with ICP experiment results show that the method has a higher registration accuracy and operation efficiency. [ABSTRACT FROM AUTHOR]