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This study proposes a dynamic simulation-based framework that utilizes swarm intelligence algorithms to optimize the design of hybrid assembly lines in the automotive industry. Two recent discrete versions of Whale Optimization Algorithm (named VNS-DWOA) and Gorilla Troops Optimizer (named DGTO) were developed to solve the assembly line balancing problem. The effectiveness of these algorithms was compared to six conventional meta-heuristics as well as the solution proposed by process design experts. The experimental results show that our methods outperform the conventional meta-heuristics and achieve comparable or better results than the experts' solution. Particulary, VNS-DWOA, being the top performer, has consistently provided averagely remarkable enhancements of cycle time, ranging from 7% when compared to the process expert's solution to 20% maximum improvement compared to all other methods. The findings of this study highlight the effectiveness of utilizing swarm intelligence algorithms and dynamic simulation-based frameworks as well as the potential benefits of implementing these digital methods in industrial settings, as they can significantly accelerate and enhance the optimization of assembly line design particularly and reduce time to market generally. [ABSTRACT FROM AUTHOR]

Data clustering has attracted the interest of scholars in many fields. In recent years, using heuristic algorithms to solve data clustering problems has gradually become a tendency. The black hole algorithm (BHA) is one of the popular heuristic algorithms among researchers because of its simplicity and effectiveness. In this paper, an improved self-adaptive logarithmic spiral path black hole algorithm (SLBHA) is proposed. SLBHA innovatively introduces a logarithmic spiral path and random vector path to BHA. At the same time, a parameter is used to control the randomness, which enhances the local exploitation ability of the algorithm. Besides, SLBHA designs a replacement mechanism to improve the global exploration ability. Finally, a self-adaptive parameter is introduced to control the replacement mechanism and maintain the balance between exploration and exploitation of the algorithm. To verify the effectiveness of the proposed algorithm, comparison experiments are conducted on 13 datasets creatively using the evaluation criteria including the Jaccard coefficient as well as the Folkes and Mallows index. The proposed methods are compared with the selected algorithms such as the whale optimization algorithm (WOA), compound intensified exploration firefly algorithm (CIEFA), improved black hole algorithm (IBH), etc. The experimental results demonstrate that the proposed algorithm outperforms the compared algorithms on both external criteria and quantization error of the clustering problem. [ABSTRACT FROM AUTHOR]

In recent years, researchers have paid increasing attention to Peer to Peer lending market. In this lending method, the lenders don't usually have enough knowledge to understand how much money must be allocated in each loan. So, it is important to help investors to assess the loans. According to the needs of investors, generally, a bi-objective optimization model is considered that maximizes return and minimizes risk. In current work, internal rate of return as return and probability of default as risk variable is considered. First, an artificial neural network is used to evaluate the return. Then, we apply logistic regression to predict the probability of loan default. After providing the model, due to its complexity, one of the essential issues is solving it. In this study, the model is solved by metaheuristic algorithms. This is done from different points of view, including non-dominated sorting genetic algorithm II, multi-objective particle swarm optimization, and Pareto envelope-based selection algorithm II. Finally, from the perspective of an investor, the best algorithm is introduced. [ABSTRACT FROM AUTHOR]

In the dynamic smart grid landscape, accurate probabilistic forecasting of electric load is critical. This paper presents a novel 24-hour-ahead probabilistic load forecasting model by integrating quantile regression with a parallel convolutional neural network (CNN) and bidirectional gated recurrent unit (BiGRU) architecture. Carefully tuning hyperparameters can enhance model performance and generalization capability. Consequently, we propose an improved whale optimization algorithm for automatic hyperparameter tuning of the forecasting model. Case studies demonstrate the proposed method's superior performance over benchmark models in terms of average interval score and pinball loss. In addition, it exhibits valid coverage and tight interval bandwidths. The model provides precise short-term load forecasts to support robust smart grid planning and operations. [ABSTRACT FROM AUTHOR]

Numerous heuristic and metaheuristic algorithms inspired by nature have been developed so far. These algorithms have demonstrated their excellence in resolving challenging problems across a variety of fields. However, many optimization algorithms fail while solving a complex real-world problem efficiently in an independent way because these problems have a distinct search behavior as they contain numerous equality and inequality constraints of the linear, nonlinear, and non-convex types. In this study, two optimization algorithms: the Marine Predators Algorithm (MPA) and the Social Group Optimization (SGO) algorithm are hybridized in order to increase their efficiency and problem-solving capability to solve this type of problem. This proposed hybrid optimization algorithm is named as Marine Predators Social Group Optimization (MPSGO) algorithm. This hybrid algorithm combines the strengths of both techniques to enhance the efficiency and effectiveness of the optimization process. It is validated through thirty benchmark functions of CEC2014 and after that 26 real-world optimization problems test suites of CEC 2020 from the chemical and mechanical engineering domain have been solved. The validation results are compared with ten state-of-art optimization algorithm and real-world optimization problem result is compared with BiPop matrix adaptation evolution strategy (BP-ϵMAg-ES), enhanced Multi-Operator Differential Evolution (EnMODE), LSHADE for Constrained Optimization (COLSHADE), Cohort Intelligence self-adaptive penalty function (CI-SAPF), and Cohort Intelligence self-adaptive penalty function Colliding Bodies Optimization (CI-SAPF-CBO). As MPSGO is a hybrid algorithm, again we have compared its performance with the seven best modified and improved optimization algorithms. In terms of convergence quality and improving solution quality, the simulated results of MPSGO have been shown to be competitive with those of state-of-the-art, hybrid, improved, and advanced optimization algorithms, proving the validity as well as the feasibility of the MPSGO algorithm. [ABSTRACT FROM AUTHOR]

Packing problems have been studied for a long time and have great applications in real-world scenarios. In recent times, with problems in the industrial world increasing in size, exact algorithms are often not a viable option and faster approaches are needed. We study Monte Carlo tree search, a random sampling algorithm that has gained great importance in literature in the last few years. We propose three approaches based on MCTS and its integration with metaheuristic algorithms or deep learning models to obtain approximated solutions to packing problems that are also interpretable by means of MCTS exploration and from which knowledge can be extracted. We focus on two-dimensional rectangle packing problems in our experimentation and use several well known benchmarks from literature to compare our solutions with existing approaches and offer a view on the potential uses for knowledge extraction from our method. We manage to match the quality of state-of-the-art methods, with improvements in time with respect to some of them and greater interpretability. [ABSTRACT FROM AUTHOR]

Methaheuristics (MHs) are techniques widely used for solving complex optimization problems. In recent years, the interest in combining MH and machine learning (ML) has grown. This integration can occur mainly in two ways: ML-in-MH and MH-in-ML. In the present work, we combine the techniques in both ways—ML-in-MH-in-ML, providing an approach in which ML is considered to improve the performance of an evolutionary algorithm (EA), whose solutions encode parameters of an ML model—artificial neural network (ANN). Our approach called TS in EA in ANN employs a reinforcement learning neighborhood (RLN) mutation based on Thompson sampling (TS). TS is a parameterless reinforcement learning method, used here to boost the EA performance. In the experiments, every candidate ANN solves a regression problem known as protein structure prediction deviation. We consider two protein datasets, one with 16,382 and the other with 45,730 samples. The results show that TS in EA in ANN performs significantly better than a canonical genetic algorithm (GA in ANN) and the evolutionary algorithm without reinforcement learning (EA in ANN). Analyses of the parameter's frequency are also performed comparing the approaches. Finally, comparisons with the literature show that except for one particular case in the largest dataset, TS in EA in ANN outperforms other approaches considered the state of the art for the addressed datasets. [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]

This study focuses on exploring the effects of geometrical imperfections and different analysis methods on the optimum design of Double-Layer Grids (DLGs), as used in the construction industry. A total of 12 notable metaheuristics are assessed and contrasted, and as a result, the Slime Mold Algorithm is identified as the most effective approach for size optimization of DLGs. To evaluate the influence of geometric imperfections and nonlinearity on the optimal design of real-size DLGs, the optimization process is carried out by considering and disregarding geometric nonlinearity while incorporating three distinct forms of geometrical imperfections, namely local imperfections, global imperfections, and combinations of both. In light of the uncertain nature of geometrical imperfections, probabilistic distributions are used to define these imperfections randomly in direction and magnitude. The results demonstrate that it is necessary to account for these imperfections to obtain an optimal solution. It's worth noting that structural imperfections can increase the maximum stress ratio by up to 70%. The analysis also reveals that the initial curvature of members has a more significant impact on the optimal design of structures than the nodal installation error, indicating the need for greater attention to local imperfection issues in space structure construction. [ABSTRACT FROM AUTHOR]

In this paper, a new algorithm named the improved transient search optimization algorithm (ITSOA) is utilized to solve classical test functions, optimize the consumption of building energy, and optimize hybrid energy system production. The conventional TSOA draws inspiration from the fleeting behavior of electrical circuits with energy storage components. Rosenbrock's direct rotation technique is used to improve the traditional TSOA performance against exploration and exploitation unbalance. First, the ITSOA performance is investigated in solving 23 classical benchmark functions, and the outcomes have shown the superior capability of the recommended algorithm in comparison with the conventional TSOA, DMO, SHO, GA, MRFO, and PSO methods. Also, the ITSOA proficiency is verified in solving the building energy optimization (BEO) problem for minimizing the energy usage of two simple and detailed buildings. The optimization results showed that the optimized solutions of ITSOA in single and multi-objective optimizations compared to conventional TSOA, DMO, SHO, GA, MRFO, and PSO obtained a lower value of the cost function. Also, the superiority of ITSOA has been confirmed to solve the BEO compared to previous methods. Moreover, the multi-objective optimization results have shown that ITSOA is able to determine the ultimate solution among the Pareto front set based on the fuzzy decision-making approach and building energy utilization decisions. [ABSTRACT FROM AUTHOR]

In the coming decade, as restrictions on fossil fuel usage become more stringent, investment in renewable energy projects presents an increasingly appealing opportunity. Evaluating investment attractiveness involves considering both profitability and investment risk. This study proposes a multi-objective mathematical model for identifying the optimal Renewable Energy Project Portfolio (REPP), aiming to maximize net present value while minimizing investment risk. The key innovation of this model is its incorporation of project lifetime and workforce employment considerations to discern the best REPP. To optimize the objective functions of this mathematical model, a hybrid meta-heuristic algorithm combining Artificial Immune System (AIS) and Artificial Fish Swarm (AFS) algorithms is introduced. Genuine data from a varied spectrum of renewable energy projects spanning 20 countries has been meticulously collected. The proposed model is optimized using this dataset, considering portfolio sizes of 3, 5, 10, and 15. The numerical results indicate that, at a specific investment risk threshold, the proposed hybrid algorithm outperforms both AIS and AFS in terms of profitability. Furthermore, the assessment of the geographical distribution of selected projects reveals a deliberate effort to avoid concentration in any specific region, demonstrating a commitment to identifying optimal investment opportunities globally. This research advances the understanding of renewable energy project portfolio optimization, providing valuable insights for investors, policymakers, and sustainable development practitioners. [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]

Shape-morphing capabilities are crucial for enabling multifunctionality in both biological and artificial systems. Various strategies for shape morphing have been proposed for applications in metamaterials and robotics. However, few of these approaches have achieved the ability to seamlessly transform into a multitude of volumetric shapes post-fabrication using a relatively simple actuation and control mechanism. Taking inspiration from thick origami and hierarchies in nature, we present a hierarchical construction method based on polyhedrons to create an extensive library of compact origami metastructures. We show that a single hierarchical origami structure can autonomously adapt to over 103 versatile architectural configurations, achieved with the utilization of fewer than 3 actuation degrees of freedom and employing simple transition kinematics. We uncover the fundamental principles governing theses shape transformation through theoretical models. Furthermore, we also demonstrate the wide-ranging potential applications of these transformable hierarchical structures. These include their uses as untethered and autonomous robotic transformers capable of various gait-shifting and multidirectional locomotion, as well as rapidly self-deployable and self-reconfigurable architecture, exemplifying its scalability up to the meter scale. Lastly, we introduce the concept of multitask reconfigurable and deployable space robots and habitats, showcasing the adaptability and versatility of these metastructures. Enabling complex shape morphing in engineering systems remains a significant challenge. In this work, authors demonstrate that a transformer-like hierarchical origami metastructure can self-reconfigure into over 1000 versatile structures with fewer than 3 actuation degrees of freedom and simple control. [ABSTRACT FROM AUTHOR]

The limited energy possessed by each individual sensor node makes the process of designing and developing an efficient wireless sensor network as a herculean task. The process of clustering and successive cluster head (CH) process with energy efficiency is essential for extending network lifetime and sustaining the existence of alive sensor nodes in the network. In this paper, a multi-objective optimization algorithm based on Hybrid Bald Eagle Search Optimization Algorithm (HBESAOA) is presented as the energy clustering solution for targeting on the process of extending the sensor nodes' network lifetime. This multi-objective optimization HBESAOA algorithm included the fitness evaluating factors of energy, delay, distance, node centrality and node degree into account during the process of CH selection. It specifically adopted the well-balanced exploration and exploitation capabilities of Bald Eagle Search (BES), such that potential search process during clustering and subsequent CH selection is attained in the network. It is prevented with the potential of attaining maximized solution diversity with prevented premature convergence problem. The results of this HBESAOA scheme is identified to be significant in increasing the mean network lifetime by 26.78%, compared to the baseline CH selection schemes independent to the position of the sink node. The energy sustenance in the network is also predominantly improved by 21.98%, independent to the scalable increase in the number of sensor nodes on comparison with the competitive approaches used for evaluation. [ABSTRACT FROM AUTHOR]

In many engineering optimization problems, the number of function evaluations is severely limited by the time or cost constraints. These limitations present a significant challenge in the field of global optimization, because existing metaheuristic methods typically require a substantial number of function evaluations to find optimal solutions. This paper presents a new metaheuristic optimization algorithm that considers the information obtained by a radial basis function neural network (RBFNN) in terms of the objective function for guiding the search process. Initially, the algorithm uses the maximum design approach to strategically distribute a set of solutions across the entire search space. It then enters a cycle in which the RBFNN models the objective function values from the current solutions. The algorithm identifies and uses key neurons in the hidden layer that correspond to the highest objective function values to generate new solutions. The centroids and standard deviations of these neurons guide the sampling process, which continues until the desired number of solutions is reached. By focusing on the areas of the search space that yield high objective function values, the algorithm avoids exhaustive solution evaluations and significantly reduces the number of function evaluations. The effectiveness of the method is demonstrated through a comparison with popular metaheuristic algorithms across several test functions, where it consistently outperforms existing techniques, delivers higher-quality solutions, and improves convergence rates. [ABSTRACT FROM AUTHOR]

Parameter identification of solar photovoltaic (PV) cells is crucial for the PV system modeling. However, finding optimal parameters of PV models is an intractable problem due to the highly nonlinear characteristics between currents and voltages in different environments. To address this problem, whale optimization algorithm (WOA)-based meta-heuristic algorithm has turned out to be a feasible and effective approach. As a highly promising optimization algorithm, different enhanced WOA variants have been proposed. Nevertheless, there has been no comparative study of WOA and its variants for parameter identification of PV models so far. To further investigate and analyze the performance of WOA in the studied problem, this work applied and compared WOA and ten enhanced WOA variants for identifying five PV model parameters. Different evaluation indices including solution accuracy, search robustness, and convergence curve were employed to reveal their performance variation. Based on the simulation results, a multi-model statistical analysis with the Friedman test at a confidence level 0.05 was conducted to rank all algorithms. EWOA that hybridizes the sorting-based differential mutation operator and the Lévy flight strategy ranked first and its performance was further verified. Besides, according to the simulation results, possible effective improvement directions for WOA in tackling this intractable problem are concluded to guide future work. [ABSTRACT FROM AUTHOR]

Communication quality is compromised when wireless sensor network (WSN) operate in harsh environments, which can be improved by supplementing the nodes. This paper proposes a deployment strategy to optimize the placement of new nodes in WSN, specifically in complex environments with limited communication. To achieve this, the paper introduces the concept of strong connectivity relationships and presents a novel metaheuristic algorithm, namely double-state differential evolution (DSDE), which divides the optimization process into two states and adopts different optimization strategies. The proposed DSDE can improve the overall network communication glowing at a lower computing cost. Extensive experiments show that the proposed DSDE has better performance than state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Agriculture plays a substantial part in aggregating the nation's economic growth. The production of crops had been greatly affected due to alterations in weather patterns. Recent technologies greatly assist in increasing agricultural output by changing conventional farming into precision farming. The primary issue that has yet to be resolved is growing a specific crop in certain soil and climatic circumstances. This research introduces a novel deep learning (DL) technique for the automatic recommendation of crops based on soil, fertilizers, and climatic conditions. The proposed method is trained and tested with publicly available Kaggle datasets and real-time collected Maharashtra datasets consisting of both soil and climatic parameters. Initially, data from the dataset are collected and fed into a new Concurrent Excited Gated Recurrent Unit (CEGRU) based DL approach for reliably proposing crops. In addition, a meta-heuristic algorithm named the hunter-prey optimization (HPO) technique is introduced to tune the parameters of the proposed network model. For implementation, the Python platform is utilized, and various performances like accuracy, kappa, PPV, recall, sensitivity, time complexity, and RMSE are analyzed. The proposed method obtains the overall accuracy of 99.2% and 99.3%, PPV of 97.5% and 98.3%, kappa of 95.7% and 96.6%, RMSE of 1.05 and 1.50, the time complexity of 105 s are obtained for both Maharashtra and Kaggle datasets respectively. Furthermore, sensitivity and recall scores were assessed independently for each dataset, yielding results of 98.61% and 97.54%, respectively. [ABSTRACT FROM AUTHOR]

Global optimization techniques are increasingly preferred over human-driven methods in the design of electromagnetic structures such as metasurfaces, and careful construction and parameterization of the physical structure is critical in ensuring computational efficiency and convergence of the optimization algorithm to a globally optimal solution. While many design variables in physical systems take discrete values, optimization algorithms often benefit from a continuous design space. This work demonstrates the use of level set functions as a continuous basis for designing material distributions for metasurface arrays and introduces an improved parameterization which is termed the periodic level set function. We explore the use of alternate norms in the definition of the level set function and define a new pseudo-inverse technique for upsampling basis coefficients with these norms. The level set method is compared to the fragmented parameterization and shows improved electromagnetic responses for two dissimilar cost functions: a narrowband objective and a broadband objective. Finally, we manufacture an optimized level set metasurface and measure its scattering parameters to demonstrate real-world performance. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is an extensive system of interrelated devices equipped with sensors to monitor and track real world objects, spanning several verticals, covering many different industries. The IoT's promise is capturing interest as its value in healthcare continues to grow, as it can overlay on top of challenges dealing with the rising burden of chronic disease management and an aging population. To address difficulties associated with IoT-enabled healthcare, we propose a secure routing protocol that combines a fuzzy logic system and the Whale Optimization Algorithm (WOA) hierarchically. The suggested method consists of two primary approaches: the fuzzy trust strategy and the WOA-inspired clustering methodology. The first methodology plays a critical role in determining the trustworthiness of connected IoT equipment. Furthermore, a WOA-based clustering framework is implemented. A fitness function assesses the likelihood of IoT devices acting as cluster heads. This formula considers factors such as centrality, range of communication, hop count, remaining energy, and trustworthiness. Compared with other algorithms, the proposed method outperformed them in terms of network lifespan, energy usage, and packet delivery ratio by 47%, 58%, and 17.7%, respectively. [ABSTRACT FROM AUTHOR]

Child Drawing Development Optimization is a recently developed metaheuristic algorithm that has been demonstrated to perform well on multiple benchmark tests. In this paper, a binary Child Drawing Development Optimization (BCDDO) is proposed for wrapper feature selection. The proposed BCDDO is utilized to choose a subset of important features to reach the highest classification accuracy. Harris Hawk optimization, salp swarm algorithm, gray wolf optimization, and whale optimization algorithm are utilized to evaluate the effectiveness and efficiency of the suggested feature selection method. In the field of feature selection to improve classification accuracy, the proposed method has gained a considerable classification accuracy advantage over previously mentioned methods. Four datasets are used in this research work; breast cancer, moderate COVID, big COVID, and Iris using XGBoost classifier and the classification accuracies were (98.83%, 98.75%, 99.36%, and 96%), respectively, for the four mentioned datasets. [ABSTRACT FROM AUTHOR]

The intrusion detection system is responsible for revealing different intrusion activities, including the denial of service, man-in-middle, Mirai, Scan, and other types of intrusion activities. It is used in many applications, including the smart home Internet of Things networks, where security risks threaten the privacy of individuals. In this context, many works were proposed for detecting and classifying the different types of attacks. However, many challenges are identified for this type of problem, such as the large amount of data available, the imbalanced nature of the data, and the quality of detection and classification outcomes. This paper aims to address these challenges by proposing an approach that considers a metaheuristic-based random weight neural network to detect intrusion activities and classify the different types and subtypes of activities. The following points summarize the contribution of this paper. First, the automatic tuning of the neural network parameters where the weights, biases, regularization value, the number of neurons, and the type of activation function are optimized by different metaheuristic algorithms to produce high-quality results. Second, the proposed approach adopts a clustering with reduction technique to tackle the challenge of processing large volumes of data. Third, oversampling the dataset is also embedded in the proposed approach to avoid a biased classification of the majority class. The experiments are conducted based on a large dataset with more than half a million instances. The results show that the proposed approach outperforms the other classification approaches in geometric mean (G-Mean) and has promising results. [ABSTRACT FROM AUTHOR]

In artificial intelligence (AI), optimization techniques are used to solve several problems in different fields. One of these areas is software reliability verification, which is an important part of software products, as it helps determine how reliable the software is to complete its functions. This is done by estimating the parameters of software reliability growth models (SRGMs). SRGMs predict the expected number of failures after completion, while also serving as an indicator of software readiness for delivery. Therefore, this study aims to optimize the estimation of these parameters based on the available failure data using one of the stochastic optimization algorithms, the simulated annealing algorithm (SA) due to its power and effectiveness. Three SRGMs' models are studied: delayed S-shaped, Musa-Okumoto logarithmic and Power models, to examine the feasibility of the proposed algorithm using five different data sets. The results were compared and analyzed with several algorithms: Particle swarm optimization (PSO), cuckoo search (CS), modify whale optimization algorithm (MWOA), S-shaped model with logistic TEF and social spider algorithm (SSA). A comparison was also made with recent SRGMs that do not rely on AI techniques. The results showed that the proposed algorithm based on SA outperformed all other methods in finding the optimal parameters. [ABSTRACT FROM AUTHOR]

The forensic-based investigation (FBI) is a metaheuristic algorithm inspired by the criminal investigation process. The collaborative efforts of the investigation and pursuit teams demonstrate the FBI's involvement during the exploitation and exploration phases. When choosing the promising population, the FBI algorithm's population selection technique focuses on the same region. This research aims to propose a dynamic population selection method for the original FBI and thereby enhance its convergence performance. To achieve this objective, the FBI may employ dynamic oppositional learning (DOL), a dynamic version of the oppositional learning methodology, to dynamically navigate to local minima in various locations. Therefore, the proposed advanced method is named DOLFBI. The performance of DOLFBI on the CEC2019 and CEC2022 benchmark functions is evaluated by comparing it with several other popular metaheuristics in the literature. As a result, DOLFBI yielded the lowest fitness value in 18 of 22 benchmark problems. Furthermore, DOLFBI has shown promising results in solving real-world engineering problems. It can be argued that DOLFBI exhibits the best convergence performance in cantilever beam design, speed reducer, and tension/compression problems. DOLFBI is often utilized in truss engineering difficulties to determine the minimal weight. Its success is comparable to other competitive MAs in the literature. The Wilcoxon signed-rank and Friedman rank tests further confirmed the study's stability. Convergence and trajectory analyses validate the superior convergence concept of the proposed method. When the proposed study is compared to essential and enhanced MAs, the results show that DOLFBI has a competitive framework for addressing complex optimization problems due to its robust convergence ability compared to other optimization techniques. As a result, DOLFBI is expected to achieve significant success in various optimization challenges, feature selection, and other complex engineering or real-world problems. [ABSTRACT FROM AUTHOR]

The flexible job shop scheduling problem (FJSP) is a classic NP-hard problem, and the quality of its scheduling solution directly affects the operational efficiency of the manufacturing system. However, the traditional scheduling algorithms suffer from poor generalization when solving FJSP; there are problems such as long computational time and dimensional disasters, especially as the scale of FJSP increases. Therefore, an effective deep actor-critic reinforcement learning (DACRL) method is proposed for solving FJSP. Firstly, the FJSP is modeled as a multi-agent Markov decision process (MMDP), the state space, action space, and reward function in the MMDP are designed. Secondly, a DACRL model is constructed to solve FJSP. The actor network is responsible for choosing the most suitable scheduling rule in different states, while the critic network is responsible for outputting the value function of the actions and providing feedback to the actor network to better adjust the scheduling strategy. Finally, the proposed DACRL method is validated on benchmark FJSP instances of different scales. The experimental results show that the proposed method significantly outperforms the heuristic scheduling rules and double deep Q-network (DDQN) in terms of solution quality. Compared with the meta-heuristic algorithms and the self-learning genetic algorithm (SLGA), the proposed method has higher solution efficiency with the same solution quality. [ABSTRACT FROM AUTHOR]

Harris Hawks optimization (HHO) algorithm was a powerful metaheuristic algorithm for solving complex problems. However, HHO could easily fall within the local minimum. In this paper, we proposed an improved Harris Hawks optimization (IHHO) algorithm for solving different engineering tasks. The proposed algorithm focused on random location-based habitats during the exploration phase and on strategies 1, 3, and 4 during the exploitation phase. The proposed modified Harris hawks in the wild would change their perch strategy and chasing pattern according to updates in both the exploration and exploitation phases. To avoid being stuck in a local solution, random values were generated using logarithms and exponentials to explore new regions more quickly and locations. To evaluate the performance of the proposed algorithm, IHHO was compared to other five recent algorithms [grey wolf optimization, BAT algorithm, teaching–learning-based optimization, moth-flame optimization, and whale optimization algorithm] as well as three other modifications of HHO (BHHO, LogHHO, and MHHO). These optimizers had been applied to different benchmarks, namely standard benchmarks, CEC2017, CEC2019, CEC2020, and other 52 standard benchmark functions. Moreover, six classical real-world engineering problems were tested against the IHHO to prove the efficiency of the proposed algorithm. The numerical results showed the superiority of the proposed algorithm IHHO against other algorithms, which was proved visually using different convergence curves. Friedman's mean rank statistical test was also inducted to calculate the rank of IHHO against other algorithms. The results of the Friedman test indicated that the proposed algorithm was ranked first as compared to the other algorithms as well as three other modifications of HHO. [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]

This study examined the optimal size of an autonomous hybrid renewable energy system (HRES) for a residential application in Buea, located in the southwest region of Cameroon. Two hybrid systems, PV-Battery and PV-Battery-Diesel, have been evaluated in order to determine which was the better option. The goal of this research was to propose a dependable, low-cost power source as an alternative to the unreliable and highly unstable electricity grid in Buea. The decision criterion for the proposed HRES was the cost of energy (COE), while the system's dependability constraint was the loss of power supply probability (LPSP). The crayfish optimization algorithm (COA) was used to optimize the component sizes of the proposed HRES, and the results were contrasted to those obtained from the whale optimization algorithm (WOA), sine cosine algorithm (SCA), and grasshopper optimization algorithm (GOA). The MATLAB software was used to model the components, criteria, and constraints of this single-objective optimization problem. The results obtained after simulation for LPSP of less than 1% showed that the COA algorithm outperformed the other three techniques, regardless of the configuration. Indeed, the COE obtained using the COA algorithm was 0.06%, 0.12%, and 1% lower than the COE provided by the WOA, SCA, and GOA algorithms, respectively, for the PV-Battery configuration. Likewise, for the PV-Battery-Diesel configuration, the COE obtained using the COA algorithm was 0.065%, 0.13%, and 0.39% lower than the COE provided by the WOA, SCA, and GOA algorithms, respectively. A comparative analysis of the outcomes obtained for the two configurations indicated that the PV-Battery-Diesel configuration exhibited a COE that was 4.32% lower in comparison to the PV-Battery configuration. Finally, the impact of the LPSP reduction on the COE was assessed in the PV-Battery-Diesel configuration. The decrease in LPSP resulted in an increase in COE owing to the nominal capacity of the diesel generator. [ABSTRACT FROM AUTHOR]

The energy management (EM) solution of the multi-microgrids (MMGs) is a crucial task to provide more flexibility, reliability, and economic benefits. However, the energy management (EM) of the MMGs became a complex and strenuous task with high penetration of renewable energy resources due to the stochastic nature of these resources along with the load fluctuations. In this regard, this paper aims to solve the EM problem of the MMGs with the optimal inclusion of photovoltaic (PV) systems, wind turbines (WTs), and biomass systems. In this regard, this paper proposed an enhanced Jellyfish Search Optimizer (EJSO) for solving the EM of MMGs for the 85-bus MMGS system to minimize the total cost, and the system performance improvement concurrently. The proposed algorithm is based on the Weibull Flight Motion (WFM) and the Fitness Distance Balance (FDB) mechanisms to tackle the stagnation problem of the conventional JSO technique. The performance of the EJSO is tested on standard and CEC 2019 benchmark functions and the obtained results are compared to optimization techniques. As per the obtained results, EJSO is a powerful method for solving the EM compared to other optimization method like Sand Cat Swarm Optimization (SCSO), Dandelion Optimizer (DO), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), and the standard Jellyfish Search Optimizer (JSO). The obtained results reveal that the EM solution by the suggested EJSO can reduce the cost by 44.75% while the system voltage profile and stability are enhanced by 40.8% and 10.56%, respectively. [ABSTRACT FROM AUTHOR]

High-utility itemsets mining (HUIM) is an important research topic in data mining that consists of extracting efficient itemsets that generate high profit in databases. Metaheuristic algorithms are good candidates for finding some sufficiently good high-utility itemsets (HUIs) while reducing the prohibitive computational cost incurred by such mining. However, metaheuristic algorithms suffer from crucial issues: runtime and memory consumption, entailing scalability issues. To contribute in alleviating these drawbacks, we explore a hybrid approach combining metaheuristics with an optimization technique, and experimentally show the obtainment of better quality solutions. The exploration component in our approach relies on the crow search algorithm (CSA) which is a method that presents a strong capacity in the global search process. The exploitation component is based on the particle swarm optimization (PSO) which is an established stochastic optimization technique known to have a good convergence capability. This article proposes the HUIM-ICSO algorithm, coupling CSA and PSO, as a hybrid method for mining HUIs. In addition to the proposed main CSA-based algorithm, two additional novel enhanced variants are incorporated and integrated with PSO. Experiments are undertaken on 12 publicly available datasets and compared with state-of-the-art algorithms, such as mHUIMiner, HUIM-SPSO and HUIM-AF. Experiments show that, as the number of transactions or items grows, the suggested approach remains scalable and delivers favorable outcomes in terms of runtime, memory usage, and the quantity of HUIs mined, all while maintaining a good convergence rate. [ABSTRACT FROM AUTHOR]

The concept of computational intelligence (CI)-based optimization algorithms emerged in the early 1960s as a more practical approach to the contemporary derivate-based approaches. This paved the way for many modern algorithms to arise with an unprecedented growth rate in recent years, each claiming to have a novel and present a profound breakthrough in the field. That said, many have raised concerns about the performance of these algorithms and even identified fundamental flaws that could potentially undermine the integrity of their results. On that note, the premise of this study was to replicate some of the more prevalent, fundamental components of these algorithms in an abstract format as a measure to observe their behavior in an isolated environment. Six pseudo algorithms were designed to create a spectrum of intelligence behavior ranging from absolute randomness to local search-oriented computational architecture. These were then used to solve a set of centered and non-centered benchmark suites to see if statistically different patterns would emerge. The obtained result clearly highlighted that the algorithm's performance would suffer significantly as these benchmarks got more intricate. This is not just in terms of the number of dimensions in the search space but also the mathematical structure of the benchmark. The implication is that, in some cases, sheer processing resources can mask the algorithm's lack of sufficient intelligence. But as importantly, this study attempted to identify some mechanics and concepts that could potentially cause or amplify this problem. For instance, the excessive use of greedy strategy, a prevalent measure embedded in many modern CI-based algorithms, has been identified as potentially one of these reasons. The result, however, highlights a more fundamental problem in the CI-based optimization field. That is, these algorithms are often treated as a black box. This perception cultivated the culture of not exploring the underlying structure of these algorithms as long as they were deemed capable of generating acceptable results, which permits similar biases to go undetected. [ABSTRACT FROM AUTHOR]

Network function virtualization (NFV) is a contemporary network architecture concept that integrates complex network functions into software-based virtual network functions (VNFs) instead of specialized hardware. Resource allocation for multiple service function chain requests (SFC) in NFV-enabled networks is a fundamental challenge for network providers. Several research studies have focused on this problem in detail. Specifically, SFC routing attempts to satisfy each request by assigning a path across data centers that places the ordered set of required VNFs. This paper formulates this problem as an SFC provisioning problem (SFCRP) with the two objectives of maximizing the number of accepted requests and balancing the utilization of network resources. To address this problem, a two-stage routing algorithm (2PRT) that integrates Yen's algorithm and a genetic algorithm is proposed. The simulation results based on 12 scenarios that use three real-world networks and randomly generated requests indicate that the batch mode significantly enhances the acceptance rate compared to the existing methods. [ABSTRACT FROM AUTHOR]

The mechanical properties of arteries are often assessed with biaxial testing. Constitutive models are fitted to the experimental stress-stretch responses to determine relations describing the intrinsic characteristics of the material. Nonlinear least square-based optimization routines and the Levenberg–Marquardt and Trust-Region-Reflective algorithms are commonly used for fitting. But they may not always produce the best fit for the experimental data. We describe a different optimized fitting routine that can significantly improve the constitutive model fit quality. Experimental stress-stretch data were obtained from biaxial testing of 16 human superficial femoral arteries. A metaheuristic population-based algorithm, Differential Evolution (DE), and loss functions based on the Minkowski and Chebyshev Distance functions were used to improve the fit with common constitutive relations. Hyperparameter tuning by grid search method was performed to determine how algorithm settings affect the fit quality. The DE algorithm showed a superior fit in all samples compared to the traditional algorithms. The loss functions based on Minkowski Distance and Chebyshev relations rather than the square error produced better fits. This allows describing the experimental data with simpler constitutive relations that have fewer material parameters, which reduces the complexity and improves the efficiency of computational implementations. [ABSTRACT FROM AUTHOR]

This article explores the use of different metaheuristic optimization algorithms for training a self-organizing neural network (SONN) model to estimate Brazilian tensile strength (BTS) in granite rocks, including brainstorm optimization (BSO), coronavirus herd immunity optimization (CHIO), and social ski-driver optimization (SSDO). The optimization processes of the algorithms were similar, and the same population size and maximum iterations were used based on 284 granite samples collected. The Schimidt hammer test (Rn), point load test (IS50), and dry density test (DD) based on the ISRM standards were considered to predict BTS. Accordingly, 80% of the whole dataset (approximately, 228 samples) was used to train the intelligence models, while the remaining 20% was used for testing the trained performance of the models. Tenfold cross-validation with three repeats was applied to avoid the overfitting phenomena. While the BSO–SONN demonstrated excellent performance on both training and testing datasets with low MAE and RMSE values and a high R2 (MAE = 0.164, RMSE = 0.201, R2 = 0.974 on the training dataset, MAE = 0.179, RMSE = 0.231, R2 = 0.970 on the testing dataset), the CHIO–SONN also performed well, but it had higher MAE and RMSE values compared to BSO–SONN. However, it still has a good R2 (MAE = 4.212, RMSE = 4.555, R2 = 0.897 on the training dataset, MAE = 3.984, RMSE = 4.379, R2 = 0.904 on the testing dataset), and the SSDO–SONN showed lower performance, especially in terms of R2 (MAE = 1.295, RMSE = 1.545, R2 = 0.261 on the training dataset, MAE = 1.325, RMSE = 1.574, R2 = 0.156 on the testing dataset), indicating that the model might not be capturing the variation in the data well. Another conventional machine learning model, namely support vector machine (SVM), was also applied and compared to the metaheuristic algorithm-optimized models. The SVM's results showed that it performs reasonably well, with good R2 values (MAE = 0.183, RMSE = 0.357, R2 = 0.907 on the training dataset, MAE = 0.292, RMSE = 0.646, R2 = 0.761 on the testing dataset). However, its MAE and RMSE are slightly higher compared to BSO–SONN. As a result, the BSO–SONN model still outperformed all other models in both training and testing datasets, providing the most accurate and reliable predictions of BTS values with the accuracy approximately 97% in practical engineering. The SSDO–SONN model lags behind in terms of R2, suggesting that it may need further improvement. Sensitivity analysis results also suggested that Rn has the highest importance for the BSO–SONN model in predicting BTS. Highlights: Self-organizing neural network is a promising technique to estimate Brazilian tensile strength of rock mass. The Schimidt hammer test, point load test and dry density test based on the ISRM standards were considered to predict Brazilian tensile strength of rock mass. Three hybrid intelligence models were developed for rapid estimating Brazilian tensile strength of rock mass in practical engineering. Brainstorm optimization algorithm provided a robust efficiency in improving the accuracy of the self-organizing neural network with an accuracy of 97%. [ABSTRACT FROM AUTHOR]

The optimal foraging algorithm (OFA) is a swarm-based algorithm motivated by animal behavioral ecology theory. When solving complex optimization problems characterized by multiple peaks, OFA is easy to get trapped in local minima and encounters slow convergence. Therefore, this paper presents an improved optimal foraging algorithm with social behavior based on quasi-opposition (QOS-OFA) to address these problems. First, quasi-opposition-based learning (QOBL) is introduced to improve the overall quality of the population in the initialization phase. Second, an efficient cosine-based scale factor is designed to accelerate the exploration of the search space. Third, a new search strategy with social behavior is designed to enhance local exploitation. The cosine-based scale factor is used as a regulator to achieve a balance between global exploration and local exploitation. The proposed QOS-OFA is compared with seven meta-heuristic algorithms on a CEC benchmark test suite and three real-world optimization problems. The experimental results show that QOS-OFA is better than other competitors on most of the test problems. [ABSTRACT FROM AUTHOR]

Swarm intelligence (SI) algorithms represent a class of Artificial Intelligence (AI) optimization metaheuristics used for solving complex optimization problems. However, a key challenge in solving complex problems is maintaining the balance between exploration and exploitation to find the optimal global solution and avoid local minima. This paper proposes an innovative Swarm Intelligence (SI) algorithm called the Four Vector Intelligent Metaheuristic (FVIM) to address the aforementioned problem. FVIM's search strategy is guided by four top-performing leaders within a swarm, ensuring a balanced exploration-exploitation trade-off in the search space, avoiding local minima, and mitigating low convergence issues. The efficacy of FVIM is evaluated through extensive experiments conducted over two datasets, incorporating both qualitative and quantitative statistical measurements. One dataset contains twenty-three well-known single-objective optimization functions, such as fixed-dimensional and multi-modal functions, while the other dataset comprises the CEC2017 functions. Additionally, the Wilcoxon test was computed to validate the result's significance. The results illustrate FVIM's effectiveness in addressing diverse optimization challenges. Moreover, FVIM has been successfully applied to tackle engineering design problems, such as weld beam and truss engineering design. [ABSTRACT FROM AUTHOR]

Optimization techniques, particularly meta-heuristic algorithms, are highly effective in optimizing and enhancing efficiency across diverse models and systems, renowned for their ability to attain optimal or near-optimal solutions within a reasonable timeframe. In this work, the Puma Optimizer (PO) is proposed as a new optimization algorithm inspired from the intelligence and life of Pumas in. In this algorithm, unique and powerful mechanisms have been proposed in each phase of exploration and exploitation, which has increased the algorithm's performance against all kinds of optimization problems. In addition, a new type of intelligent mechanism, which is a type of hyper-heuristic for phase change, is presented. Using this mechanism, the PO algorithm can perform a phase change operation during the optimization operation and balance both phases. Each phase is automatically adjusted to the nature of the problem. To evaluate the proposed algorithm, 23 standard functions and CEC2019 functions were used and compared with different types of optimization algorithms. Moreover, using the statistical test T-test and the execution time to solve the problem have been discussed. Finally, it has been tested using four machine learning and data mining problems, and the results obtained from all the analysis signifies the excellent performance of this algorithm against all kinds of problems compared to other optimizers. This algorithm has performed better than the compared algorithms in 27 benchmarks out of 33 benchmarks and has obtained better results in solving the clustering problem in 7 data sets out of 10 data sets. Furthermore, the results obtained in the problems of community detection and feature selection and MLP were superior. The source codes of the PO algorithm are publicly available at https://www.mathworks.com/matlabcentral/fileexchange/157231-puma-optimizer-po. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) technology has facilitated different human-related activities and, therefore, has extended to a wide range of applications. However, these networks have the limited resources that must be utilized in an efficient manner. Hence, various studies have introduced several methods and algorithms for better managing these resources. Although these approaches produce acceptable solutions, their performance still needs improvement because of an increasing number of IoT devices and application services. To address such a limitation, the present study proposed a novel IoT environments-specific bandwidth allocation method, which dynamically distributes the wireless bandwidth among the devices according to their heterogeneous nature and the existence of various traffic services. To this end, the Trader metaheuristic algorithm was developed for the discrete problems and formulated as a multi-objective algorithm. To evaluate the performance of the proposed method, it was applied to the six gold standard datasets generated based on the Poisson distribution. The outcomes indicated that the proposed approach surpasses the other introduced state-of-the-art methods in terms of the service success rate by 6.32%, network throughput by 5.79%, and resource efficiency by 3.13%. The results also showed that based on the different statistical criteria, the proposed discrete metaheuristic algorithm yields better outcomes compared to the other efficient algorithms. [ABSTRACT FROM AUTHOR]

The demand for cloud-based computation is increasing exponentially in the age of information technology virtualization, which is increasing data centre energy consumption and service level agreement (SLA) violations. This contributes to global warming and excessive load on existing infrastructure. As a result, it is critical to improve the resource utilisation of cloud data centres (CDCs). Virtual machine (VM) consolidation is the most effective method for optimising resource utilisation in CDCs. In this context, this research proposes the global optimal search-based meta-heuristic algorithm black widow adaptive VM placement (BWAVP) approach-based VM consolidation that combines energy conservation, resource utilisation and required quality of services with accurate VM-PM mapping. The investigation of the BWAVP approach shows that it reduces energy consumption by 18% on average when compared to other VM placement approaches, and reduces SLA violations, and VM migrations by more than 80%. Further, The research proposed a localized adaptive over and underutilisation host detection technique that further reduces energy consumption by 10% while maintaining the quality of services (QoSs) at the same level. [ABSTRACT FROM AUTHOR]

Differential Evolution (DE) is a population-based metaheuristic search algorithm that exhibits excellent performance. However, it is sensitive to mutation strategies and control parameters.To mitigate the impact of these factors, this paper proposes refining differential evolution with mutation rate and neighborhood weight local search(MRNLDE). The algorithm guides individuals in selecting an appropriate mutation mode through the mutation rate, effectively utilizing the evolutionary information inherent in the individuals themselves, thereby enhancing the search efficiency of the algorithm. Furthermore, the neighborhood individual information is utilized to re-explore the solution space, allowing for a comprehensive exploration of individual evolutionary potential and enhancing the algorithm's diversity. The performance of MRNLDE was validated under two sets of benchmark problems at the Institute of Electrical and Electronics Engineers (IEEE) Conference on Computing in Evolution (CEC), and the results show that MRNLDE performs well overall. [ABSTRACT FROM AUTHOR]

This work presents two solution methods for the m-machine robust permutation flow shop problem with processing time uncertainty. The goal is to minimize the makespan of the worst-case scenario by utilizing an approach based on budgeted uncertainty, in which only a subset of operations will reach their worst-case processing time values. To obtain efficient solutions to this problem, we first extend an existing two-machine worst-case procedure, based on dynamic programming, generalizing it to m machines. The worst-case calculation is then incorporated into two proposed solution methods: an exact column-and-constraint generation algorithm and a GRASP metaheuristic. Based on experiments with four sets of literature-based instances, empirical results demonstrate the ability of the GRASP to efficiently produce an optimal or near-optimal solution in most cases. [ABSTRACT FROM AUTHOR]

The main resources in software projects are human resources equipped with various skills, which makes software development a typical intelligence-intensive process. Therefore, effective human resource scheduling is indispensable for the success of software projects. The aim of software project scheduling is to assign the right employee to the right activity at the right time. Uncertainty is inevitable in software development, which further complicates the scheduling of projects. We investigate the software project scheduling problem with uncertain activity durations (SPSP-UAD) and aim at obtaining effective scheduling policies for the problem. We present and transform a scenario-based non-linear chance-constrained stochastic programming model into an equivalent linear programming model. To solve the NP-hard SPSP-UAD efficiently, we develop a hybrid meta-heuristic TLBO-GA that combines the teaching–learning-based optimization algorithm (TLBO) and the genetic algorithm (GA). Our TLBO-GA is also equipped with some problem-specific operators, such as population initialization, rows exchange and local search. We use simulation to evaluate the scheduling policies obtained by our algorithms. Extensive computational experiments are conducted to evaluate the performance of our TLBO-GA in comparison to the exact solver CPLEX and four existing meta-heuristic algorithms. The comparative results reveal the effectiveness and efficiency of our TLBO-GA. Our TLBO-GA provides an extensible and adaptive automated scheduling decision support tool for the software project manager in the complex and uncertain software development environment. [ABSTRACT FROM AUTHOR]

This study aims to address the problem of optimal power flow (OPF) in electrical networks by integrating wind power production FACTS devices. The main objectives of this study include minimizing generating costs, reducing power loss, enhancing the voltage profile of the system, and increasing its load ability. In this study, the tuna swarm optimization algorithm (TSO) is utilized as a novel meta-heuristic technique to optimize electrical networks to incorporate FACTS devices and stochastic wind energy production. To account for wind power, a model based on the Weibull probability density function is utilized to identify the optimal values of the decision variables. The study compares several objective functions, including minimization of fuel cost and active power loss across the transmission system, and simulates the test system consisting of TCSC, TCPS, and SVC using the IEEE 30-bus system as a network for examining system parameters. The efficacy of the TSO methodology is explored and compared to other traditional approaches in the paper. The simulation findings of the study show that by lowering the overall power cost and power losses, TSO is more successful in determining the OPF's ideal solution. The results show that the TSO algorithm performs better than driving training-based optimization (DTBO), Coulomb–Franklin's algorithm (CFA), and whale optimization algorithm (WOA) since it can handle more difficult OPF issues with a lower convergence rate. When comparing the four methods, for instance, TSO produced a noticeable improvement. It was able to successfully reduce the cost function to 807.454 $/h which is better than 807.9545 $/h for CFA, 809.4945 for DTBO, and 828.89 $/h for WOA, while lowering the power loss to 1.842 MW which is lower than the losses incurred by CFA, DTBO, and WOA, which are 1.9137 MW, 2.703 MW, and 2.757 MW, respectively. Furthermore, in Case 3, the gross cost reduced to 914.3735 $/h. [ABSTRACT FROM AUTHOR]

Binary metaheuristic algorithms prove to be invaluable for solving binary optimization problems. This paper proposes a binary variant of the peacock algorithm (PA) for feature selection. PA, a recent metaheuristic algorithm, is built upon lekking and mating behaviors of peacocks and peahens. While designing the binary variant, two major shortcomings of PA (lek formation and offspring generation) were identified and addressed. Eight binary variants of PA are also proposed and compared over mean fitness to identify the best variant, called binary peacock algorithm (bPA). To validate bPA's performance experiments are conducted using 34 benchmark datasets and results are compared with eight well-known binary metaheuristic algorithms. The results show that bPA classifies 30 datasets with highest accuracy and extracts minimum features in 32 datasets, achieving up to 99.80% reduction in the feature subset size in the dataset with maximum features. bPA attained rank 1 in Friedman rank test over all parameters. [ABSTRACT FROM AUTHOR]

In order to mitigate/prevent the risks of landslides, one of the essential tools that can be used to manage and plan the development of human settlements is landslide susceptibility. The two metaheuristic algorithms explored in this paper are the SCE and VSA algorithms used to optimize the artificial neural network (ANN) model. By integrating the two algorithms with the artificial neural network model, we try to determine its optimum computational parameters in order to generate the landslide susceptibility mapping for the Kurdistan province in Iran. Sixteen causative factors of landslides are included in the spatial database. The landslide susceptibility maps were generated in a GIS medium, and in order to evaluate the employed predictive models, the criterion of the area under the curve (AUC) was employed. This investigation includes 1072 landslide events, which are divided as follows: one-third as testing data and two-thirds as training data (i.e., a 75:25 ratio). The results indicate that after using the abovementioned algorithms, the AUC increased noticeably from 0.708 to 0.788 for SCE-MLP and from 0.744 to 0.818 for VSA-MLP in the training phase. The criterion of the area under the curve was utilized in order to evaluate the accuracy of the employed probabilistic models. Incidentally, the comparable AUCs calculated for the VSA-MLP testing databases and the obtained AUCs were 0.818, 0.801, 0.791, 0.786, 0.784, 0.778, 0.777, 0.776, 0.754 and 0.744, respectively for population size in training databases equal to 50, 350, 500, 150, 450, 200, 400, 300, 100 to and 250. Also, in case of SCE-MLP, the training and testing AUC were found (0.878, 0.865, 0.851, 0.850, 0.819, 0.816, 0.815, 0.781, 0.760, and 0.756) and (0.788, 0.782, 0.759, 0.744, 0.727, 0.720, 0.718, 0.713 and 0.708). The best fit for swarm size conditions of the SCE-MLP and VSA-MLP model showed 150 and 350, respectively. The acquired results indicate that the VSA-ANN model has a better predictive capability in optimization of the structure of the artificial neural network model and computational parameters in comparison with the SCE-ANN. [ABSTRACT FROM AUTHOR]

In the context of workflows, scheduling involves the systematic arrangement of tasks, allocation of resources, and coordination of dependencies to achieve desired outcomes. Effective scheduling is paramount in cloud–fog computing environments to harness the full potential of distributed resources while meeting application requirements. However, finding the most efficient solution often involves the quest for an optimal algorithm. An optimal algorithm is one that produces the best possible outcome in terms of a specified objective. While metaheuristic algorithms have been proposed by several researchers to address this issue, the solutions often converge at the local level, limiting their effectiveness. This study proposes the hybrid particle whale optimization algorithm as a solution for workflow scheduling problems. It is a combination of particle swarm optimization (PSO) and the whale optimization algorithm (WOA). By integrating the features of both algorithms, this algorithm tries to avoid becoming trapped in local optima. This algorithm aims to minimize the total execution cost, makespan, and energy consumption of tasks in a cloud–fog computing environment. The performance of the proposed algorithm is compared with that of the round robin, ant colony optimization, PSO, and WOA algorithms using five different scientific workflows, namely, the Inspiral, Montage, Epigenomics, Cybershake, and Sipht algorithms, which deploy various numbers of tasks and VMs. The proposed algorithm outperformed the other existing scheduling strategies. Moreover, the approach provides users with the flexibility to customize the scheduling strategy based on their preferences. Overall, this paper presents a novel approach for workflow scheduling in cloud–fog computing environments that improves the performance and efficiency of applications while also providing users with greater control over the scheduling process. [ABSTRACT FROM AUTHOR]

Power Quality (PQ) concerns is the main concern particularly voltage disturbances, fluctuation, sag, harmonics, and swell in the power distribution system. An efficacious device namely Dynamic voltage restorer (DVR) has the extraordinary dynamic reliability which is integrated between the source and load to compensate such voltage disturbances in the electric power distribution system. However, in this paper DVR is incorporated with source to protect the sensitive load from the voltage disturbance influenced by the source side under non-linear load condition. Furthermore, in order to enhance the restoration of the voltage property of DVR, a novel Z type source Inverter (ZSI) is presented in this work. The constant DC voltage of DC link utilized for compensation process is ensured by this ZSI. In addition, the hybrid Harris Hawks Optimization (HHO) and Black Widow Optimization (BWO) is used to control the Z-DVR by regulating the gain parameters of the PID controller and to compensating the voltage issues such as sag, fluctuation, swell, and interruption in the load side. This proposed work is implemented using the MATLAB/Simulink software with the version of 2023b to analyze the results. In addition, to confirm the superiority of the proposed work HHO–BWO which is compared with previous work such as BWO-PID, HHO-PID, GA-PID, and MRSF controller in form of total harmonic distribution (THD), computational complexity and mitigation of PQ issues. [ABSTRACT FROM AUTHOR]

This paper proposes a multi-strategy modified seagull algorithm to optimize DV-Hop localization algorithm (DISO) to improve the precision of non-range-ranging localization algorithm in wireless sensor networks. Firstly, the algorithm analyzes the causes of errors in the positioning of the traditional non-ranging location algorithm DV-Hop, and improves these steps. Among them, the communication area of anchor nodes is divided by different radii, so as to reduce the influence of distance on hop number. The node distribution is stochastic, so the mean square error is used instead of the unbiased estimation, and the weight is introduced to calculate the average jump distance, which reduces the error caused by the random distribution of nodes. Secondly, the objective function optimization method is used to replace the trilateral measurement, and the improved seagull optimization algorithm is used for iterative optimization. Finally, the seagull optimization algorithm is modified in view of its shortcomings. The chaotic mapping was used to initialize the seagull population and increase its diversity. The flight parameters of seagull and the position update methods of the worst and best seagull are improved, and the optimization ability of the algorithm is improved by combining levy flight mechanism and T distribution variation strategy. The simulation results show that the initial population distribution of DISO algorithm is more uniform, which establishes a basic advantage for the subsequent optimization. Keeping the other parameters consistent, DISO algorithm has higher positioning accuracy than other comparison algorithms, no matter changing the number of anchor nodes or the total number of nodes or changing the communication radius. The positioning errors of DISO algorithm are reduced by 45.63%, 17.17%, 22.61% and 11.68% compared with DV-Hop algorithm and other comparison algorithms under different number of anchor nodes. Under different total number of nodes, the positioning error is reduced by 49.91%, 20.81%, 35.80% and 9.20%. Under different communication radius, the positioning error is reduced by 55.47%, 21.07%, 24.84% and 13.11%. It is proved that DISO algorithm has more accurate localization results. [ABSTRACT FROM AUTHOR]

During the last decades many metaheuristics for global numerical optimization have been proposed. Among them, Basin Hopping is very simple and straightforward to implement, although rarely used outside its original Physical Chemistry community. In this work, our aim is to compare Basin Hopping, and two population variants of it, with readily available implementations of the well known metaheuristics Differential Evolution, Particle Swarm Optimization, and Covariance Matrix Adaptation Evolution Strategy. We perform numerical experiments using the IOH profiler environment with the BBOB test function set and two difficult real-world problems. The experiments were carried out in two different but complementary ways: by measuring the performance under a fixed budget of function evaluations and by considering a fixed target value. The general conclusion is that Basin Hopping and its newly introduced population variant are almost as good as Covariance Matrix Adaptation on the synthetic benchmark functions and better than it on the two hard cluster energy minimization problems. Thus, the proposed analyses show that Basin Hopping can be considered a good candidate for global numerical optimization problems along with the more established metaheuristics, especially if one wants to obtain quick and reliable results on an unknown problem. [ABSTRACT FROM AUTHOR]

In metropolitan cities, it is very complicated to govern the optimum routes for garbage collection vehicles due to high waste production and very dense population. Furthermore, wrongly designed routes are the source of wasting time, fuel and other resources in the collection of municipal trash procedure. The Vehicle Routing Problem (VRP) published between 2011 and 2023 was systematically analysed. The majority of the surveyed research compute the waste collecting problems using metaheuristic approaches. This manuscript serves two purposes: first, categorising the VRP and its variants in the field of waste collection; second, examining the role played by most of the metaheuristics in the solution of the VRP problems for a waste collection. Three case study of Asia continent has been analysed and the results show that the metaheuristic algorithms have the capability in providing good results for large-scale data. Lastly, some promising paths ranging from highlighting research gap to future scope are drawn to encourage researchers to conduct their research work in the field of waste management route problems. [ABSTRACT FROM AUTHOR]