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This paper reviews the papers on the applications of VNS in the health care area by analyzing the characteristics of VNS in different problems. In the health care field, many complex optimization problems need to be tackled in a short time considering multiple influencing factors, such as personnel preferences, resources limitations, etc. As a metaheuristic, Variable neighborhood search (VNS) algorithm can get an approximate solution for the complex problem in a short time, and has been widely used to deal with the real-world health care optimization problems based on systematic neighborhood changes and perturbation operations when needed. We classify the found papers into five categories based on the topics, i.e., home health care problems, operating room problems, nurse rostering problems, routing optimization problems, and other topics. For each topic, the detailed operations of VNS and the differences in solving various problems have been discussed. In addition, we analyze the studied problems and summarize the approaches used in the literature. The application tendency of VNS in the health care area is also discussed at the end of the paper. [ABSTRACT FROM AUTHOR]

During the optimization process, a large number of data are generated through the search. Machine learning techniques and algorithms can be used to handle the generated data to contribute to the optimization process. The use of machine learning enhancing metaheuristics applied to optimization problems has been drawing attention due to their capacity to add domain knowledge during the search process. This knowledge can accelerate metaheuristics and lead to better and promising solutions. This work provides a systematic literature review of machine learning enhancing metaheuristics and summarizes the current state of the classification of the research field, main techniques and machine learning models, validations strategies, and real-world optimization problems that the approach was applied. Our keyword search found 1.960 papers, published in the last 10 years. After considering the inclusion and exclusion criteria and performing backward snowballing procedure, we have analyzed 111 primary studies. The results show the predominance of the use of surrogate-assisted evolutionary algorithms (SAEAs) for improving the efficiency of the optimization, and the use of estimation of distribution algorithms (EDAs) to increase the effectiveness of the optimization. The objective function value is the mostly applied evaluating criteria to validate the algorithm with other methods. The developed techniques of the studies found are applied in diverse real-world applications such as developing machine learning models, physics simulations with expensive function evaluation, and the variants of the classical job shop scheduling problem. We also discuss trends and opportunities of the research field. [ABSTRACT FROM AUTHOR]

This paper provides a recent overview of the exact, approximate, and hybrid optimization methods that handle Multi-Objective Mixed-Integer Non-Linear Programming (MO-MINLP) problems. Both the domains of exact and approximate research have experienced significant growth, driven by their shared goal of addressing a wide range of real-world problems. This work presents a comprehensive literature review that highlights the significant theoretical contributions in the field of hybrid approaches between these research areas. We also point out possible research gaps in the literature. Hence, the main research questions to be answered in this paper involve the following: (1) how to exactly or approximately solve a MO-MINLP problem? (2) What are the drawbacks of exact methods as well as approximate methods? (3) What are the research lines that are currently underway to enhance the performances of these methods? and (4) Where are the research gaps in this field? This work aims to provide enough descriptive information for newcomers in this area about the research that has been carried out and that is currently underway concerning exact, approximate, and hybrid methods used to solve MO-MINLP problems. [ABSTRACT FROM AUTHOR]

Ship maintenance service optimisation is of great significance for improving the competitiveness of shipbuilding enterprises. In this paper, we investigate a ship maintenance service scheduling problem considering the deteriorating maintenance time, distributed maintenance tasks, and limited maintenance teams. The objective is to minimise the service span. First, we construct an initial mixed-integer programming model for the studied problem. Then, through the property analysis of the problem with a single maintenance team, an exact scheduling algorithm is proposed. In addition, the lower bound of the problem with multiple maintenance teams is derived. A scheduled rule is developed to obtain the lower bound for the problem. Based on the property analysis, the original mixed-integer programming model is simplified to an improved mathematical programming model. Since the studied problem is NP-hard, this paper proposes two heuristic algorithms and an integrated metaheuristic algorithm based on the variable neighbourhood search to obtain approximate optimal solutions in a reasonable time. In computational experiments, the two models can solve problems on small scale, while metaheuristics can find approximately optimal solutions in each problem category. Moreover, the computational results validate the performance of the proposed integrated metaheuristic in terms of convergence and stability. [ABSTRACT FROM AUTHOR]

Metaheuristic algorithms are increasingly utilized to solve complex optimization problems because they can efficiently explore large solution spaces. The moss growth optimization (MGO), introduced in this paper, is an algorithm inspired by the moss growth in the natural environment. The MGO algorithm initially determines the evolutionary direction of the population through a mechanism called the determination of wind direction, which employs a method of partitioning the population. Meanwhile, drawing inspiration from the asexual reproduction, sexual reproduction, and vegetative reproduction of moss, two novel search strategies, namely spore dispersal search and dual propagation search, are proposed for exploration and exploitation, respectively. Finally, the cryptobiosis mechanism alters the traditional metaheuristic algorithm's approach of directly modifying individuals' solutions, preventing the algorithm from getting trapped in local optima. In experiments, a thorough investigation is undertaken on the characteristics, parameters, and time cost of the MGO algorithm to enhance the understanding of MGO. Subsequently, MGO is compared with 10 original and advanced CEC 2017 and CEC 2022 algorithms to verify its performance advantages. Lastly, this paper applies MGO to four real-world engineering problems to validate its effectiveness and superiority in practical scenarios. The results demonstrate that MGO is a promising algorithm for tackling real challenges. The source codes of the MGO are available at https://aliasgharheidari.com/MGO.html and other websites. [ABSTRACT FROM AUTHOR]

An efficient variant of the recent sea horse optimizer (SHO) called SHO-OBL is presented, which incorporates the opposition-based learning (OBL) approach into the predation behavior of SHO and uses the greedy selection (GS) technique at the end of each optimization cycle. This enhancement was created to avoid being trapped by local optima and to improve the quality and variety of solutions obtained. However, the SHO can occasionally be vulnerable to stagnation in local optima, which is a problem of concern given the low diversity of sea horses. In this paper, an SHO-OBL is suggested for the tackling of genuine and global optimization systems. To investigate the validity of the suggested SHO-OBL, it is compared with nine robust optimizers, including differential evolution (DE), grey wolf optimizer (GWO), moth-flame optimization algorithm (MFO), sine cosine algorithm (SCA), fitness dependent optimizer (FDO), Harris hawks optimization (HHO), chimp optimization algorithm (ChOA), Fox optimizer (FOX), and the basic SHO in ten unconstrained test routines belonging to the IEEE congress on evolutionary computation 2020 (CEC'20). Furthermore, three different design engineering issues, including the welded beam, the tension/compression spring, and the pressure vessel, are solved using the proposed SHO-OBL to test its applicability. In addition, one of the most successful approaches to data transmission in a wireless sensor network that uses little energy is clustering. In this paper, SHO-OBL is suggested to assist in the process of choosing the optimal power-aware cluster heads based on a predefined objective function that takes into account the residual power of the node, as well as the sum of the powers of surrounding nodes. Similarly, the performance of SHO-OBL is compared to that of its competitors. Thorough simulations demonstrate that the suggested SHO-OBL algorithm outperforms in terms of residual power, network lifespan, and extended stability duration. [ABSTRACT FROM AUTHOR]

One of the biggest problems in gaming AI is related to how we can optimize and adapt a deep reinforcement learning (DRL) model, especially when it is running inside complex, dynamic environments like "PacMan". The existing research has concentrated more or less on basic DRL approaches though the utilization of advanced optimization methods. This paper tries to fill these gaps by proposing an innovative methodology that combines DRL with high-level metaheuristic optimization methods. The work presented in this paper specifically refactors DRL models on the "PacMan" domain with Energy Serpent Optimizer (ESO) for hyperparameter search. These novel adaptations give a major performance boost to the AI agent, as these are where its adaptability, response time, and efficiency gains start actually showing in the more complex game space. This work innovatively incorporates the metaheuristic optimization algorithm into another field—DRL—for Atari gaming AI. This integration is essential for the improvement of DRL models in general and allows for more efficient and real-time game play. This work delivers a comprehensive empirical study for these algorithms that not only verifies their capabilities in practice but also sets a state of the art through the prism of AI-driven game development. More than simply improving gaming AI, the developments could eventually apply to more sophisticated gaming environments, ongoing improvement of algorithms during execution, real-time adaptation regarding learning, and likely even robotics/autonomous systems. This study further illustrates the necessity for even-handed and conscientious application of AI in gaming—specifically regarding questions of fairness and addiction. [ABSTRACT FROM AUTHOR]

Demand-Driven Material Requirements Planning (DDMRP) is a recent inventory management method that has generated considerable interest in both academia and industry. Many recent papers have demonstrated the superiority of DDMRP over classical methods like MRP or Kanban, an observation confirmed by companies that have implemented DDMRP. However, DDMRP depends on many parameters that affect its performance. Only general rules are given by the authors of the method to fix these parameters but no algorithm. The present paper aims to fill this gap by proposing a multi-objective optimisation algorithm to fix a set of eight identified parameters. The suggested genetic algorithm is coupled with a simulation algorithm that computes the objective functions. Two opposing objective functions are considered: first, the maximisation of orders delivered on-time to the customer and, second, the minimisation of on-hand inventory. A set of data instances was generated to test the suggested method. Fronts of non-dominated solutions are found for all these instances. [ABSTRACT FROM AUTHOR]

The Robotic Assembly Line Balancing Problem (RALBP) is a joint optimisation problem that is concerned with assigning both assembly operations and robots to workstations that are placed within a straight line. RALBP-2 is the particular problem where the cycle time, which is the maximum time spent on a workstation by the product being assembled, is minimised while the number of workstations is fixed. Sequence-dependent setup times are considered which raises the problem of sequencing the operations assigned to each workstation. Both the durations of the operations and the setup times depend on the robot. Two different variants are identified from literature. The first variant assumes that, given a set of types of robots, each type of robot can be assigned to multiple workstations without any limitation. Given a set of robots, the second variant forces each robot to be assigned to at most one workstation. Both assumptions are studied in this paper. The particular case of a given giant sequence of operations is solved thanks to a polynomial optimal algorithm. The latter algorithm, called split, is then embedded in a metaheuristic framework that explores the space of giant sequences. Benchmark data sets from literature are considered in the experimental section. A comparative study with other methods from literature shows the competitiveness of the suggested approach. [ABSTRACT FROM AUTHOR]

A traditional logistics decision model mainly studies the location decision of logistics distribution centers, storage inventory management, vehicle scheduling, and transportation routes. The logistics location-inventory-routing problem (LIRP) is an integrated optimization of the three problems—a comprehensive optimization problem for the whole logistics system. This review paper uses the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) method to review the literature on LIRP systematically. A total of 112 LIRP-related studies published between 2010 and 2021 are reviewed and classified based on 10 abstract and citation databases. The classification includes four aspects: problem characteristics, demand data types, model-based solutions, and application fields. From this systematic review, a few observations are recorded. First, the most popular problems among researchers are the multi-period multi-product problem, the multi-echelon single-link problem, and the multi-depot multi-retailer problem. Based on the objective function, the minimization of total supply chain cost is the primary concern of the LIRP literature. Researchers also favor other problem characteristics such as multi-objective programming, inventory control replenishment policy, and a homogeneous fleet of vehicles. We found that stochastic data are a common factor in an uncertain environment and have broad coverage. When dealing with the LIRP, heuristic and metaheuristic algorithms are the most widely used solution methodologies in the literature. In the application field of LIRP, the perishable products logistics network is mentioned in most applications. Finally, we discuss and emphasize the challenges of and recommendations for future work. This paper provides a systematic review of the literature on LIRP based on the PRISMA method, which contributes vital support and valuable information for researchers interested in LIRP. [ABSTRACT FROM AUTHOR]

Over the past decade, metaheuristic algorithms have gained significant attention from researchers due to their effectiveness and computational efficiency. Conventional clustering algorithms often suffer from various limitations, but the use of metaheuristic algorithms into clustering has shown promising results in achieving globally optimal centroid positions within clusters. The paper shows the implementation of metaheuristic algorithms with the kernel possibilistic fuzzy c-means algorithm (KPFCM), leading to notable improvements under normal as well as under noisy conditions. Furthermore, this paper focuses on optimizing the objective functions (case-1: single objective function; case-2: multiobjective function) through the utilization of the kernel trick and the probabilistic nature of metaheuristic algorithms, specifically genetic algorithm (GA), particle swarm optimization (PSO), and teaching learning-based optimization (TLBO) algorithm. The proposed approach is evaluated on six benchmark datasets, considering both single objective function optimization (case-1) and multiobjective function optimization (case-2). In case-1, three hybrid algorithms are introduced for single objective function optimization: the genetic algorithm-based kernel possibilistic fuzzy c-means (GA-KPFCM) algorithm, the particle swarm optimization-based kernel possibilistic fuzzy c-means (PSO-KPFCM) algorithm, and the teaching learning-based optimization with kernel possibilistic fuzzy c-means (TLBO-KPFCM) algorithm. Results obtained from these algorithms demonstrate improved performance compared to traditional possibilistic fuzzy c-means (PFCM) and kernel possibilistic fuzzy c-means (KPFCM) algorithms. Additionally, a comparative analysis of hybrid metaheuristic with kernel possibilistic fuzzy c-means algorithms is conducted against hybrid metaheuristic fuzzy c-means algorithms and hybrid metaheuristic possibilistic fuzzy c-means algorithms, confirming the superiority of the proposed hybrid combinations. For multiobjective optimization (MOO) clustering, a Pareto front is established using the concept of non-dominated solutions. The proposed multiobjective hybrid algorithms (case-2) for optimization include the multiobjective particle swarm optimization kernel possibilistic fuzzy c-means (MPSO-KPFCM) algorithm, the non-dominated sorting genetic algorithm third generation kernel possibilistic fuzzy c-means (NSGAIII-KPFCM) algorithm, and the non-dominated sorting teaching learning-based optimization kernel possibilistic fuzzy c-means (NSTLBO-KPFCM) algorithm. These algorithms demonstrate their effectiveness in achieving optimal solutions for multiobjective clustering problems. [ABSTRACT FROM AUTHOR]

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Radiomics is a non-invasive method for extracting quantitative and higher-dimensional features from medical images for diagnosis. It has received great attention due to its huge application prospects in recent years. We can know that the number of features selected by the existing radiomics feature selectionmethods is basically about ten. In this paper, a heuristic feature selection method based on frequency iteration and multiple supervised training mode is proposed. Based on the combination between features, it decomposes all features layer by layer to select the optimal features for each layer, then fuses the optimal features to form a local optimal group layer by layer and iterates to the global optimal combination finally. Compared with the currentmethod with the best prediction performance in the three data sets, thismethod proposed in this paper can reduce the number of features fromabout ten to about three without losing classification accuracy and even significantly improving classification accuracy. The proposed method has better interpretability and generalization ability, which gives it great potential in the feature selection of radiomics. [ABSTRACT FROM AUTHOR]

The software requirement selection problem endeavors to ascertain the optimal set of software requirements with the dual objectives of minimizing software cost and maximizing customer satisfaction. The intricate nature of this problem stems from the interdependencies among individual software requirements, rendering it a complicated NP-hard problem. In this paper, we introduce a novel multi-objective optimization algorithm christened the Quantum -Inspired Multi-Objective African Vulture Optimization Algorithm with Hierarchical Structures (QMO_HSAVOA), where hierarchical structure and in-quantum computation ideas are introduced to improve the performance of the algorithm in QMO_HSAVOA. To gauge the efficacy of QMO_HSAVOA in tackling the software requirement selection problem, we empirically apply it to the problem, orchestrating three distinct simulation experiments. The ensuing evaluation of QMO_HSAVOA's performance is conducted with meticulous scrutiny through the application of Friedman's statistical test to the experimental outcomes. These results decisively demonstrate that the proposed QMO_HSAVOA not only delivers exceptionally competitive outcomes but also outshines alternative algorithms. This finding provision is an innovative and highly efficient solution for addressing the software requirement selection problem. [ABSTRACT FROM AUTHOR]

As one of the important concepts in epidemiology, herd immunity was recommended to control the COVID-19 pandemic. Inspired by this technique, the Coronavirus Herd Immunity Optimiser has recently been introduced, demonstrating promising results in addressing optimisation problems. This particular algorithm has been utilised to address optimisation problems widely; However, there is room for enhancement in its performance by making modifications to its parameters. This paper aims to improve the Coronavirus Herd Immunity Optimisation algorithm to employ it in addressing breast cancer diagnosis problem through feature selection. For this purpose, the algorithm was discretised after the improvements were made. The Opposition-Based Learning approach was applied to balance the exploration and exploitation stages to enhance performance. The resulting algorithm was employed in the diagnosis of breast cancer, and its performance was evaluated on ten benchmark functions. According to the simulation results, it demonstrates superior performance in comparison with other well-known approaches of the similar nature. The results demonstrate that the new approach performs well in diagnosing breast cancer with high accuracy and less computational complexity and can address a variety of real-world optimisation problems. [ABSTRACT FROM AUTHOR]

When dealing with complex models in real situations, many optimization problems require the use of more than one objective function to adequately represent the relevant characteristics of the system under consideration. Multi-objective optimization algorithms that can deal with several objective functions are necessary in order to obtain reasonable results within an adequate processing time. This paper presents the multi-objective version of a recent metaheuristic algorithm that optimizes a single objective function, known as the Majority–minority Cellular Automata Algorithm (MmCAA), inspired by cellular automata operations. The algorithm presented here is known as the Multi-objective Majority–minority Cellular Automata Algorithm (MOMmCAA). The MOMmCAA adds repository management and multi-objective search space density control to complement the performance of the MmCAA and make it capable of optimizing multi-objective problems. To evaluate the performance of the MOMmCAA, results on benchmark test sets (DTLZ, quadratic, and CEC-2020) and real-world engineering design problems were compared against other multi-objective algorithms recognized for their performance (MOLAPO, GS, MOPSO, NSGA-II, and MNMA). The results obtained in this work show that the MOMmCA achieves comparable performance with the other metaheuristic methods, demonstrating its competitiveness for use in multi-objective problems. The MOMmCAA was implemented in MATLAB and its source code can be consulted in GitHub. [ABSTRACT FROM AUTHOR]

The increasing sophistication and frequency of cyber threats necessitate the development of advanced techniques for detecting and mitigating attacks. This paper introduces a novel cybersecurity-focused Multi-Layer Perceptron (MLP) trainer that utilizes evolutionary computation methods, specifically tailored to improve the training process of neural networks in the cybersecurity domain. The proposed trainer dynamically optimizes the MLP's weights and biases, enhancing its accuracy and robustness in defending against various attack vectors. To evaluate its effectiveness, the trainer was tested on five widely recognized security-related datasets: NSL-KDD, CICIDS2017, UNSW-NB15, Bot-IoT, and CSE-CIC-IDS2018. Its performance was compared with several state-of-the-art optimization algorithms, including Cybersecurity Chimp, CPO, ROA, WOA, MFO, WSO, SHIO, ZOA, DOA, and HHO. The results demonstrated that the proposed trainer consistently outperformed the other algorithms, achieving the lowest Mean Square Error (MSE) and highest classification accuracy across all datasets. Notably, the trainer reached a classification rate of 99.5% on the Bot-IoT dataset and 98.8% on the CSE-CIC-IDS2018 dataset, underscoring its effectiveness in detecting and classifying diverse cyber threats. [ABSTRACT FROM AUTHOR]

Traffic lights play an important role in efficient traffic management, especially in crowded cities. Optimizing traffic helps to reduce crowding, save time, and ensure the smooth flow of traffic. Metaheuristic algorithms have a proven ability to optimize smart traffic management systems. This paper investigates the effectiveness of two metaheuristic algorithms: particle swarm optimization (PSO) and grey wolf optimization (GWO). In addition, we posit a hybrid PSO-GWO method of optimizing traffic light control using IoT-enabled data from sensors. In this study, we aimed to enhance the movement of traffic, minimize delays, and improve overall traffic precision. Our results demonstrate that the hybrid PSO-GWO method outperforms individual PSO and GWO algorithms, achieving superior traffic movement precision (0.925173), greater delay reduction (0.994543), and higher throughput improvement (0.89912) than standalone methods. PSO excels in reducing wait times (0.7934), while GWO shows reasonable performance across a range of metrics. The hybrid approach leverages the power of both PSO and GWO algorithms, proving to be the most effective solution for smart traffic management. This research highlights using hybrid optimization techniques and IoT (Internet of Things) in developing traffic control systems. [ABSTRACT FROM AUTHOR]

The correct diagnosis and early treatment of respiratory diseases can significantly improve the health status of patients, reduce healthcare expenses, and enhance quality of life. Therefore, there has been extensive interest in developing automatic respiratory disease detection systems. Most recent methods for detecting respiratory disease use machine and deep learning algorithms. The success of these machine learning methods depends heavily on the selection of proper features to be used in the classifier. Although metaheuristic-based feature selection methods have been successful in addressing difficulties presented by high-dimensional medical data in various biomedical classification tasks, there is not much research on the utilization of metaheuristic methods in respiratory disease classification. This paper aims to conduct a detailed and comparative analysis of six widely used metaheuristic optimization methods using eight different transfer functions in respiratory disease classification. For this purpose, two different classification cases were examined: binary and multi-class. The findings demonstrate that metaheuristic algorithms using correct transfer functions could effectively reduce data dimensionality while enhancing classification accuracy. [ABSTRACT FROM AUTHOR]

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

Complex and nonlinear optimization challenges pose significant difficulties for traditional optimizers, which often struggle to consistently locate the global optimum within intricate problem spaces. To address these challenges, the development of hybrid methodologies is essential for solving complex, real-world, and engineering design problems. This paper introduces FVIMDE, a novel hybrid optimization algorithm that synergizes the Four Vector Intelligent Metaheuristic (FVIM) with Differential Evolution (DE). The FVIMDE algorithm is rigorously tested and evaluated across two well-known benchmark suites (i.e., CEC2017, CEC2022) and an additional set of 50 challenging benchmark functions. Comprehensive statistical analyses, including mean, standard deviation, and the Wilcoxon rank-sum test, are conducted to assess its performance. Moreover, FVIMDE is benchmarked against state-of-the-art optimizers, revealing its superior adaptability and robustness. The algorithm is also applied to solve five structural engineering challenges. The results highlight FVIMDE's ability to outperform existing techniques across a diverse range of optimization problems, confirming its potential as a powerful tool for complex optimization tasks. [ABSTRACT FROM AUTHOR]

This paper presents a modified metaheuristic algorithm named the modified Firefly algorithm. Any metaheuristic algorithm will have exploration and exploitation steps, and the goal of modification is to maintain a balance between them. The improvement relies on movement equations, alterations to the algorithm's structure by introducing a single loop, and a selection of movement equations at random. Two movement equations are included in the improved method and are randomly selected. This guarantees both regionally and globally focused solution-finding. This prevents the algorithm from getting stuck at a local minimum. Comparing the modified version to the original Firefly method, just one for loop is used, reducing the algorithm's complexity. The algorithm's performance is evaluated with 35 traditional benchmark test functions and 10 CEC2019 test functions. According to the findings, the suggested method performed optimally in 24 traditional benchmark test functions and best in the six remaining benchmark test functions. The improved algorithm produced the best outcomes in seven of the 10 CEC2019 test functions. In contrast, the Firefly algorithm produced optimal results in 18 classical benchmark test functions and the best results in 6 CEC2019 test functions. The proposed algorithm is compared with other variants of the Firefly algorithm for common test functions in the literature. The results show that the proposed algorithm outperforms other variants in most test functions. [ABSTRACT FROM AUTHOR]

The personalization of curriculum plays a pivotal role in supporting students in achieving their unique learning goals. In recent years, researchers have dedicated efforts to address the challenge of personalizing curriculum through diverse techniques and approaches. However, it is crucial to acknowledge the phenomenon of student forgetting, as individuals exhibit variations in limitations, backgrounds, and goals, as evidenced by studies in the field of learning sciences. This paper introduces the complex issue of fully individualizing a curriculum while considering the impact of student forgetting, presenting a comprehensive framework to tackle this problem. Moreover, we conduct two experiments to explore this issue, aiming to assess the difficulty of identifying relevant curricula within this context and uncover behavioral patterns associated with the problem. The findings from these experiments provide valuable prescriptive recommendations for educational stakeholders seeking to implement personalized approaches. Furthermore, we demonstrate the complexity of this problem, highlighting the need for our framework as an initial decision-making tool to address this challenging endeavor. [ABSTRACT FROM AUTHOR]

Metaheuristic algorithm is a generalization of heuristic algorithm that can be applied to almost all optimization problems. For optimization problems, metaheuristic algorithm is one of the methods to find its optimal solution or approximate solution under limited conditions. Most of the existing metaheuristic algorithms are designed for serial systems. Meanwhile, existing algorithms still have a lot of room for improvement in convergence speed, robustness, and performance. To address these issues, this paper proposes an easily parallelizable metaheuristic optimization algorithm called team competition and cooperation optimization (TCCO) inspired by the process of human team cooperation and competition. The proposed algorithm attempts to mathematically model human team cooperation and competition to promote the optimization process and find an approximate solution as close as possible to the optimal solution under limited conditions. In order to evaluate the performance of the proposed algorithm, this paper compares the solution accuracy and convergence speed of the TCCO algorithm with the Grasshopper Optimization Algorithm (GOA), Seagull Optimization Algorithm (SOA), Whale Optimization Algorithm (WOA) and Sparrow Search Algorithm (SSA). Experiment results of 30 test functions commonly used in the optimization field indicate that, compared with these current advanced metaheuristic algorithms, TCCO has strong competitiveness in both solution accuracy and convergence speed. [ABSTRACT FROM AUTHOR]

Although primarily developed for aluminium alloys, friction stir welding (FSW) has nowadays emerged out as a 'green' effective joining process for other light weight metallic alloys owing to its solid-state nature. It has been experimented that quality of the weld mainly depends on selection of the optimal combination of various welding parameters, like tool rotational speed, welding speed, axial load, tool shoulder geometry, tool tilt angle, pin geometry etc. In this paper, seven multi-criteria decision making (MCDM) techniques, i.e. weighted aggregated sum product assessment, technique for order of preference by similarity to ideal solution, grey relational analysis, VIekriterijumsko KOmpromisno Rangiranje, multi-objective optimization on the basis of ratio analysis, complex proportional assessment and preference ranking organization method for enrichment evaluation are separately hybridized with particle swarm optimization (PSO) algorithm to identify the best parametric combinations of two FSW processes. The corresponding polynomial regression (PR) models are developed to be the inputs to these hybrid optimizers. They are later compared with the traditional weighted sum multi-objective optimization (PR-WSMO-PSO) approach, showing their superior performance. Among those MCDM techniques, preference ranking organization method for enrichment evaluation hybridized with PSO evolves out as the best method with respect to improvement in the corresponding performance metric. It is observed that during FSW of double phase α/β brass plates, an optimal combination of tool rotational speed = 1097 rpm, traverse speed = 90.8 mm/min and axial force = 2.5 kN, and during FSW of AZ31-AZ91 magnesium alloys, an optimal intermix of tool rotational speed = 986 rpm, welding speed = 50 mm/min and tool shoulder diameter = 21 mm would lead to simultaneous attainment of the most desired weld characteristics. Moreover, there is approximately 2.02–14.19% saving in computational time for all the MCDM-PR-PSO approaches as compared to PR-WSMO-PSO approach. [ABSTRACT FROM AUTHOR]

Early in 2019, COVID-19 was discovered for the first time in Wuhan, China, resulting in the deaths of a significant number of people in many different countries all over the world. Due to the rapid spread of this epidemic, scientists have strived to find quick and accurate diagnostic methods to lessen its global impact. Chest X-ray images were the best tool for rapidly and safely detecting COVID-19, but the manual examination of those images might result in faulty diagnoses. Therefore, the scientists have used deep learning (DL) models to remedy this shortcoming and classify the images infected with COVID-19 more accurately. Image segmentation is an essential step in improving the classification accuracy of DL models. Among existing image segmentation techniques, multilevel thresholding-based image segmentation techniques have gained significant interest due to their simplicity and high accuracy. However, the computational cost of those techniques exponentially increases as the number of threshold levels increases. Therefore, over the last few years, metaheuristic algorithms have collaborated with those techniques to significantly lessen the computational cost and accurately solve the image segmentation problem. However, those algorithms have some shortcomings, such as falling into local minima and slow convergence speed, which make them unable to find precise results. Therefore, in this paper, we present a new multilevel thresholding-based medical image segmentation technique based on the recently proposed spider wasp optimizer (SWO) to better segment the medical images, especially the chest X-ray images for detecting COVID-19 infection more accurately and rapidly. In addition, SWO is enhanced by two newly proposed mechanisms, namely global search improvement and local search improvement, to present a new better variant, namely improved SWO (ISWO). The former mechanism is responsible for improving the exploration operator by sharing the knowledge of the current individual and a newly generated individual, while the latter aims to improve the exploitation operator to improve the convergence speed. To evaluate the stability of ISWO and SWO, ten COVID-19 X-ray images with heterogeneous histograms under nine threshold levels (T) are used. Also, they are compared to eight rival optimizers according to several performance metrics to demonstrate their efficacy. According to the experimental results, ISWO is the best-performing algorithm, followed by SWO. Quantitatively, ISWO could achieve an average fitness value of 2796.837, while SWO could reach a value of 2796.33. [ABSTRACT FROM AUTHOR]

This paper introduces the Wolverine Optimization Algorithm (WoOA), a biomimetic method inspired by the foraging behaviors of wolverines in their natural habitats. WoOA innovatively integrates two primary strategies: scavenging and hunting, mirroring the wolverine's adeptness in locating carrion and pursuing live prey. The algorithm's uniqueness lies in its faithful simulation of these dual strategies, which are mathematically structured to optimize various types of problems effectively. The effectiveness of WoOA is rigorously evaluated using the Congress on Evolutionary Computation (CEC) 2017 test suite across dimensions of 10, 30, 50, and 100. The results showcase WoOA's robust performance in exploration, exploitation, and maintaining a balance between these phases throughout the search process. Compared to twelve established metaheuristic algorithms, WoOA consistently demonstrates a superior performance across diverse benchmark functions. Statistical analyses, including paired t-tests, Friedman test, and Wilcoxon rank-sum tests, validate WoOA's significant competitive edge over its counterparts. Additionally, WoOA's practical applicability is illustrated through its successful resolution of twenty-two constrained scenarios from the CEC 2011 suite and four complex engineering design challenges. These applications underscore WoOA's efficacy in tackling real-world optimization challenges, further highlighting its potential for widespread adoption in engineering and scientific domains. [ABSTRACT FROM AUTHOR]

Supply chain efficiency is a major challenge in today's business environment, where efficient resource allocation and coordination of activities are essential for competitive advantage. Traditional efficiency strategies often struggle for resources for the complex and dynamic network. In response, bio-inspired metaheuristic algorithms have emerged as powerful tools to solve these optimization problems. Referring to the random search nature of metaheuristic algorithms and emphasizing that no metaheuristic algorithm is the best optimizer for all optimization applications, the No Free Lunch (NFL) theorem encourages researchers to design newer algorithms to be able to provide more effective solutions to optimization problems. Motivated by the NFL theorem, the innovation and novelty of this paper is in designing a new meta-heuristic algorithm called Bobcat Optimization Algorithm (BOA) that imitates the natural behavior of bobcats in the wild. The basic inspiration of BOA is derived from the hunting strategy of bobcats during the attack towards the prey and the chase process between them. The theory of BOA is stated and then mathematically modeled in two phases (i) exploration based on the simulation of the bobcat's position change while moving towards the prey and (ii) exploitation based on simulating the bobcat's position change during the chase process to catch the prey. The performance of BOA is evaluated in optimization to handle the CEC 2017 test suite for problem dimensions equal to 10, 30, 50, and 100, as well as to address CEC 2020. The optimization results show that BOA has a high ability in exploration, exploitation, and balance them during the search process in order to achieve a suitable solution for optimization problems. The results obtained from BOA are compared with the performance of twelve well-known metaheuristic algorithms. The findings show that BOA has been successful in handling the CEC 2017 test suite in 89.65, 79.31, 93.10, and 89.65% of the functions for the problem dimension equal to 10, 30, 50, and 100, respectively. Also, the findings show that in order to handle the CEC 2020 test suite, BOA has been successful in 100% of the functions of this test suite. The statistical analysis confirms that BOA has a significant statistical superiority in the competition with the compared algorithms. Also, in order to analyze the efficiency of BOA in dealing with real world applications, twenty-two constrained optimization problems from CEC 2011 test suite and four engineering design problems have been selected. The findings show that BOA has been successful in 90.90% of CEC2011 test suite optimization problems and in 100% of engineering design problems. In addition, the efficiency of BOA to handle SCM applications has been challenged to solve ten case studies in the field of sustainable lot size optimization. The findings show that BOA has successfully provided superior performance in 100% of the case studies compared to competitor algorithms. [ABSTRACT FROM AUTHOR]

The Multi-Depot Open Vehicle Routing Problem (MDOVRP) is only one example of several optimization problems that are classified as NP-hard. Therefore, heuristic and metaheuristic approaches are helpful in obtaining a near-optimal solution. A hybrid HHO algorithm called HHO-PSO is proposed in this work to address the MDOVRP. The goal is to minimize costs for the routes of a fleet of vehicles that start moving from depots and fulfill customers' demands. To improve the exploration of the Harris Hawks Optimization (HHO) algorithm, the exploration method of Particle Swarm Optimization (PSO) which is more robust, is used in this paper. Experimental results proved that the proposed hybrid algorithm works better than the original PSO and HHO in discrete space in terms of balance, exploitation, and exploration to solve the MDOVRP. Moreover, the suggested algorithm is compared to five cutting-edge approaches on 24 MDOVRP instances with a broad number of customers. The computational findings reveal that the suggested approach outperformed the other comparable metaheuristic techniques in solving the MDOVRP. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a novel optimization algorithm called the OX optimizer, inspired by oxen animals, which are characterized by their great strength. The OX optimizer is designed to address the challenges posed by complex, high-dimensional optimization problems. The design of the OX optimizer embodies a fundamental symmetry between global and local search processes. This symmetry ensures a balanced and effective exploration of the solution space, highlighting the algorithm's innovative contribution to the field of optimization. The OX optimizer has been evaluated on CEC2022 and CEC2017 IEEE competition benchmark functions. The results demonstrate the OX optimizer's superior performance in terms of convergence speed and solution quality compared to existing state-of-the-art algorithms. The algorithm's robustness and adaptability to various problem landscapes highlight its potential as a powerful tool for solving diverse optimization tasks. Detailed analysis of convergence curves, search history distributions, and sensitivity heatmaps further support these findings. Furthermore, the OX optimizer has been applied to optimize support vector machines (SVMs), emphasizing parameter selection and feature optimization. We tested it on the NSL-KDD dataset to evaluate its efficacy in an intrusion detection system. The results demonstrate that the OX optimizer significantly enhances SVM performance, facilitating effective exploration of the parameter space. [ABSTRACT FROM AUTHOR]