Search

In this paper, we present the MU (multi-user) MIMO (multiple-input & multiple-output) system which is capable to serve the maximum number of users without additional bandwidth or power budget. MIMO systems maximize the channel capability as per the DPC scheme (Dirty Paper Coding). According to DPC, several antennas distributed over the base station are used for serving multiple users simultaneously. Nevertheless, DPC is a comprehensive search algorithm employing sequences of encoded user data for transmitting information to multiple users. Due to increased user density and subsequent transmit antennas in the MU-MIMO system, the exhaustive search (i.e. DPC) becomes cumbersome with the expanded search space domain. This search task would not be completed within a coherence time frame of the packet data communication system. The required objective is to consider this as a cost function for optimization to maximize the realizable sum-rate (throughput) of the MU-MIMO systems. This paper illuminates the joint antenna and user scheduling (JAAU) process with binary artificial bee colony (binary ABC) accomplishing nearly 98–99% of system throughput with the suggestively decreased figure in computation complexity and run time. Binary ABC is observed to have the global optimum value swiftly within the stipulated packet time frame of next-generation wireless data communications. Moreover, binary ABC achieves an appreciable system capacity for MU-MIMO systems as compared with DPC (which attains the maximum system throughput with the highest complexity). These findings are corroborated with exhaustive simulation results presented in this article. [ABSTRACT FROM AUTHOR]

Avionics systems are a crucial part of aircraft, and the heterogeneity of resources also leads to load differentials within these systems. Inefficient load balancing technology faces the dual challenge of over-utilization and under-utilization of resources, which results in the decline of service performance (in the case of over-utilization) or the waste of resources (in the case of under-utilization). However, it is necessary to control avionics systems via an efficient load balancing method under time constraints and resource states. Therefore, this paper proposes a load balancing method for avionics systems using both artificial bee colony and simulated annealing algorithms. First, the load balancing model of avionics systems is established; this model can reflect the demands of the tasks for the resources in detail. Then, the load balancing of avionics systems is realized by artificial bee colony and simulated annealing algorithms, the hybrid algorithm not only retains the advantages of simple and easy implementation of ABC algorithm, but also utilizes the probability jump of SA algorithm to jump out of the local extreme and achieve the effect of global optimization. Finally, compared with the existing algorithms, the experimental results show that the algorithm proposed in this paper produces a good and stable load balance performance. [ABSTRACT FROM AUTHOR]

Mobile object detection and tracking is an important application of wireless sensor network. However, longevity of such application is a challenge as sensor nodes in wireless sensor network operates on battery power. Therefore, there is the requirement for increasing the lifetime of this application by optimal usage of energy while tracking. Of course, while optimizing the energy usage, tracking accuracy should not be compromised. A number of research works are carried out in the past to enhance the wireless sensor network lifetime but no work resolved the problem completely. This paper studies the issue of data routing to sink in energy constrained wireless sensor network based application where tracking information collected from different sensor nodes are processed to determine the object's presence in the monitoring area. This paper proposes an energy efficient hybrid artificial bee colony-modified Dijkstra (HABC-MD) algorithm for cluster based network for optimum usage of energy during routing of packet. The objective of this algorithm is to save the energy of the network by optimizing the number of node-to-node transmission while tracking. It uses metaheuristic approach based artificial bee colony algorithm for selection of cluster head in each round and heuristic approach-based Dijkstra algorithm for selection of optimum route between source node and sink for packet transmission. Modified Dijkstra algorithm considers both distance between source and destination and energy of the source node for determination of optimum route to sink. The performance of HABC-MD is compared with existing hybrid LEACH-Dijkstra algorithm and is found that performance of our algorithm is better in comparison to later algorithm. [ABSTRACT FROM AUTHOR]

One of the limitations of the dung beetle optimization (DBO) is its susceptibility to local optima and its relatively low search accuracy. Several strategies have been utilized to improve the diversity, search precision, and outcomes of the DBO. However, the equilibrium between exploration and exploitation has not been achieved optimally. This paper presents a novel algorithm called the ODBO, which incorporates cat map and an opposition-based learning strategy, which is based on symmetry theory. In addition, in order to enhance the performance of the dung ball rolling phase, this paper combines the global search strategy of the osprey optimization algorithm with the position update strategy of the DBO. Additionally, we enhance the population's diversity during the foraging phase of the DBO by incorporating vertical and horizontal crossover of individuals. This introduction of asymmetry in the crossover operation increases the exploration capability of the algorithm, allowing it to effectively escape local optima and facilitate global search. [ABSTRACT FROM AUTHOR]

In 2022, the World Health Organization declared an outbreak of monkeypox, a viral zoonotic disease. With time, the number of infections with this disease began to increase in most countries. A human can contract monkeypox by direct contact with an infected human, or even by contact with animals. In this paper, a diagnostic model for early detection of monkeypox infection based on artificial intelligence methods is proposed. The proposed method is based on training the artificial neural network (ANN) with the adaptive artificial bee colony algorithm for the classification problem. In the study, the ABC algorithm was preferred instead of classical training algorithms for ANN because of its effectiveness in numerical optimization problem solutions. The ABC algorithm consists of food and limit parameters and three procedures: employed, onlooker and scout bee. In the algorithm standard, artificial onlooker bees are produced as much as the number of artificially employed bees and an equal number of limit values are assigned for all food sources. In the advanced adaptive design, different numbers of artificial onlooker bees are used in each cycle, and the limit numbers are updated. For effective exploitation, onlooker bees tend toward more successful solutions than the average fitness value of the solutions, and limit numbers are updated according to the fitness values of the solutions for efficient exploration. The performance of the proposed method was investigated on CEC 2019 test suites as examples of numerical optimization problems. Then, the system was trained and tested on a dataset representing the clinical symptoms of monkeypox infection. The dataset consists of 240 suspected cases, 120 of which are infected and 120 typical cases. The proposed model's results were compared with those of ten other machine learning models trained on the same dataset. The deep learning model achieved the best result with an accuracy of 75%. It was followed by the random forest model with an accuracy of 71.1%, while the proposed model came third with an accuracy of 71%. [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]

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]

Software cost estimation (SCE), estimating the cost and time required for software development, plays a highly significant role in managing software projects. A somewhat accurate SCE is necessary for a software project to be successful. It allows effective control of construction time and cost. In the past few decades, various models have been presented to evaluate software projects, including mathematical models and machine learning algorithms. In this paper, a new model based on the hybrid of the artificial fish swarm algorithm (AFSA) and the artificial bee colony (ABC) algorithm is presented for SCE. The initial population of AFSA, which includes the values of the effort factors, is generated using the ABC algorithm. ABC algorithm is used to solve the problems of the AFSA algorithm such as population diversity and getting stuck in a local optimum. ABC algorithm achieves the best solutions using observer and scout bees. The evaluation of the combined method has been implemented on eight different data sets and evaluated based on eight different criteria such as mean magnitude of relative error and PRED (0.25). The proposed method is more error-free than current SCE methods, according to the results. The error value of the proposed method is lower on NASA60, NASA63, and NASA93 datasets. [ABSTRACT FROM AUTHOR]

Over the last decade, dual active bridge (DAB) converters have become critical components in high-frequency power conversion systems. Recently, intensive efforts have been directed at optimizing DAB converter design and control. In particular, several strategies have been proposed to improve the performance of DAB control systems. For example, fractional-order (FO) control methods have proven potential in several applications since they offer improved controllability, flexibility, and robustness. However, the FO controller design process is critical for industrializing their use. Conventional FO control design methods use frequency domain-based design schemes, which result in complex and impractical designs. In addition, several nonlinear equations need to be solved to determine the optimum parameters. Currently, metaheuristic algorithms are used to design FO controllers due to their effectiveness in improving system performance and their ability to simultaneously tune possible design parameters. Moreover, metaheuristic algorithms do not require precise and detailed knowledge of the controlled system model. In this paper, a hybrid algorithm based on the chaotic artificial ecosystem-based optimization (AEO) and manta-ray foraging optimization (MRFO) algorithms is proposed with the aim of combining the best features of each. Unlike the conventional MRFO method, the newly proposed hybrid AEO-CMRFO algorithm enables the use of chaotic maps and weighting factors. Moreover, the AEO and CMRFO hybridization process enables better convergence performance and the avoidance of local optima. Therefore, superior FO controller performance was achieved compared to traditional control design methods and other studied metaheuristic algorithms. An exhaustive study is provided, and the proposed control method was compared with traditional control methods to verify its advantages and superiority. [ABSTRACT FROM AUTHOR]

This paper proposes a novel hyper-heuristic algorithm termed evolutionary multi-mode slime mold optimization (EMSMO) for addressing continuous optimization problems. The architecture of a typical hyper-heuristic algorithm comprises two main components: the high-level component and the low-level component. The low-level component contains a set of low-level heuristics (LLHs) and intrinsic problem attributes, while the high-level component manipulates the LLHs to construct the sequence of heuristics. Inspired by the foraging behaviors of slime mold, we designed four easy-implemented search strategies including the search for food, approach food, wrap food, and re-initialization as the LLHs for the low-level component. In the high-level component, we adopt an improvement-based probabilistic selection function that contains two metrics: (1) the probability of improvement and (2) the normalized improvement. The selection function cooperates with the roulette wheel strategy to construct the optimization sequence. To evaluate the performance of our proposal, we implement comprehensive numerical experiments on CEC2013 benchmark functions and three engineering optimization problems. Six classic or advanced evolutionary algorithms and three hyper-heuristic algorithms are applied as competitor algorithms to evaluate the competitiveness of EMSMO. Experimental and statistical results show that EMSMO has broad prospects for solving continuous optimization problems. [ABSTRACT FROM AUTHOR]

The paper proposes a calculation procedure for square cascades based on cross-sections of partial fluxes. The bees algorithm is used to achieve a constant cross-section for the stage feed flux. The optimization goal is determined as a residual sum of squares of the given stage feed flows calculated through cross-sections of partial fluxes. The paper describes the numerical experiment with germanium tetrafluoride used for multicomponent gas separation of germanium isotopes. It is shown that the proposed calculation procedure can be used to obtain the high concentration of 70Ge and 76Ge isotopes in the square cascade of the stage feed flux. [ABSTRACT FROM AUTHOR]

In order to solve the problems of insufficient optimization accuracy, slow convergence speed and easy to fall into local optimum in the whale optimization algorithm, this paper proposes a whale optimization algorithm with improved neighborhood search strategy. First, the algorithm generates a more evenly distributed and higher-quality initial population through an initialization strategy based on the opposite-based learning of pinhole imaging to expand the early search space of the algorithm. Secondly, it adopts improved neighborhood search strategy based on similarity and uses Mahalanobis distance and the law of universal gravitation to calculate and rank the similarity of solutions. At the same time, the algorithm counts the times of convergence oscillation. According to the algorithm iteration process, it selects the corresponding similarity ranking solution as the object to update the position and performs the second position update for the solution with most times of oscillation, so as to implement the space exploration of the target population to speed up the convergence of the algorithm and enhance the ability to jump out of the local optimum. Finally, an adaptive step size adjustment strategy is introduced, and the population convergence is adjusted using adaptive step size parameters according to the algorithm optimization process to improve the algorithm's global search performance and avoid premature convergence of the algorithm. The improved algorithm proposed in this paper is analyzed and compared with the sine–cosine optimization algorithm, artificial bee colony algorithm and three improved whale algorithms on a set of 20 test functions in low-dimensional and high-dimensional, respectively, and perform ANOVA and T-test on the simulation results. The results show that the improved algorithm proposed in this paper effectively improves the convergence accuracy and convergence speed. In addition, the improved optimization algorithm proposed in this paper is applied to the engineering optimization design. The solutions show that the improved algorithm can obtain the optimal value with higher accuracy and more stability than other algorithms, and can effectively solve the engineering design problem. [ABSTRACT FROM AUTHOR]

As the terminal of the power system, the distribution network is the main area where failures occur. In addition, with the integration of distributed generation, the traditional distribution network becomes more complex, rendering the conventional fault location algorithms based on a single power supply obsolete. Therefore, it is necessary to seek a new algorithm to locate the fault of the distributed power distribution network. In existing fault localization algorithms for distribution networks, since there are only two states of line faults, which can usually be represented by 0 and 1, most algorithms use discrete algorithms with this characteristic for iterative optimization. Therefore, this paper combines the advantages of the particle swarm algorithm and genetic algorithm and uses continuous real numbers for iteration to construct a successive particle swarm genetic algorithm (SPSO-GA) different from previous algorithms. The accuracy, speed, and fault tolerance of SPSO-GA, discrete particle swarm Genetic algorithm, and artificial fish swarm algorithm are compared in an IEEE33-node distribution network with the distributed power supply. The simulation results show that the SPSO-GA algorithm has high optimization accuracy and stability for single, double, or triple faults. Furthermore, SPSO-GA has a rapid convergence velocity, requires fewer particles, and can locate the fault segment accurately for the distribution network containing distorted information. [ABSTRACT FROM AUTHOR]

Space debris poses a potentially catastrophic risk to large-scale LEO constellations due to its substantial cascading effects, thereby rendering efficient and timely debris removal a critical research area in contemporary aerospace engineering. This paper delves into the application of the three-dimensional traveling salesman problem with orbital transfer constraints for space debris removal task planning. To overcome the challenges of sparse optimal solutions and vulnerability to local optima in addressing large-scale space debris removal task planning problems, we introduce a new Hierarchical Exploration Artificial Bee Colony (HEABC) optimization algorithm. Initially, we present an innovative two-step search strategy that employs a short-and-long term hybrid approach to optimize search time utilization and enhance solution quality, thereby addressing the sparsity of optimal solutions in the HEABC algorithm. Subsequently, to mitigate the issue of converging to local optima in high-dimensional encoding-based search problems, we devise a dual population update strategy aimed at preserving the innate evolutionary driving force of the search population. This strategy ensures the continuous updating of the population, even in the absence of intrinsic driving forces. Ultimately, experimental results substantiate that our proposed HEABC algorithm attains superior task planning sequences in a reduced time span and exhibits heightened adaptability in comparison to various traditional search algorithms. This is corroborated by numerical experiments conducted on one publicly available and one STK-generated space debris datasets. [ABSTRACT FROM AUTHOR]

In the era of digital intelligence, this paper studies the task allocation algorithm of distributed large data stream group computing, and reasonably allocates the task of group computing to meet the needs of massive computing and analysis of distributed large data stream. According to the idea of swarm intelligence perception and crowdsourcing platform, the task allocation model of distributed large data stream group computing is constructed to realize the task allocation of group computing. A distributed large data stream group computing task model and a user model are constructed, user attributes are initialized by using the accuracy of the answers submitted by users, the possibility that users can participate in the group computing task is predicted by a logistic regression algorithm, so that user candidate sequences participating in the computing task can be obtained, and the accuracy of the user's real topics and corresponding topics can be grasped by capturing the candidate users' real topics and evaluating the accuracy algorithm. Select the users who meet the subject area, update the candidate user sequence, and filter the users again on the basis of fully considering the factors such as information gain, user integrity and cost, so as to get the final user sequence and complete the task allocation of group computing. Experiments show that this method can solve the problem of distributed large data flow group computing task allocation, achieve high accuracy, reduce the cost, and effectively improve the information gain. [ABSTRACT FROM AUTHOR]

Feature selection aims to select crucial features to improve classification accuracy in machine learning and data mining. In this paper, a new binary grasshopper optimization algorithm using time-varying Gaussian transfer functions (BGOA-TVG) is proposed for feature selection. Compared with the traditional S-shaped and V-shaped transfer functions, the proposed Gaussian time-varying transfer functions have the characteristics of a fast convergence speed and a strong global search capability to convert a continuous search space to a binary one. The BGOA-TVG is tested and compared to S-shaped and V-shaped binary grasshopper optimization algorithms and five state-of-the-art swarm intelligence algorithms for feature selection. The experimental results show that the BGOA-TVG has better performance in UCI, DEAP, and EPILEPSY datasets for feature selection. [ABSTRACT FROM AUTHOR]

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

As a new abstract computational model in evolutionary transfer optimization (ETO), single-objective to multi-objective optimization (SMO) is conducted at the macroscopic level rather than the intermediate level for specific algorithms or the microscopic level for specific operators; this method aims to develop systems with a profound grasp of evolutionary dynamic and learning mechanism similar to human intelligence via a "decomposition" style (in the abstract of the well-known "Transformer" article "Attention is All You Need", they use "attention" instead). To the best of our knowledge, it is the first work of SMO for discrete cases because we extend our conference paper and inherit its originality status. In this paper, by implementing the abstract SMO in specialized memetic algorithms, key knowledge from single-objective problems/tasks to the multi-objective core problem/task can be transferred or "gathered" for permutation flow shop scheduling problems, which will reduce the notorious complexity in combinatorial spaces for multi-objective settings in a straight method; this is because single-objective tasks are easier to complete than their multi-objective versions. Extensive experimental studies and theoretical results on benchmarks (1) emphasize our decomposition root in mathematical programming, such as Lagrangian relaxation and column generation; (2) provide two "where to go" strategies for both SMO and ETO; and (3) contribute to the mission of building safe and beneficial artificial general intelligence for manufacturing via evolutionary computation. [ABSTRACT FROM AUTHOR]

The artificial bee colony algorithm (ABC) is a promising metaheuristic algorithm for continuous optimization problems, but it performs poorly in solving discrete problems. To address this issue, this paper proposes a hybrid discrete artificial bee colony (HDABC) algorithm based on label similarity for the point-feature label placement (PFLP) problem. Firstly, to better adapt to PFLP, we have modified the update mechanism for employed bees and onlooker bees. Employed bees learn the label position of the better individuals, while onlooker bees perform dynamic probability searches using two neighborhood operators. Additionally, the onlooker bees' selection method selects the most promising solutions based on label similarity, which improves the algorithm's search capabilities. Finally, the Metropolis acceptance strategy is replaced by the original greedy acceptance strategy to avoid the premature convergence problem. Systematic experiments are conducted to verify the effectiveness of the neighborhood solution generation method, the selection operation based on label similarity, and the Metropolis acceptance strategy in this paper. In addition, experimental comparisons were made at different instances and label densities. The experimental results show that the algorithm proposed in this paper is better or more competitive with the compared algorithm. [ABSTRACT FROM AUTHOR]

This paper proposes binary versions of artificial rabbits optimization (ARO) for feature selection (FS) with medical diagnosis data. ARO is a recent swarm-based optimization algorithm that mimics rabbits' natural survival tactics and eating habits. It was modeled in an optimization context to tackle optimization problems of continuous search spaces. In this paper, ARO is improved to deal with the binary domain of FS. The improvements include three additions: First, different alternatives of transfer functions were used to convert ARO from continuous to binary; second, the global-best concept was added to the binary ARO to improve the exploitation capability of the proposed algorithm; and finally, Lévy flight and opposition-based learning strategies were injected into the proposed algorithm to enhance its diversity and thus improve the balance between global exploration and local exploitation during all stages of the search process. Six binary variants of ARO were designed across an extensive set of experiments to study the impact of using the proposed amendments on the performance of the proposed ARO algorithm. These variants are: binary ARO with S-shaped transfer function (BAROS), binary ARO with V-shaped transfer function (BAROV), BAROS with the global-best concept (BGAROS), BGAROV with the global-best concept (BGAROV), BGAROS with Lévy flight and opposition-based learning strategies (BGAROSLO), and BGAROV with Lévy flight and opposition-based learning strategies (BGAROVLO). The proposed binary ARO versions were evaluated using 23 medical FS datasets. In addition, the proposed algorithm was applied to detect coronavirus disease using a real COVID-19 dataset. Five performance measures were used: classification accuracy, sensitivity, specificity, fitness value, and the number of selected features. In a nutshell, the proposed binary ARO versions were able to achieve success rates for these performance metrics as follows: 66.7%, 50%, 33.3%, 66.7%, and 83.3%, respectively. In conclusion, the success of the proposed ARO versions was realized due to the suitable design of the parameters of the proposed ARO version, such as transfer functions, global-best concept, Lévy flight, and opposition-based learning strategies. A comprehensive comparative evaluation was studied against ten well-established methods using the same datasets with a high preference for the proposed ARO versions, especially BGAROSLO which can achieve the best accuracy for the majority of the FS datasets. This is proven using Friedman's statistical test ad-hocked by Holm's test. [ABSTRACT FROM AUTHOR]

In this paper, a novel Artificial Bee Colony algorithm is proposed to solve a variant of the Job Shop Scheduling Problem where only an interval of possible processing times is known for each operation. The solving method incorporates a diversification strategy based on the seasonal behaviour of bees. That is, the bees tend to explore more at the beginning of the search (spring) and be more conservative towards the end (summer to winter). This new strategy helps the algorithm avoid premature convergence, which appeared to be an issue in previous papers tackling the same problem. A thorough parametric analysis is conducted and a comparison of different seasonal models is performed on a set of benchmark instances from the literature. The results illustrate the benefit of using the new strategy, improving the performance of previous ABC-based methods for the same problem. An additional study is conducted to assess the robustness of the solutions obtained under different ranking operators, together with a sensitivity analysis to compare the effect that different levels of uncertainty have on the solutions' robustness. [ABSTRACT FROM AUTHOR]

Image segmentation is a significant step in image analysis and computer vision. Many entropy-based approaches have been presented on this topic. Among them, Tsallis entropy isone of the best-performing methods. In this paper, the surface defect images of galvanised steel sheets were studied. Atwo-dimensional asymmetric Tsallis cross-entropy image segmentation algorithm based on chaotic bee colony algorithm optimisation was used to investigate the segmentation of surface defects under complex texture backgrounds. On the basis of Tsallis entropy threshold segmentation, a more concise expression form was used to define the asymmetric Tsallis cross-entropy in order to reduce the calculation complexity of the algorithm. The chaotic algorithm was combined with the artificial bee colony (ABC) algorithm to construct the chaotic bee colony algorithm, so that the optimal threshold of Tsallis entropy could be quickly identified. The experimental results showed that compared with the maximum Shannon entropy algorithm, the calculation time of this algorithm decreased by about 58% and the threshold value increased by about (26%, 54%). Compared with the two-dimensional Tsallis cross-entropy algorithm, the calculation time of this algorithm decreased by about 35% and about 20% was improved in the g-axis direction only. Compared with the two-dimensional asymmetric Tsallis cross-entropy algorithm, the calculation time of this algorithm decreased by about 30% and the threshold values of the two algorithms were almost the same. The algorithm proposed in this paper can rapidly and effectively segment defect targets, making it a more suitable method for detecting surface defects in factories with a rapid production pace. [ABSTRACT FROM AUTHOR]

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

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

This paper presents a comprehensive investigation of the optimal design of an interior permanent magnet synchronous motor (IPMSM) for electric vehicles (EVs), utilizing the hybrid artificial bee colony algorithm–support vector machine (HAS) algorithm. The performance of the drive motor is a crucial determinant of the overall vehicle performance, particularly in EVs that rely solely on a motor for propulsion. In this context, interior permanent magnet synchronous motors (IPMSMs) offer a compelling choice due to their high torque density, wide speed range, superior efficiency, and robustness. However, accurate analysis of the nonlinear characteristics of IPMSMs necessitates finite element analysis, which can be time-consuming. Therefore, research into methods for deriving an optimal model with minimal computation is of significant importance. The HAS is a powerful multimodal optimization technique that is capable of exploring several optimal solutions. It enhances the navigation capability by combining the artificial bee colony algorithm (ABC) with the kernel support vector machine (KSVM). Specifically, the algorithm improves the search ability by optimizing the movement of bees in each region generated by the KSVM. Furthermore, hybridization with the Nelder–Mead method ensures accurate and quick convergence at pointers discovered in the ABC. To demonstrate the effectiveness of the proposed algorithm, this study compared its performance with a conventional algorithm in two mathematical test functions, verifying its remarkable performance. Finally, the HAS algorithm was applied to the optimal design of the IPMSM for EVs. Overall, this paper provides a thorough investigation of the application of the HAS algorithm to the design of IPMSMs for electric vehicles, and its application is expected to benefit from the combination of machine-learning techniques with various other optimization algorithms. [ABSTRACT FROM AUTHOR]

The shuffled frog leaping algorithm (SFLA) is a promising metaheuristic bionics algorithm, which has been designed by the shuffled complex evolution and the particle swarm optimization (PSO) framework. However, it is easily trapped into local optimum and has the low optimization accuracy when it is used to optimize complex engineering problems. To overcome the shortcomings, a novel modified shuffled frog leaping algorithm (MSFLA) with inertia weight is proposed in this paper. To extend the scope of the direction and length of the updated worst frog (vector) of the original SFLA, the inertia weight α was introduced and its meaning and range of the new parameters are fully explained. Then the convergence of the MSFLA is deeply analyzed and proved theoretically by a new dynamic equation formed by Z-transform. Finally, we have compared the solution of the 7 benchmark functions with the original SFLA, other improved SFLAs, genetic algorithm, PSO, artificial bee colony algorithm, and the grasshopper optimization algorithm with invasive weed optimization. The testing results showed that the modified algorithms can effectively improve the solution accuracy and convergence property, and exhibited an excellent ability of global optimization in high-dimensional space and complex function problems. [ABSTRACT FROM AUTHOR]

The shuffled frog leaping algorithm (SFLA) is a promising metaheuristic bionics algorithm, which has been designed by the shuffled complex evolution and the particle swarm optimization (PSO) framework. However, it is easily trapped into local optimum and has the low optimization accuracy when it is used to optimize complex engineering problems. To overcome the shortcomings, a novel modified shuffled frog leaping algorithm (MSFLA) with inertia weight is proposed in this paper. To extend the scope of the direction and length of the updated worst frog (vector) of the original SFLA, the inertia weight α was introduced and its meaning and range of the new parameters are fully explained. Then the convergence of the MSFLA is deeply analyzed and proved theoretically by a new dynamic equation formed by Z-transform. Finally, we have compared the solution of the 7 benchmark functions with the original SFLA, other improved SFLAs, genetic algorithm, PSO, artificial bee colony algorithm, and the grasshopper optimization algorithm with invasive weed optimization. The testing results showed that the modified algorithms can effectively improve the solution accuracy and convergence property, and exhibited an excellent ability of global optimization in high-dimensional space and complex function problems. [ABSTRACT FROM AUTHOR]

This paper proposes a virtual inertia control strategy for wind farms based on auto disturbance rejection controller with artificial bee colony (ABC) algorithm. First, based on the system frequency dynamic response equation, an active disturbance rejection controller is designed according to the non‐linear feedback control law and the extended state observer to improve the anti‐interference capability of the virtual inertia control system. Second, in order to solve the problem of difficulty in tuning the parameters of the active disturbance rejection control (ADRC), an ABC algorithm based on nectar collection behaviour was proposed to iteratively optimize the parameters of ADRC. Finally, the proposed control method is effectively verified based on Matlab/Simulink. The simulation results show that compared with traditional algorithms, the proposed ABC‐ADRC can effectively control the rotor speed to adjust the frequency of the power grid system, achieve power sharing and adaptive noise elimination, reduce the complexity of parameter settings, not only improve anti‐interference ability, but also enhance the robustness of the system. [ABSTRACT FROM AUTHOR]

The new crown pneumonia epidemic is raging, in the context of global integration, the scope of the impact of this sudden event spread around the world, the stock market has not been spared, the financial risk has increased dramatically compared with the past, the emergence of the epidemic has led to the spread of investor panic, March 2020, the U.S. S&P 500 index appeared in the four plunge, and led to the market trading meltdown, the world's financial markets have had an extremely serious impact. The study of the impact of Xin Guan Pneumonia on the company's stock returns is not only conducive to enriching the theoretical study of public health emergencies, but also conducive to improving the coping strategy, stabilizing the general economic market, and enhancing the public's awareness of risk response. This paper compares the effect of the four intelligent algorithms of chaotic particle swarm algorithm, chaotic bee colony algorithm, chaotic fruit fly algorithm and chaotic ant colony algorithm combined with neural network on the prediction of the stock price trend of Yunnan national culture, and the study shows that the speed of convergence of the chaotic particle swarm optimization neural network and the speed of descent is better than that of the two models of chaotic fruit fly and chaotic bee colony, and the coefficients of decision of the chaotic particle swarm optimization neural network are higher than that of the other three models, and the errors are lower than the other three models. Indexes are lower than the other three models and have high accuracy in stock prediction of Yunnan ethnic culture, this finding emphasizes the potential of PSO-BP model to provide robust stock market prediction, which is important for both investors and policy makers in dealing with volatile market conditions. [ABSTRACT FROM AUTHOR]

This paper introduces Right Truncated Inverse Generalized Rayleigh distribution (RTIGRD) with two parameters and with some of its properties as; (Survival-Function, Probability Density Function, Hazard-Function, Cumulative Distribution Function, R-Th Moment, Mean, Variance, Median, Moment Generating Function, and Mode. In addition, we propose a new hybrid algorithm (Artificial Bee Colony Algorithm with Firefly Algorithm (ABC_FA)) to estimate Survival functions based on the parameters (,) of (RTIGRD). Simulation is utilized to compare the proposed algorithm with traditional methods (Maximum Likelihood Estimator and moment method) and standard algorithms (Artificial Bee Colony Algorithm and Firefly Algorithm). The results show proposed approach (ABC_FA) provides a 100% accurate estimate of the survival function for the cases selected in this research, as it has a less mean square error than other estimation methods. [ABSTRACT FROM AUTHOR]

In rough set theory, a construct is an attribute subset with the same ability to discern objects belonging to different classes as the whole set of attributes, while maintaining the similarity between objects belonging to the same class. Although algorithms for reducts computation can be adapted to compute constructs, practical problems exist where these algorithms cannot compute all constructs within a reasonable time frame. Therefore, this paper introduces an algorithm for computing all constructs of a decision system. The results of experiments with various decision systems (both artificial and real-world) suggest that our algorithm is, in most cases, faster than the state-of-the-art algorithms when the simplified binary discernibility–similarity matrix has a density of less than 0.29. [ABSTRACT FROM AUTHOR]

The purpose of clustering is to identify distributions and patterns within unlabelled datasets. Since the proposal of the original synchronization clustering (SynC) algorithm in 2010, synchronization clustering has become a significant research direction. This paper proposes a shrinking synchronization clustering (SSynC) algorithm utilizing a linear weighted Vicsek model. SSynC algorithm is developed from SynC algorithm and a more effective synchronization clustering (ESynC) algorithm. Through analysis and comparison, we find that SSynC algorithm demonstrates superior synchronization effect compared to SynC algorithm, which is based on an extensive Kuramoto model. Additionally, it exhibits similar effect to ESynC algorithm, based on a linear version of Vicsek model. In the simulations, a comparison is conducted between several synchronization clustering algorithms and classical clustering algorithms. Through experiments using some artificial datasets, eight real datasets and three picture datasets, we observe that compared to SynC algorithm, SSynC algorithm not only achieves a better local synchronization effect but also requires fewer iterations and incurs lower time costs. Furthermore, when compared to ESynC algorithm, SSynC algorithm obtains reduced time costs while achieving nearly the same local synchronization effect and the same number of iterations. Extensive comparison experiments with some class clustering algorithms demonstrate the effectiveness of SSynC algorithm. [ABSTRACT FROM AUTHOR]

Aiming at the problems of low chip area utilization, long design cycle and high manufacturing cost in BAW filter layout, this paper proposed a BAW filter automatic layout method based on improved artificial swarm algorithm. Firstly, it constructed the evaluation model of automatic layout, and simplified the difficulty of automatic layout by combining the sequen- tial layout of circular container with the clustering layout based on rotation angle coding. At the same time, it used an im- proved artificial swarm algorithm to search for the optimal sequential layout and aggregation layout of BAW filters. Finally, combining the design case of multiple BAW filters to verify the validity of the automatic layout algorithm, the effective area of the layout was about 17% higher, the overall layout was square, and the automatic layout of the filter took only 30 min to com- plete. The results show that the algorithm can improve chip area utilization and reduce the design cycle of BAW filter. [ABSTRACT FROM AUTHOR]

This paper addresses a variant of the Job Shop Scheduling Problem with makespan minimisation where uncertainty in task durations is taken into account and modelled with intervals. A novel Artificial Bee Colony algorithm is proposed where the classical layout is simplified, increasing the algorithm's speed and reducing the number of parameters to set up. We also take into account the fundamental principles of exploration around a local solution and attraction to a global solution to improve diversity in the hive. The increase on speed and diversity allows to include a Local Search phase to better exploit promising areas of the search space. A parametric analysis is conducted and the contribution of the new strategies is analysed. The results of the new approach are competitive with those obtained with previous methods in the literature, but taking less runtime. The addition of Local Search improves the results even further, outperforming the best-known ones from the literature. An additional sensitivity study is conducted to assess the advantages of considering uncertainty and how increasing it affects the solution's robustness. [ABSTRACT FROM AUTHOR]

In this paper we study a complete quadrangle in the Euclidean plane that has a rectangular hyperbola circumscribed to it. Hereby, the approach is based on the rectangular coordinates and we prove the following main result: Let ABCD be a complete quadrangle and l a , l b , l c , l d mutually parallel lines through the circumcenters of BCD, ACD, ABD, ABC, respectively. Orthopoles of the lines l a , l b , l c , l d with respect to the triangles BCD, ACD, ABD, ABC lie on a line which passes through the center of the rectangular hyperbola H circumscribed to ABCD, and it is antiparallel to the given lines with respect to the axes of the hyperbola H . [ABSTRACT FROM AUTHOR]

In order to improve the shortcomings of the aquila optimizer (AO) in solving complex optimization problems, such as easily falling into local optimality, this paper proposed a multi-strategy improved aquila optimizer (MIAO ) . Firstly, this paper proposed the mirror simplex method strategy to expand the search range of aquila optimizer, enhancing the population diversity and the ability to escape from shackles. Secondly, the X3 phase of the aquila optimizer combined the social free foraging strategy to escape from the bondage of global mean and improved the diversity of individual aquatic eagles in the late iteration. At the same time, the X4 phase incorporated the ladder stepping strategy to ensure that the current dominant individuals accelerated the trend toward the global optimum and increased the convergence speed. Finally, it enhanced the optimization ability of the algorithm by improving the original development mechanism. Experimental results and Wilcoxon signed rank sum test results of 10 commonly used benchmark functions and some of the CEC2017 functions show that the improved algorithm has better performance in terms of accuracy, convergence and stability. In addition, through testing and analyzing a mechanical optimization design experiment to further verify the superiority and practicability of the improved algorithm. [ABSTRACT FROM AUTHOR]

It also reports simulation-based comparison study with other optimization algorithms such as particle swarm optimization, differential evolution, and whale optimization algorithm. The experiment results of the algorithm in the form of precision are compared with conventional algorithms to draw conclusions about the efficacy of the proposed algorithm. In the paper titled "Analysis of Amplitude Scintillation and Positioning Error of IRNSS/GPS/SBAS Receiver for Heavy Rainy Days", the authors examine the characteristics of amplitude scintillation on an IRNSS/SBAS/GPS receiver with all seven satellites of the Indian Regional Navigation Satellite System (IRNSS) at the low-altitude station IITRAM, Ahmedabad, on days with heavy rain. In the paper "Model Order Reduction Based Power System Stabilizer Design Using Improved Whale Optimization Algorithm", the authors present an optimization technique I viz i . improved whale optimization for designing a power system stabilizer using model-order reduction for a modified single machine infinite bus system. [Extracted from the article]

Aiming at the hybrid flow shop scheduling problem with the objective of minimizing the makespan, this paper proposed an opposite artificial bee colony algorithm with opposition-based learning to solve the problem. First, according to the characteristics of hybrid flow shop scheduling problem, this paper established the corresponding mathematical model and simulation optimization model. Secondly, in order to avoid the problem of local optima in the process of optimization, this paper added the opposition-based learning strategy in the three stages of population initialization, employment bee and observation bee, used the reverse order strategy between two points and the element exchange strategy to speed up the optimization, then used the elitist preservation strategy to retain the optimal solution. Finally, the simulation experiment selected two examples and 21 benchmark problems of different scales, the results of simulation experiment verified that proposed algorithm is effective in solving such problems by comparison with the experimental of related algorithms. [ABSTRACT FROM AUTHOR]

In this paper, a new hybrid method is developed that incorporates the rationale of truncated power basis (TPB) and artificial bee colony-back propagation neural networks (ABC-BPNN) into an adaptive fuzzy semi-parametric regression model yielding more accurate results and better generalization ability. The proposed adaptive fuzzy semi-parametric regression model comprises sub-model formulation and approximates the observed fuzzy outputs from the outside employing neural networks computation, such that the proposed adaptive fuzzy regression model better explains the inherent dependence and vagueness that exist in a given dataset. Using the cross-validation criterion and absolute deviation-based distance measures for LR-type fuzzy numbers, a target function optimization problem of constructing the adaptive fuzzy semi-parametric regression is performed by solving the smooth function, bandwidth of kernel function, and regression coefficients. This strategy significantly increases the goodness of fit for the proposed algorithm and offers a dependence framework among magnitude and uncertainty for the fuzzy regression model. We also use three formula measures to evaluate the fit quality of the regression results within each membership function as well as in the center or spread tendency property, respectively. The proposed algorithm is numerically evaluated on three experimental examples including a simulation study and two practical cases to prove its practicality and efficiency. Comparative analyses of our proposed method are provided to support its cogency, and the results show that our proposed model is more effective and stable than some other existing fuzzy regressions. [ABSTRACT FROM AUTHOR]

The approximate degree reduction of ball NURBS curves is a knotty technique in geometric modeling. As is known to all, the degree reduction of ball NURBS ones is mathematically an optimization problem that can be solved efficiently by swarm intelligence algorithm. In this paper, an improved artificial rabbits optimization (ARO) is used to accomplish the optimal multi-degree reduction of the ball curves. Firstly, by incorporating mutation strategy, adaptive group strategy and Elite-feedback strategy to the ARO, the improved ARO named IARO is developed to increase the population diversity and enhance its capability of jumping out of the local minima. Secondly, the superiority of IARO is comprehensively verified by comparing with the original ARO and numerous celebrated and newly developed algorithms on the IEEE Congress on Evolutionary Computation (CEC, for short) 2017 benchmark functions. Meanwhile, the statistical testing of IARO has been conducted to validate its significance. Finally, by minimizing the distance between the original curve and the approximate curve, the optimization models of multi-degree reduction for ball NURBS curves are established. The IARO is utilized to solve the optimization models, and the optimal approximate ball NURBS curves are obtained. Some representative numerical examples illustrate the ability of the proposed IARO in effectively solving the multi-degree reduction problem of ball NURBS curves in terms of precision, robustness, and convergence characteristics. [ABSTRACT FROM AUTHOR]

Automatic tooth arrangement and path planning play an essential role in computer-aided orthodontic treatment. However, state-of-the-art methods have some shortcomings: low efficiency, excessive cost of displacements or collisions and insufficient accuracy. To address these issues, this paper proposes an innovative orthodontic path planning method based on the improved gray wolf optimization algorithm, which is called OPP-IGWO. First, the tooth model is preprocessed to obtain initial pose in which each tooth is segmented and built up with an oriented bounding box. Next, the target pose of each tooth is determined through the ideal dental arch curve and the optimal jaw principle. Finally, the path from the initial pose to the target pose of each tooth is planned based on IGWO, which is improved mainly from three aspects: (1) The greedy idea is adopted to initialize the gray wolf population based on dental interpolation. (2) The linear convergence factor in the traditional gray wolf optimization algorithm (GWO) is replaced with a nonlinear convergence factor. (3) We propose a position update strategy based on a dynamic weighting approach, which introduces a learning rate for each gray wolf. The experimental results show that our OPP-IGWO method outperforms the state-of-the-art methods. Compared with improved genetic algorithm, multiparticle improved swarm optimization, normal simplified mean particle swarm optimization algorithm, improved artificial bee colony algorithm, chaotic gray wolf optimization algorithm, hybrid gray wolf optimization algorithm and differential evolution algorithm, random walk gray wolf Optimization algorithm and reinforcement learning gray wolf optimization algorithm, the OPP-IGWO has an improvement on performance by 15.46%, 2.24%, 7.18%, 15.99%, 7.67%, 1.01%, 1.42%, 10.23%, respectively. [ABSTRACT FROM AUTHOR]

Forever and a day, breast cancer has caused significant negative impacts on the quality of lives of number of women, more often than not turning into a fatal disease. The growth in the number of such cases has constantly been a major concern for the community as well as medical experts. To prevent irreversible damages caused by the disease, early identification of breast cancer is essential. Various researches and techniques have been devised in the past as an attempt to achieve this task with appreciable accuracy. As an advancement to these pre-existing algorithms and methods, we have devised a model by exploiting the techniques of nature-inspired metaheuristics in order to efficiently detect breast cancer at an early stage while maintaining acceptable levels of accuracy. In this paper, we propose a hybrid model, namely "hybrid artificial bee colony and black hole with genetic operators (GBHABC)", for the early detection of breast cancer. In the proposed model, we employed a support vector machine (SVM) ensemble technique, optimized using the proposed GBHABC model. This model combines the techniques of two major algorithms, namely artificial bee colony (ABC) and black hole (BH), guided through crossover and mutation genetic operators. Datasets from the well-known UCI breast cancer repository have been used to train the models and evaluate test result. For a fair and accurate evaluation of the model, a number of metrics have been examined including accuracy, sensitivity, specificity, F1-score and precision. An impeccable accuracy of 99.42% was obtained on the UCI dataset, clearly outperforming any literature in the same field. [ABSTRACT FROM AUTHOR]

Solving the energy management (EM) problem in microgrids with the incorporation of demand response programs helps in achieving technical and economic advantages and enhancing the load curve characteristics. The EM problem, with its large number of constraints, is considered as a nonlinear optimization problem. Artificial rabbits optimization has an exceptional performance, however there is no single algorithm can solve all engineering problem. So, this paper proposes a modified version of artificial rabbits optimization algorithm, called QARO, by quantum mechanics based on Monte Carlo method to determine the optimal scheduling for MG resources effectively. The main objective is minimization of the daily operating cost with the maximization of MG operator (MGO) benefit. The operating cost includes the conventional diesel generator operating cost and the cost of power transactions with the grid. The performance of the proposed algorithm is assessed using different standard benchmark test functions. A ranking order for the test function based on the average value and Tied rank technique, Wilcoxon's rank test based on median value, and Anova Kruskal–Wallis test showed that QARO achieved best results on the most functions and outperforms all other compared technique. The obtained results of the proposed QARO are compared with those obtained by employing well-known and newly-developed algorithms. Moreover, the proposed QARO is used to solve two case studies of day-ahead EM problem in MG, then the obtained results are also compared with other well-known optimization techniques, the results demonstrate the effectiveness of QARO in reducing the operating cost and maximization the MGO benefit. [ABSTRACT FROM AUTHOR]

Purpose: Assembly lines are widely employed in manufacturing processes to produce final products in a flow efficiently. The simple assembly line balancing problem is a basic version of the general problem and has still attracted the attention of researchers. The type-I simple assembly line balancing problems (SALBP-I) aim to minimise the number of workstations on an assembly line by keeping the cycle time constant. Design/methodology/approach: This paper focuses on solving multi-objective SALBP-I problems by utilising an artificial bee colony based-hyper heuristic (ABC-HH) algorithm. The algorithm optimises the efficiency and idleness percentage of the assembly line and concurrently minimises the number of workstations. The proposed ABC-HH algorithm is improved by adding new modifications to each phase of the artificial bee colony framework. Parameter control and calibration are also achieved using the irace method. The proposed model has undergone testing on benchmark problems, and the results obtained have been compared with state-of-the-art algorithms. Findings: The experimental results of the computational study on the benchmark dataset unequivocally establish the superior performance of the ABC-HH algorithm across 61 problem instances, outperforming the state-of-the-art approach. Originality/value: This research proposes the ABC-HH algorithm with local search to solve the SALBP-I problems more efficiently. [ABSTRACT FROM AUTHOR]