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

This paper presents an optimized quantum circuit for the s c r y p t cryptographic algorithm. We applied various optimization techniques to reduce the DW cost, which is the product of the time and space complexity of quantum circuits. In our proposed method, the number of ancilla qubits was significantly reduced through the use of optimized inverse operations, while the depth was minimized by implementing parallel structures. For the SHA-256, we devised a structure that achieves a substantial reduction in the number of ancilla qubits with only a slight increase in quantum circuit depth. By cleaning the dirty ancilla qubits used in the previous round through inverse operations, we enabled their reuse in each subsequent round. Specifically, we reduced the number of 8128 ancilla qubits, achieving this with an increase of only 6 in the full depth of the quantum circuit. Additionally, within Salsa20/8 in SMix, we reused qubits through inverse operations and performed some operations in parallel to reduce both the number of qubits and the overall quantum circuit depth. Finally, our quantum circuit for s c r y p t demonstrates a significant reduction in the width (the number of qubits) with only a minimal increase in the full quantum circuit depth. [ABSTRACT FROM AUTHOR]

In this paper, we investigate a distributed interval optimization problem whose local functions are interval functions rather than scalar functions. Focusing on distributed interval optimization, this paper presents a distributed primal-dual algorithm. A criterion is introduced under which linear convergence to the Pareto solution of distributed interval optimization problems can be achieved without strong convexity. Lastly, a numerical simulation is presented to illustrate the linear convergence of the algorithm that has been proposed. [ABSTRACT FROM AUTHOR]

Economic Dispatch Problems (EDP) refer to the process of determining the power output of generation units such that the electricity demand of the system is satisfied at a minimum cost while technical and operational constraints of the system are satisfied. This procedure is vital in the efficient energy management of electricity networks since it can ensure the reliable and efficient operation of power systems. As power systems transition from conventional to modern ones, new components and constraints are introduced to power systems, making the EDP increasingly complex. This highlights the importance of developing advanced optimization techniques that can efficiently handle these new complexities to ensure optimal operation and cost-effectiveness of power systems. This review paper provides a comprehensive exploration of the EDP, encompassing its mathematical formulation and the examination of commonly used problem formulation techniques, including single and multi-objective optimization methods. It also explores the progression of paradigms in economic dispatch, tracing the journey from traditional methods to contemporary strategies in power system management. The paper categorizes the commonly utilized techniques for solving EDP into four groups: conventional mathematical approaches, uncertainty modelling methods, artificial intelligence-driven techniques, and hybrid algorithms. It identifies critical research gaps, a predominant focus on single-case studies that limit the generalizability of findings, and the challenge of comparing research due to arbitrary system choices and formulation variations. The present paper calls for the implementation of standardized evaluation criteria and the inclusion of a diverse range of case studies to enhance the practicality of optimization techniques in the field. [ABSTRACT FROM AUTHOR]

Snake optimizer (SO) is an optimization algorithm drawn from the reproductive habits of serpents. It exhibits outstanding effectiveness in solving continuous optimization problems. However, SO may face some performance challenges related to its population diversity and early convergence behavior. In this paper, we address the challenges of SO by introducing the Hybrid snake optimizer algorithm (HSOA). HSOA is a novel approach to optimization that incorporates two new optimization techniques into the SO algorithm. First, it incorporates a new opposition-based learning technique called Oppositional-mutual learning into the initialization stage of the SO algorithm. Second, it integrates dynamic polynomial mutation, which is an intelligent mutation method, into the initialization and optimization stages of the SO algorithm. These integrated approaches aim to increase the population's diversity of SO, while improving its searchability during its optimization stage. In power systems, the economic load dispatch (ELD) is an intricate optimization problem that becomes more challenging when the restrictions of the valve point effect (VPE) are incorporated into it. ELD with VPE is non-convex, lacking smoothness, and exhibiting nonlinearity that considers operational limitations expressed as both equality and inequality constraints to generate electricity. The suggested HSOA algorithm underwent evaluation and was compared with 47 renowned optimization algorithms across five real-world ELD problems with different specifications: generators with different unit capacities, transmission losses, prohibited operation zones, and Ramp Rate restrictions. The experimental results demonstrate that HSOA produces competitive solutions for the five real-world ELD problems. In detail, HSOA achieves the top rank in three cases of ELD problems with a 3-unit generator, and it secures the second and third positions in high-dimensional ELD problems with 40-unit and 80-unit generators, respectively. The statistical tests confirm the reliability and efficiency of HSOA. In addition, the effectiveness of HSOA was evaluated using the single-objective IEEE-CEC 2014 functions and compared to the results of eight popular metaheuristic algorithms. The results demonstrate that HSOA is a competitive optimization algorithm capable of solving the functions of IEEE-CEC 2014. [ABSTRACT FROM AUTHOR]

This document discusses the importance of optimization in various fields such as statistics, biology, finance, economics, and control. It highlights the advancements made in optimization theory and methods, including first-order methods and augmented Lagrangian methods. The document also introduces a special issue on "Symmetry in Optimization Theory, Algorithms and Applications," which features five papers on topics such as ensemble learning, ant colony system algorithms, subspace algorithms, consensus problems in multi-agent systems, and dynamic conditional correlation models. The authors express their gratitude to the contributors and reviewers of the special issue. [Extracted from the article]

The mathematical side of applied problems in multiple subject areas (biology, pattern recognition, etc.) is reduced to the problem of discrete optimization in the following mathematical method. We were provided a network and graphs in its leaves, for which we needed to find a rearrangement of graphs by non-leaf nodes, in which the given functional reached its minimum. Such a problem, even in the simplest case, is NP-hard, which means unavoidable restrictions on the network, on graphs, or on the functional. In this publication, this problem is addressed in the case of all graphs being so-called "structures", meaning directed-loaded graphs consisting of paths and cycles, and the functional as the sum (over all edges in the network) of distances between structures at the endpoints of every edge. The distance itself is equal to the minimal length of sequence from the fixed list of operations, the composition of which transforms the structure at one endpoint of the edge into the structure at its other endpoint. The list of operations (and their costs) on such a graph is fixed. Under these conditions, the given discrete optimization problem is called the reconstruction problem. This paper presents novel algorithms for solving the reconstruction problem, along with full proofs of their low error and low polynomial complexity. For example, for the network, the problem is solved with a zero error algorithm that has a linear polynomial computational complexity; and for the tree the problem is solved using an algorithm with a multiplicative error of at most two, which has a second order polynomial computational complexity. [ABSTRACT FROM AUTHOR]

With the gradual emergence of customized manufacturing, intelligent manufacturing systems have experienced widespread adoption, leading to a surge in research interests in the associated problem of intelligent scheduling. In this paper, we study the flexible job shop scheduling problem (FJSP) with setup time, handling time, and processing time in a multi-equipment work center production environment oriented toward smart manufacturing and make-to-order requirements. A mathematical model with the optimization objectives of minimizing the maximum completion time, the total number of machine adjustments, the total number of workpieces handled and the total load of the machine is constructed, and an improved discrete particle swarm algorithm based on Pareto optimization and a nonlinear adaptive inertia weighting strategy is proposed to solve the model. By integrating the model characteristics and algorithm features, a hybrid initialization method is designed to generate a higher-quality initialized population. Next, three cross-variance operators are used to implement particle position updates to maintain information sharing among particles. Then, the performance effectiveness of this algorithm is verified by testing and analyzing 15 FJSP test instances. Finally, the feasibility and effectiveness of the designed algorithm for solving multi-objective FJSPs are verified by designing an FJSP test example that includes processing time, setup time and handling time. [ABSTRACT FROM AUTHOR]

This paper is concerned with the design of a dual-loop control system for permanent magnet synchronous motor (PMSM). An improved linear extended state observer (LESO) with excellent estimation capability is employed to develop an improved linear active disturbance rejection control (LADRC) suitable for PMSM speed regulation, achieving outstanding disturbance suppression in PMSM speed control. The use of an internal model control scheme to initialize the parameters of the proportional-integral- (PI-) based current controller simplifies the search space of the control system parameter optimization. An improved particle swarm optimization (PSO) algorithm is applied to optimize the controller parameters, thereby enhancing the overall system performance. Finally, through a series of simulations and experiments, we validate that our proposed controller exhibits superior performance compared to some other control methods. [ABSTRACT FROM AUTHOR]

Machine learning algorithms often contain many hyperparameters whose values affect the predictive performance of the induced models in intricate ways. Due to the high number of possibilities for these hyperparameter configurations and their complex interactions, it is common to use optimization techniques to find settings that lead to high predictive performance. However, insights into efficiently exploring this vast space of configurations and dealing with the trade-off between predictive and runtime performance remain challenging. Furthermore, there are cases where the default hyperparameters fit the suitable configuration. Additionally, for many reasons, including model validation and attendance to new legislation, there is an increasing interest in interpretable models, such as those created by the decision tree (DT) induction algorithms. This paper provides a comprehensive approach for investigating the effects of hyperparameter tuning for the two DT induction algorithms most often used, CART and C4.5. DT induction algorithms present high predictive performance and interpretable classification models, though many hyperparameters need to be adjusted. Experiments were carried out with different tuning strategies to induce models and to evaluate hyperparameters' relevance using 94 classification datasets from OpenML. The experimental results point out that different hyperparameter profiles for the tuning of each algorithm provide statistically significant improvements in most of the datasets for CART, but only in one-third for C4.5. Although different algorithms may present different tuning scenarios, the tuning techniques generally required few evaluations to find accurate solutions. Furthermore, the best technique for all the algorithms was the Irace. Finally, we found out that tuning a specific small subset of hyperparameters is a good alternative for achieving optimal predictive performance. [ABSTRACT FROM AUTHOR]

In this work, an improved moth-flame optimization algorithm is proposed to alleviate the problems of premature convergence and convergence to local minima. From the perspective of diversity, an inertia weight of diversity feedback control is introduced in the moth-flame optimization to balance the algorithm's exploitation and global search abilities. Furthermore, a small probability mutation after the position update stage is added to improve the optimization performance. The performance of the proposed algorithm is extensively evaluated on a suite of CEC'2014 series benchmark functions and four constrained engineering optimization problems. The results of the proposed algorithm are compared with the ones of other improved algorithms presented in literatures. It is observed that the proposed method has a superior performance to improve the convergence ability of the algorithm. In addition, the proposed algorithm assists in escaping the local minima. [ABSTRACT FROM AUTHOR]

There is a trend to deploy neural network on edge devices in recent years. While the mainstream of research often concerns with single edge device processing and edge-cloud two-layer neural network collaborative computing, in this paper, we propose partitioning multi-layer edge network for neural network collaborative computing. With the proposed method, sub-models of neural network are deployed on multi-layer edge devices along the communication path from end users to cloud. Firstly, we propose an optimal path selection method to form a neural network collaborative computing path with lowest communication overhead. Secondly, we establish a time-delay optimization mathematical model to evaluate the effects of different partitioning solutions. To find the optimal partition solution, an ordered elitist genetic algorithm (OEGA) is proposed. The experimental results show that, compared with traditional cloud computing, single-device edge computing and edge-cloud collaborative computing, the proposed multi-layer edge network collaborative computing has a smaller runtime delay with limited bandwidth resources, and because of the pipeline computing characteristics, the proposed method has a better response speed when processing large number of requests. Meanwhile, the OEGA algorithm has better performance than conventional methods, and the optimized partitioning method outperforms other methods like random and evenly partition. [ABSTRACT FROM AUTHOR]

Aquila optimizer (AO) is an efficient meta-heuristic optimization method, which mimics the hunting style of Aquila in nature. However, the AO algorithm may suffer from immature convergence during the exploitation stage. In this paper, two strategies are elegantly employed into conventional AO, such as random opposition-based learning and nonlinear flexible jumping factor, which can efficiently enhance the performance of conventional AO. Experiments on 1 7 benchmark functions and image segmentation demonstrate the effectiveness of the proposed algorithm. Comparison with several state-of-the-art meta-heuristic optimization techniques indicates the efficacy of the developed method. [ABSTRACT FROM AUTHOR]

A wireless sensor network (WSN) consists of an extensive number of low-power sensor nodes to gather information from their environment and monitor physical activities. This makes node localization a crucial aspect in most WSN applications since measurement data is worthless unless the location from where the data is acquired is known precisely. The majority of localization solutions rely on anchor nodes for estimating the node locations with different localization accuracy, complexity, and hence different applicability. But, the cost and complexity in the localization of large-scale WSNs are not significantly reduced. In this paper, a novel Hybridized Dragonfly and Jaya Optimization technique (HyDAJ) is introduced for improving localization accuracy and performance of mobile WSNs. The proposed hybrid technique combines the advantages of Dragonfly algorithm and Jaya algorithm to localize the sensor nodes in a more efficient way and overcomes the limitations of the original algorithm. The hybrid algorithm verifies that all target nodes are precisely localized with higher accuracy. Simulation results reveal that HyDAJ outperforms existing methods under multiple metrics including localization efficiency, mean localization error, computation time, and energy consumption. [ABSTRACT FROM AUTHOR]

Detecting small and densely packed objects in images remains a significant challenge in computer vision. Existing object detection methods often exhibit low accuracy and frequently miss detection when identifying dense small objects and require larger model parameters. This study introduces a novel detection framework designed to address these limitations by integrating advanced feature fusion and optimization techniques. Our approach focuses on enhancing both detection accuracy and parameter efficiency. The approach was evaluated on the open-source VisDrone2019 data set and compared with mainstream algorithms. Experimental results demonstrate a 70.2% reduction in network parameters and a 6.3% improvement in mAP@0.5 over the original YOLOv7 algorithm. These results demonstrate that the enhanced model surpasses existing algorithms in detecting small objects. [ABSTRACT FROM AUTHOR]

Doppler positioning, as an early form of positioning, has regained significant research interest in the context of Low Earth Orbit (LEO) satellites.Given the LEO mega-constellation scenario, the objective function of Doppler positioning manifests significant nonlinearity, leading to ill-conditioning challenges for prevalent algorithms like iterative least squares (LS) estimation, especially in cases where inappropriate initial values are selected. In this study, we investigate the causes of ill-posed problems from two perspectives. Firstly, we analyze the linearization errors of the Doppler observation equations in relation to satellite orbital altitude and initial value errors, revealing instances where traditional algorithms may fail to converge. Secondly, from an optimization theory perspective, we demonstrate the occurrence of convergence to locally non-unique solutions for Doppler positioning. Subsequently, to address these ill-conditioning issues, we introduce Tikhonov regularization terms in the objective function to constrain algorithm divergence, with a fitted model for the regularization coefficient. Finally, we conduct comprehensive simulation experiments in both dynamic and static scenarios to validate the performance of the proposed algorithm. On the one hand, when the initial values are set to 0, our algorithm achieves high-precision positioning, whereas the iterative LS fails to converge. On the other hand, in certain simulation scenarios, the iterative LS converges to locally non-unique solutions, resulting in positioning errors exceeding 50 km in the north and east directions, several hundred kilometers in the vertical direction, and velocity errors surpassing 120 m/s. In contrast, our algorithm demonstrates typical errors of a position error of 6.8462 m, velocity error of 0.0137 m/s, and clock drift error of 8.3746 × 10 − 6 s/s. This work provides an effective solution to the sensitivity issue of initial points in Doppler positioning and can serve as a reference for the algorithm design of Doppler positioning receivers with LEO mega-constellations. [ABSTRACT FROM AUTHOR]

In this paper, we define a new area-type filter algorithm based on the trust-region method. A relaxed trust-region quadratic correction subproblem is proposed to compute the trial direction at the current point. Consider the objective function and the constraint violation function at the current point as a point pair. We divide the point pairs into different partitions by the dominant region of the filter and calculate the contributions of the point pairs to the area of the filter separately. Different from the conventional filter, we define the contribution as the filter acceptance criterion for the trial point. The nonmonotone area-average form is also adopted in the filter mechanism. In this paper, monotone and nonmonotone methods are proposed and compared with the numerical values. Furthermore, the algorithm is proved to be convergent under some reasonable assumptions. The numerical experiment shows the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

The pursuit of achieving total power network observability in smart grids using Phasor Measurement Units (PMUs) carries a significant promise of real-time Wide-Area Monitoring, Protection, and Control (WAMPAC). PMU applications eliminate periodical measurements, thereby increasing accuracy through a high sampling rate of the measured power systems quantities. The high costs of installation of PMUs for total power system observability presents a challenge in the implementation of PMUs. This is due to the expensive costs of PMU devices. This has led to a prominent optimal PMU placement (OPP) problem that researchers tirelessly aim to solve by ensuring a complete power network observability while using the least installed PMU devices possible. In this paper, a novel Binary Firefly Algorithm (BFA) based on the node degree centrality scores of each bus is proposed to minimize PMU installations. The BFA solves the OPP problem in consideration of the effect of Zero Injection Buses (ZIBs) under normal operation and single PMU outage (SPO). The robustness and efficiency of the proposed algorithm is tested on IEEE-approved test systems and visualized with a force-directed technique on an undirected power network graph. The proposed BFA yields the same but better optimal PMU numbers, obtained by existing meta-heuristic optimization techniques found in the literature for each of the IEEE test cases, as well as highlighting the cost–benefit of having a robust system against single PMU loss while considering the ZIB effect for an improved system measurement availability. [ABSTRACT FROM AUTHOR]

In order to improve the autonomy and intelligence of group animation behavior, this paper proposes a particle swarm optimization algorithm for automatic control of group animation behavior. After the user inputs the group animation behavior construction target, the individual path is planned according to the fixed behavior rules, and the particle swarm optimization algorithm and the improved artificial fish swarm optimization algorithm are used to select the optimal action for the individual in the group animation according to the top-down control process. The experimental results show that after the PSO converges to the global optimal solution, the evolution times of the PSO converge to the global optimal solution again when the surrounding environment changes 10 times. The population diversity of particle swarm optimization algorithm is obviously better than those of the other two algorithms in the process of automatically controlling the movement of individuals to the circle and to the stand in the crowd animation. Compared with the original algorithm AFSA, the improved artificial fish swarm algorithm proposed in this paper is easier to obtain the global optimal value, and the optimization speed is also improved. In conclusion, the example analysis verifies that the algorithm can effectively realize the automatic control of group animation behavior and has high convergence speed and high autonomy. [ABSTRACT FROM AUTHOR]

Based on the theory and application, this paper discusses the optimization of art image segmentation algorithm based on FFNN (Feed Forward Neural Network). In this paper, residual units are used in the corresponding stages of encoder and decoder, and feature information of several convolution layers in each convolution stage of encoder is extracted at the same time. And the feature pyramid module is used to extract multiscale features from the feature map of the last convolution stage in the encoder. Finally, pixel by pixel additions combine the previously mentioned feature information into the corresponding layer of the decoder. Additionally, an improved weight adaptive algorithm based on feature preservation is suggested in this paper, which addresses the issue that the conventional image segmentation algorithm is noise-sensitive. The adaptive connection weight mechanism is also introduced. The accuracy and recall rates of this optimization algorithm can both reach 96.574%, according to the results of 50% cross-validation. All the segmentation performance evaluation indexes of this algorithm are higher than the existing main algorithms. Moreover, the algorithm takes a short time, does not need too much manual intervention, and can effectively segment artistic images. The optimization algorithm in this paper has certain reference significance for the related research of artistic image segmentation. [ABSTRACT FROM AUTHOR]

This paper proposes a parameter coordination optimization design of a power system stabilizer (PSS) based on an improved quasi-affine transformation evolutionary (QUATRE) algorithm to suppress low-frequency oscillation and improve the dynamic stability of power systems. To begin, the simulated annealing (SA) algorithm randomly updates the globally optimal solution of each QUATRE iteration and matches the inferior solution with a certain probability to escape the local extreme point. This new algorithm is first applied to the power system. Since the damping ratio is one of the criteria with which to measure the dynamic stability of the power system, this paper sets the objective function according to the principle of maximization of the damping coefficient of the electromechanical mode, and uses SA-QUATRE to search a group of global optimal PSS parameter combinations to improve the safety factor of the system as much as possible. Finally, the method's rationality and validity were validated by applying it to the simulation examples of the IEEE 4-machine 2-area system with different operation states. The comparison with the traditional optimization algorithm shows that the proposed method has more advantages for multi-machine PSS parameter coordination optimization, can restrain the low-frequency oscillation of the power system more effectively and can enhance the system's stability. [ABSTRACT FROM AUTHOR]

As is well known that the global optimization ability of the Fruit fly Optimization Algorithm (FOA)is weak because it is easy to fall into local optimum. In this paper, a Fruit Fly Optimization Algorithm based on Locality Sensitive Hashing-aware (LSHFOA)was proposed. The locality sensitive hashing mechanism to optimize the generation mechanism for swarm population individuals was used, which can improve the individual diversity of the population. Meanwhile, when the fruit fly population falls into the local optimum, the locality sensitive hashing mechanism was adopted to change the population location, which is used for jumping out of local optimal limits. To verify the performance of LSHFOA, it was compared with FOA and its improvement algorithms CFOA, and IFFO with 8 representative benchmark functions. A large number of experimental results showed that LSHFOA has a faster convergence speed and higher precision of optimization for function optimization, especially in high-dimensional multi-peak functions. In addition to the theoretical evaluation, we also evaluate its performance in a real-world scenario. Generally, an edge computing environment, as an extension of cloud computing, can allow the users to access the network in a low-latency manner. In this way, to capture the high-speed convergence advantage, this paper makes the first attempt to tackle a classic research problem in the edge computing environment, i.e., the edge server placement problem. The experimental results show that the new algorithm has an excellent application effect. [ABSTRACT FROM AUTHOR]

In this paper, develop image encryption algorithm based on a chaotic bit-plane decomposition and optimization algorithm of a War Optimization Algorithm (WOA). Initially, utilization of SHA-256 hash algorithm for computing the plaintext images hash parameter as initial parameter of the fractional Lorenz hyperchaotic system after the process. Use the chaotic sequence for permuting plaintext image in a bit plane to achieve the scrambled images. After that, block the scrambles image into four sub images of similar size and count the hash parameter of every row of every block through the SHA-256 hash algorithm as the initial parameter of the Sine-Tent Logistic chaotic system. Utilize the achieved chaotic sequence to substitute the images. After that, the four sub-block images to get the last encrypted image and the population can be achieved. At last, utilization information entropy of ciphertext images as the fitness function of WOA. Choose the ciphertext image with the optimal information entropy of ciphertext images as the fitness function of the WOA. Select the ciphertext image with the best information entropy from the population as the optimal encrypted image, and then, return the position value of the best war source meanwhile. The proposed method is implemented in MATLAB and performance is analyzed with performance metrices. The proposed method is compared with the conventional techniques. [ABSTRACT FROM AUTHOR]

The efficiency and the effectiveness of a machine learning (ML) model are greatly influenced by feature selection (FS), a crucial preprocessing step in machine learning that seeks out the ideal set of characteristics with the maximum accuracy possible. Due to their dominance over traditional optimization techniques, researchers are concentrating on a variety of metaheuristic (or evolutionary) algorithms and trying to suggest cutting-edge hybrid techniques to handle FS issues. The use of hybrid metaheuristic approaches for FS has thus been the subject of numerous research works. The purpose of this paper is to critically assess the existing hybrid FS approaches and to give a thorough literature review on the hybridization of different metaheuristic/evolutionary strategies that have been employed for supporting FS. This article reviews pertinent documents on hybrid frameworks that were published in the period from 2009 to 2022 and offers a thorough analysis of the used techniques, classifiers, datasets, applications, assessment metrics, and schemes of hybridization. Additionally, new open research issues and challenges are identified to pinpoint the areas that have to be further explored for additional study. [ABSTRACT FROM AUTHOR]

Clean and renewable energy is the only way to achieve sustainable energy development, with considerable social and economic benefits. As a key technology for clean and renewable energy, it is very important to research the reliability optimization of microgrids. This paper reviews the research progress in microgrid reliability optimization. This paper first classifies and summarizes the existing research on microgrid control strategies and reliability assessment. Then, the system reliability optimization framework is summarized in terms of both microgrid systems and optimization objectives. Next, we summarize the most commonly used optimization algorithms for microgrid reliability for different microgrid systems. Finally, we provide a bibliometric analysis of the literature on the reliability research of microgrids. In addition, we propose some research challenges in the future for the reliability of microgrids. [ABSTRACT FROM AUTHOR]

The importance of the time-cost-quality trade-off problem in construction projects has been widely recognized. Its goal is to minimize time and cost and maximize quality. In this paper, the bonus-penalty mechanism is introduced to improve the traditional time-cost model, and considering the nonlinear relationship between quality and time, a nonlinear time-cost quality model is established. Meanwhile, in order to better solve the time-cost-quality trade-off problem, a multi-objective immune wolf colony optimization algorithm has been proposed. The hybrid method combines the fast convergence of the wolf colony algorithm and the excellent diversity of the immune algorithm to improve the accuracy of the wolf colony search process. Finally, a railway construction project is taken as an example to prove the effectiveness of the method. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) have recently been increasingly popular in various areas, fields, and applications. Military, disaster management, rescue operations, public services, agriculture, and various other areas are examples. As a result, UAV path planning is concerned with determining the optimal path from the source to the destination while avoiding collisions with lowering the cost of time, energy, and other resources. This review aims to assort academic studies on the path planning optimization in UAV using meta-heuristic algorithms, summarize the results of each optimization algorithm, and extend the understanding of the current state of the path planning in UAV in the meta-heuristic optimization field. For this purpose, we implemented a broad, automated search using Boolean and snowballing searching methods to find academic works on path planning in UAVs. Studies and papers have been distinguished, and the following information was obtained and aggregated from each article: authors, publication's year, the journal name or the conference name, proposed algorithms, the aim of the study, the outcome, and the quality of each study. According to the findings, the meta-heuristic algorithm is a standard optimization method for tackling single and multi-objective problems. Besides, the findings show that meta-heuristic algorithms have a great compact on the path planning optimization in UAVs, and there is good progress in this field. However, the problem still exists mainly in complex and dynamic environments, on battlefields, in rescue missions, mobile obstacles, and with multiple UAVs. [ABSTRACT FROM AUTHOR]

Although the existing protein recognition methods have improved the recognition accuracy of key proteins to a certain extent, they have ignored the biological features of the proteins. In view of this shortcoming, this paper constructed a high-order dynamic complex protein network for key protein recognition. At first, this paper presented a method for feature selection and candidate set evaluation of complex protein network; a weighted network was constructed based on the obtained topological features of the complex protein network and the semantic similarity of protein gene ontology annotations. Then, this paper proposed an algorithm for recognizing key proteins in high-order dynamic protein network based on a Fruit fly optimization algorithm. At last, the effectiveness of the proposed model was verified by experimental results. [ABSTRACT FROM AUTHOR]

With the increasingly widespread application of UAV intelligence, the need for autonomous navigation and positioning is becoming more and more important. To solve the problem that UAV cannot perform localization in complex scenes, a new multi-source fusion framework factor graph optimization algorithm is used for UAV localization state estimation in this paper, which is based on IMU/GNSS/VO multi-source sensors. Based on the factor graph model and the iSAM incremental inference algorithm, a multi-source fusion model of IMU/GNSS/VO is established, including the IMU pre-integration factor, IMU bias factor, GNSS factor, and VO factor. Mathematical simulations and validations on the EuRoC dataset show that, when the selected sliding window size is 30, the factor graph optimization (FGO) algorithm can not only meet the requirements of real time and accuracy at the same time, but it also achieves a plug-and-play function in the event of local sensor failures. Finally, compared with the traditional federated Kalman algorithm and the adaptive federated Kalman algorithm, the positioning accuracy of the FGO algorithm in this paper is improved by 1.5–2-fold, and can effectively improve autonomous navigation system robustness and flexibility in complex scenarios. Moreover, the multi-source fusion framework in this paper is a general algorithm framework that can satisfy other scenarios and other types of sensor combinations. [ABSTRACT FROM AUTHOR]

To solve the problem of low accuracy in the traditional FunkSVD recommendation algorithm, an improved FunkSVD algorithm (RM-FS) is proposed. RM-FS is an improvement of the traditional FunkSVD algorithm, using RMSProp, a deep learning optimization algorithm. The RM-FS algorithm can not only solve the problem of reduced accuracy of the traditional FunkSVD algorithm because of iterative oscillations but also alleviate the impact of data sparseness on the accuracy of the algorithm, achieving the effect of improving the accuracy of the traditional algorithm. The experimental results show that the RM-FS algorithm proposed in this paper effectively improves the accuracy of the recommendation algorithm, which is better than the traditional FunkSVD recommendation algorithm and other improved FunkSVD algorithms. [ABSTRACT FROM AUTHOR]

We derive a double-optimal iterative algorithm (DOIA) in an m-degree matrix pencil Krylov subspace to solve a rectangular linear matrix equation. Expressing the iterative solution in a matrix pencil and using two optimization techniques, we determine the expansion coefficients explicitly, by inverting an m × m positive definite matrix. The DOIA is a fast, convergent, iterative algorithm. Some properties and the estimation of residual error of the DOIA are given to prove the absolute convergence. Numerical tests demonstrate the usefulness of the double-optimal solution (DOS) and DOIA in solving square or nonsquare linear matrix equations and in inverting nonsingular square matrices. To speed up the convergence, a restarted technique with frequency m is proposed, namely, DOIA(m); it outperforms the DOIA. The pseudoinverse of a rectangular matrix can be sought using the DOIA and DOIA(m). The Moore–Penrose iterative algorithm (MPIA) and MPIA(m) based on the polynomial-type matrix pencil and the optimized hyperpower iterative algorithm OHPIA(m) are developed. They are efficient and accurate iterative methods for finding the pseudoinverse, especially the MPIA(m) and OHPIA(m). [ABSTRACT FROM AUTHOR]

In an effort to overcome the problems with typical optimization algorithms' slow convergence and tendency to settle on a local optimal solution, an improved golden jackal optimization technique is proposed. Initially, the development mechanism is enhanced to update the prey's location, addressing the limitation of just relying on local search in the later stages of the algorithm. This ensures a more balanced approach to both algorithmic development and exploration. Furthermore, incorporating the instinct of evading natural predators enhances both the effectiveness and precision of the optimization process. Then, cross-mutation enhances population variety and facilitates escaping from local optima. Finally, the crossbar strategy is implemented to change both the individual and global optimal solutions of the population. This technique aims to decrease blind spots, enhance population variety, improve solution accuracy, and accelerate convergence speed. A total of 20 benchmark functions are employed for the purpose of comparing different techniques. The enhanced algorithm's performance is evaluated using the CEC2017 test function, and the results are assessed using the rank-sum test. Ultimately, three conventional practical engineering simulation experiments are conducted to evaluate the suitability of IWKGJO for engineering issues. The results obtained demonstrate the beneficial effects of the altered methodology and illustrate that the expanded golden jackal optimization algorithm has superior convergence accuracy and a faster convergence rate. [ABSTRACT FROM AUTHOR]

The visibility of pavement markings is one of the most critical factors for traffic safety, and a periodical assessment plan is crucial for maintaining this function. Traditional assessment methods, such as visual windshield surveys or manual testing using handheld devices, are unsafe, time-consuming, and labor-intensive. In recent years, transportation agencies have begun to adopt the use of mobile retroreflectivity units (MRUs) for condition assessment of pavement markings. MRUs, different from other manual methods, can be utilized to collect large-scale retroreflectivity data in an efficient manner. However, no relevant research has yet proposed a mathematical optimization model for arranging the evaluation schedule and paths of MRUs. This study aims to propose a MRU routing model, and an efficient solution methodology. A branch-and-price algorithm, including column generation and branch-and-bound, was implemented. Computational experiments have been conducted based on actual tasks from the Florida MRU program for validation. Results show that the proposed solution methodology with a set partitioning model in this study not only finds the optimal solution for problems with tasks less than 60, but also effectively narrows the solution gap to be within 1.0% for problems with tasks less than 131. [ABSTRACT FROM AUTHOR]

There are many pre-trained deep learning-based face recognition models developed in the literature, such as FaceNet, ArcFace, VGG-Face, and DeepFace. However, performing transfer learning of these models for handling face sketch recognition is not applicable due to the challenge of limited sketch datasets (single sketch per subject). One promising solution to mitigate this issue is by using optimization algorithms, which will perform a fine-tuning and fitting of these models for the face sketch problem. Specifically, this research introduces an enhanced optimizer that will evolve these models by performing automatic weightage/fine-tuning of the generated feature vector guided by the recognition accuracy of the training data. The following are the key contributions to this work: (i) this paper introduces a novel Smart Switching Slime Mold Algorithm (S2SMA), which has been improved by embedding several search operations and control rules; (ii) the proposed S2SMA aims to fine-tune the pre-trained deep learning models in order to improve the accuracy of the face sketch recognition problem; and (iii) the proposed S2SMA makes simultaneous fine-tuning of multiple pre-trained deep learning models toward further improving the recognition accuracy of the face sketch problem. The performance of the S2SMA has been evaluated on two face sketch databases, which are XM2VTS and CUFSF, and on CEC's 2010 large-scale benchmark. In addition, the outcomes were compared to several variations of the SMA and related optimization techniques. The numerical results demonstrated that the improved optimizer obtained a higher level of fitness value as well as better face sketch recognition accuracy. The statistical data demonstrate that S2SMA significantly outperforms other optimization techniques with a rapid convergence curve. [ABSTRACT FROM AUTHOR]

The theory of variational inequalities is an important tool in physics, engineering, finance, and optimization theory. The projection algorithm and its variants are useful tools for determining the approximate solution to the variational inequality problem. This paper introduces three distinct extragradient algorithms for dealing with variational inequality problems involving quasi-monotone and semistrictly quasi-monotone operators in infinite-dimensional real Hilbert spaces. This problem is a general mathematical model that incorporates a set of applied mathematical models as an example, such as equilibrium models, optimization problems, fixed point problems, saddle point problems, and Nash equilibrium point problems. The proposed algorithms employ both fixed and variable stepsize rules that are iteratively transformed based on previous iterations. These algorithms are based on the fact that no prior knowledge of the Lipschitz constant or any line-search framework is required. To demonstrate the convergence of the proposed algorithms, some simple conditions are used. Numerous experiments have been conducted to highlight the numerical capabilities of algorithms. [ABSTRACT FROM AUTHOR]

In order to improve the effect of agricultural machinery task scheduling, this paper starts from the perspective of multiobjective optimization to achieve task scheduling based on multiobjective particle swarm optimization algorithm. The position of each particle is a combination of resource options for each construction activity, and the displacement range and speed range of the particles are determined. Moreover, this paper uses the method of introducing an external storage library to store the current noninferior solutions and uses the adaptive grid method and the roulette selection method to select the global optimal solution of the particles. In addition, this paper proposes a task scheduling algorithm suitable for modern agricultural machinery based on the actual needs of current agricultural machinery task scheduling. The experimental results show that the agricultural machinery task scheduling algorithm based on multiobjective optimization proposed in this paper has a good agricultural machinery task scheduling effect and meets the basic purpose of optimizing the algorithm in this paper. [ABSTRACT FROM AUTHOR]

In this paper, a heuristic approach to doubly fed induction generator (DFIG) protection and low voltage ride through (LVRT) is carried out. DFIG-based wind systems are rapidly penetrating the power generation section. Despite their advantages, their direct coupling grid makes them highly sensitive to symmetrical faults. A well-known solution to this is the crowbar method of DFIG protection. This paper provides a method to determine the optimal crowbar resistance value, to ensure a strong trade-off between the rotor current and DC voltage transients. Further, since the crowbar method requires disconnection from the grid, the linear quadratic regulator (LQR) is applied to the system. This is to ensure fault ride through compliance with recent grid code requirements. The well-known PSO, as well as the recently developed African vultures optimization algorithm (AVOA), was applied to the problem. The first set of results show that for severe symmetrical voltage dips, the AVOA provides a good option for crowbar magnitude optimization, whereas PSO performed better for moderately severe dips. Secondly, when the LQR was optimized via the AVOA, it exhibited superiority over the conventional PSO-based PI controller. This superiority was in terms of rotor current transient magnitude, DC voltage transient magnitude, and reactive power steady-state ripple and were in the order of 67.5%, 20.35%, and 37.55%, respectively. When comparing the crowbar method and the LQR, it was observed that despite the LQR exhibiting superiority in terms of transient performance, the crowbar method offered a unanimously superior settling time. [ABSTRACT FROM AUTHOR]

Unconstrained continuous large-scale global optimization (LSGO) is still a challenging task for a wide range of modern metaheuristic approaches. A cooperative coevolution approach is a good tool for increasing the performance of an evolutionary algorithm in solving high-dimensional optimization problems. However, the performance of cooperative coevolution approaches for LSGO depends significantly on the problem decomposition, namely, on the number of subcomponents and on how variables are grouped in these subcomponents. Also, the choice of the population size is still an open question for population-based algorithms. This paper discusses a method for selecting the number of subcomponents and the population size during the optimization process ("on fly") from a predefined pool of parameters. The selection of the parameters is based on their performance in the previous optimization steps. The main goal of the study is the improvement of coevolutionary decomposition-based algorithms for solving LSGO problems. In this paper, we propose a novel self-adapt evolutionary algorithm for solving continuous LSGO problems. We have tested this algorithm on 15 optimization problems from the IEEE LSGO CEC'2013 benchmark suite. The proposed approach, on average, outperforms cooperative coevolution algorithms with a static number of subcomponents and a static number of individuals. [ABSTRACT FROM AUTHOR]

Featured Application: This article belongs to the Section Computing and Artificial Intelligence. Ineffective protein feature representation poses problems in protein classification in hierarchical structures. Discrete wavelet transform (DWT) is a feature representation method which generates global and local features based on different wavelet families and decomposition levels. To represent protein sequences, the proper wavelet family and decomposition level must be selected. This paper proposed a hybrid optimization method using particle swarm optimization and the firefly algorithm (FAPSO) to choose the suitable wavelet family and decomposition level of wavelet transformation for protein feature representation. The suggested approach improved on the work of earlier researchers who, in most cases, manually selected the wavelet family and level of decomposition based solely on experience and not on data. The paper also applied the virtual class methods to overcome the error propagation problems in hierarchical classification. The effectiveness of the proposed method was tested on a G-Protein Coupled Receptor (GPCR) protein data set consisting of 5 classes at the family level, 38 classes at the subfamily level, and 87 classes at the sub-subfamily level. Based on the result obtained, the most selected wavelet family and decomposition level chosen to represent GPCR classes by FAPSO are Biorthogonal wavelets and decomposition level 1, respectively. The experimental results show that the representation of GPCR protein using the FAPSO algorithm with virtual classes can yield 97.9%, 86.9%, and 81.3% classification accuracy at the family, subfamily, and sub-subfamily levels, respectively. In conclusion, the result shows that the selection of optimized wavelet family and decomposition level by the FAPSO algorithm, and the virtual class method can be potentially used as the feature representation method and a hierarchical classification method for GPCR protein. [ABSTRACT FROM AUTHOR]

In view of the slow convergence speed of traditional particle swarm optimization algorithms, which makes it easy to fall into local optimum, this paper proposes an OTSU multi-threshold image segmentation based on an improved particle swarm optimization algorithm. After the particle swarm completes the iterative update speed and position, the method of calculating particle contribution degree is used to obtain the approximate position and direction, which reduces the scope of particle search. At the same time, the asynchronous monotone increasing social learning factor and the asynchronous monotone decreasing individual learning factor are used to balance global and local search. Finally, chaos optimization is introduced to increase the diversity of the population to achieve OTSU multi-threshold image segmentation based on improved particle swarm optimization (IPSO). Twelve benchmark functions are selected to test the performance of the algorithm and are compared with the traditional meta-heuristic algorithm. The results show the robustness and superiority of the algorithm. The standard dataset images are used for multi-threshold image segmentation experiments, and some traditional meta-heuristic algorithms are selected to compare the calculation efficiency, peak signal to noise ratio (PSNR), structural similarity (SSIM), feature similarity (FSIM), and fitness value (FITNESS). The results show that the running time of this paper is 30% faster than other algorithms in general, and the accuracy is also better than other algorithms. Experiments show that the proposed algorithm can achieve higher segmentation accuracy and efficiency. [ABSTRACT FROM AUTHOR]

Aiming at the relative pose estimation of non-cooperative targets in space traffic management tasks, a two-step pose estimation method, based on spatially intersecting straight lines, is proposed, which mainly includes three aspects: (1) Use binocular vision to reconstruct the straight space line, and based on the direction vector of the straight line and the intersection of the straight line, solve the pose of the measured target in the measurement coordinate system, and obtain the initial value of the pose estimation. (2) Analyze the uncertainty of the spatial straight-line imaging, construct the uncertainty description matrix of the line, and filter the line features, accordingly. (3) Analyze the problems existing in the current linear distance measurement, construct the spatial linear back-projection error in the parametric coordinate space, and use the linear imaging uncertainty to weigh the projection error term to establish the optimization objective function of the pose estimation. Finally, the nonlinear optimization algorithm is used to iteratively solve the above optimization problem, to obtain high-precision pose estimation results. The experimental results show that the two-step pose estimation algorithm, proposed in this paper, can effectively achieve a high-precision and robust pose estimation for non-cooperative spatial targets. When the measurement distance is 10 m, the position accuracy can reach 10 mm, and the attitude measurement accuracy can reach 1°, which meets the pose estimation accuracy requirements of space traffic management. [ABSTRACT FROM AUTHOR]

Large-scale numerical optimization problems arise from many fields and have applications in both industrial and academic contexts. Finding solutions to such optimization problems efficiently requires algorithms that are able to leverage the increasing parallelism available in modern computing hardware. In this paper, we review previous work on parallelizing algorithms for nonlinear optimization. To introduce the topic, the paper starts by giving an accessible introduction to nonlinear optimization and high-performance computing. This is followed by a survey of previous work on parallelization and utilization of high-performance computing hardware for nonlinear optimization algorithms. Finally, we present a number of optimization software libraries and how they are able to utilize parallel computing today. This study can serve as an introduction point for researchers interested in nonlinear optimization or high-performance computing, as well as provide ideas and inspiration for future work combining these topics. [ABSTRACT FROM AUTHOR]

This article considers a box-constrained global optimization problem for Lipschitz continuous functions with an unknown Lipschitz constant. The well-known derivative-free global search algorithm DIRECT (DIvide RECTangle) is a promising approach for such problems. Several studies have shown that recent two-step (global and local) Pareto selection-based algorithms are very efficient among all DIRECT-type approaches. However, despite its encouraging performance, it was also observed that the candidate selection procedure has two possible shortcomings. First, there is no limit on how small the size of selected candidates can be. Secondly, a balancing strategy between global and local candidate selection is missing. Therefore, it may waste function evaluations by over-exploring the current local minimum and delaying finding the global one. This paper reviews and employs different strategies in a two-step Pareto selection framework (1-DTC-GL) to overcome these limitations. A detailed experimental study has revealed that existing strategies do not always improve and sometimes even worsen results. Since 1-DTC-GL is a DIRECT-type algorithm, the results of this paper provide general guidance for all DIRECT-type algorithms on how to deal with excessive local refinement more efficiently. [ABSTRACT FROM AUTHOR]

This paper presents the data description of the African buffalo optimization algorithm (ABO). ABO is a recently-designed optimization algorithm that is inspired by the migrant behaviour of African buffalos in the vast African landscape. Organizing their large herds that could be over a thousand buffalos using just two principal sounds, the /maaa/ and the /waaa/ calls present a good foundation for the development of an optimization algorithm. Since elaborate descriptions of the manual workings of optimization algorithms are rare in literature, this paper aims at solving this problem, hence it is our main contribution. It is our belief that elaborate manual description of the workings of optimization algorithms make it user-friendly and encourage reproducibility of the experimental procedures performed using this algorithm. Again, our ability to describe the algorithm's basic flow, stochastic and data generation processes in a language so simple that any non-expert can appreciate and use as well as the practical implementation of the popular benchmark Rosenbrock and Shekel Foxhole functions with the novel algorithm will assist the research community in benefiting maximally from the contributions of this novel algorithm. Finally, benchmarking the good experimental output of the ABO with those of the popular, highly effective and efficient Cuckoo Search and Flower Pollination Algorithm underscores the ABO as a worthy contribution to the existing body of population-based optimization algorithms [ABSTRACT FROM AUTHOR]

Feature selection provides the optimal subset of features for data mining models. However, current feature selection methods for high-dimensional data also require a better balance between feature subset quality and computational cost. In this paper, an efficient hybrid feature selection method (HFIA) based on artificial immune algorithm optimization is proposed to solve the feature selection problem of high-dimensional data. The algorithm combines filter algorithms and improves clone selection algorithms to explore the feature space of high-dimensional data. According to the target requirements of feature selection, combined with biological research results, this method introduces the lethal mutation mechanism and the Cauchy operator to improve the search performance of the algorithm. Moreover, the adaptive adjustment factor is introduced in the mutation and update phases of the algorithm. The effective combination of these mechanisms enables the algorithm to obtain a better search ability and lower computational costs. Experimental comparisons with 19 state-of-the-art feature selection methods are conducted on 25 high-dimensional benchmark datasets. The results show that the feature reduction rate for all datasets is above 99%, and the performance improvement for the classifier is between 5% and 48.33%. Compared with the five classical filtering feature selection methods, the computational cost of HFIA is lower than the two of them, and it is far better than these five algorithms in terms of the feature reduction rate and classification accuracy improvement. Compared with the 14 hybrid feature selection methods reported in the latest literature, the average winning rates in terms of classification accuracy, feature reduction rate, and computational cost are 85.83%, 88.33%, and 96.67%, respectively. [ABSTRACT FROM AUTHOR]

Considering that in the process of job scheduling, the cluster load should be prebalanced rather than remedied when the load is seriously unbalanced; therefore, in this paper, the task scheduling flow of the Hadoop cluster is analyzed deeply. On the Hadoop platform, a self-dividing algorithm is proposed for load balancing. An intelligent optimization algorithm is used to solve load balance. A dynamic feedback load balancing scheduling method is proposed from the point of view of task scheduling. In order to solve the shortcoming of the fair scheduling algorithm, this paper proposes two ways to improve the resource utilization and overall performance of Hadoop. When the mapping task is completed and the tasks to be reduced are assigned, the task assignment is based on the performance of the nodes to be reduced. It gives full play to the advantages of the ant colony algorithm and the hive colony algorithm so that the fusion algorithm can better deal with load balance. Then, three existing scheduling algorithms are introduced in detail: single queue scheduling, capacity scheduling, and fair scheduling. On this basis, an improved task scheduling strategy based on genetic algorithm is proposed to allocate and execute application tasks to reduce task completion time. The experiment verifies the effectiveness of the algorithm. The LBNP algorithm greatly improves the efficiency of reducing task execution and job execution. The delay capacity scheduling algorithm can ensure that most tasks can achieve localization scheduling, improve resource utilization, improve load balance, and speed up job completion time. [ABSTRACT FROM AUTHOR]

Accurate and effective wind power prediction plays an important role in wind power generation, distribution, and management. Inthis paper, a hybrid algorithm based on gradient descent and meta-heuristic optimization is designed to improve the accuracy of prediction and reduce the computational burden. The hybrid algorithm includes three steps: in the first step, we use the gradient descent algorithm to get the initial parameters. Secondly, we input the initial parameters into the meta-heuristic optimization algorithm to search for the "best parameters" (high-quality inferior solutions). Finally, we input optimized parameters into the RMSProp optimization algorithm and conduct gradient descent again to find a better solution. We used 2021 wind power data from Guangxi, China for the experiment. The results show that the hybrid prediction algorithm has better performance than the traditional Back Propagation (BP) in accuracy, stability, and efficiency. [ABSTRACT FROM AUTHOR]

Golden jackal optimization (GJO) is an effective metaheuristic algorithm that imitates the cooperative hunting behavior of the golden jackal. However, since the update of the prey's position often depends on the male golden jackal and there is insufficient diversity of golden jackals in some cases, it is prone to falling into a local optimal optimum. In order to address these drawbacks of GJO, this paper proposes an improved algorithm, called a hybrid GJO and golden sine (S) algorithm (Gold-SA) with dynamic lens-imaging (L) learning (LSGJO). First, this paper proposes novel dual golden spiral update rules inspired by Gold-SA. These rules give GJO the ability to think like a human (Gold-SA), making the golden jackal more intelligent in the process of preying, and improving the ability and efficiency of optimization. Second, a novel nonlinear dynamic decreasing scaling factor is introduced into the lens-imaging learning operator to maintain the population diversity. The performance of LSGJO is verified through 23 classical benchmark functions and 3 complex design problems in real scenarios. The experimental results show that LSGJO converges faster and more accurately than 11 state-of-the-art optimization algorithms, the global and local search ability has improved significantly, and the proposed algorithm has shown superior performance in solving constrained problems. [ABSTRACT FROM AUTHOR]

Test case generation and optimization is the foremost requirement of software evolution and test automation. In this paper, a bio-inspired Crow Search Algorithm (CSA) is suggested with an improved objective function to fulfill this requirement. CSA is a nature-inspired optimization method. The improved objective function combines branch distance and predicate distance to cover the critical path on the control flow graph. CSA is a search-based technique that uses heuristic information for automation testing, and CSA optimizers minimize test cases generated by satisfying the objective function. This paper focuses on generating test cases for all paths, including critical paths. The control flow graph covers the information flow among all the classes, functions, and conditional statements and provides test paths. The number of test cases examined through graph path coverage analysis. The minimum number of test paths is counted through complexity metrics using the cyclomatic complexity of the constructed graph. The proposed method is evaluated as mathematical optimization functions to validate their effectiveness in locating optimal solutions. The python codes are considered for evaluation and revealed that our approach is time-efficient and outperforms various optimization algorithms. The proposed approach achieved 100% path coverage, and the algorithm executes and gives optimum results in approximately 0.2745 seconds. [ABSTRACT FROM AUTHOR]

Numerous research based on offline Tamil recognition deals only with few Tamil characters since it becomes extremely complicated in distinguishing small variations in large handwritten document. The writer's complexity affects the overall formation of the characters. Such types of complexities are due to discontinuation of structures, unnecessary over loops, variation in shapes as well as irregular curves. This complex issue results in enhanced error value rate. Therefore, to conquer such issues, this paper proposes a novel approach to enhance the offline Tamil handwritten character recognition by utilizing four principal steps: pre-processing, segmentation, feature extraction and classification. For optimal segmentation of Tamil characters, this paper utilizes the Tsallis entropy approach-based atom search (TEAS) optimization algorithm. Then a Newton algorithm based deep convolution extreme learning (DELM) approach is utilized for the extraction and classification of input images. Finally, experiments are carried out for numerous Tamil handwritten recognition-based approaches. The proposed Tamil character recognition utilizes the datasets of isolated Tamil handwritten characters established by HP lab India to evaluate the efficiency of the system. [ABSTRACT FROM AUTHOR]