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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]

Thus, (3) treats the constraints HT ht and HT ht implicitly, and works solely with the auxiliary support variables I y i . [Extracted from the article]

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]

Abstract: In this paper, we focus on the proposed algorithms to solve a linear programming problem with the convex combination of the max–min and the max–average composition and the max–star composition, respectively. They have been proposed by Ghodousian and Khorram [A. Ghodousian, E. Khorram, Solving a linear programming problem with the convex combination of the max–min and the max–average fuzzy relation equations, Appl. Math. Comput. 180 (2006) 411–418] and Khorram et al. [E. Khorram, A. Ghodousian, A. Abbasi Molai, Solving linear optimization problems with max–star composition equation constraints, Appl. Math. Comput. 179 (2006) 654–661], respectively. Firstly, we show that the “Tabular method algorithm” in the first paper and the “First procedure” in the second paper may not lead to the optimal solutions of the two models in some cases. Secondly, we generalize the proposed algorithm by Abbasi Molai and Khorram [A. Abbasi Molai, E. Khorram, A modified algorithm for solving the proposed models by Ghodousian and Khorram and Khorram and Ghodousian, Appl. Math. Comput. 190 (2007) 1161–1167] to solve the two models. In fact, it modifies the presented algorithms in the two papers. Finally, some numerical examples are given to illustrate the purposes. [Copyright &y& Elsevier]

SUMMARY The present study deals with a comprehensive thermodynamic modeling of a combined heat and power (CHP) system in a paper mill, which provides 50 MW of electric power and 100 ton h−1 saturated steam at 13 bars. This CHP plant is composed of air compressor, combustion chamber (CC), Air Preheater, Gas Turbine (GT) and a Heat Recovery Heat Exchanger. The design parameters of this cycle are compressor pressure ratio ( rAC), compressor isentropic efficiency ( ηAC), GT isentropic efficiency ( ηGT), CC inlet temperature ( T3), and turbine inlet temperature ( T4). In the multi-objective optimization three objective functions, including CHP exergy efficiency, total cost rate of the system products, and CO2 emission of the whole plant, are considered. The exergoenvironmental objective function is minimized whereas power plant exergy efficiency is maximized using a Genetic algorithm. To have a good insight into this study, a sensitivity analysis of the results to the interest rate as well as fuel cost is performed. The results show that at the lower exergetic efficiency, in which the weight of exergoenvironmental objective is higher, the sensitivity of the optimal solutions to the fuel cost is much higher than the location of the Pareto Frontier with the lower weight of exergoenvironmental objective. In addition, with increasing exergy efficiency, the purchase cost of equipment in the plant is increased as the cost rate of the plant increases. Copyright © 2010 John Wiley & Sons, Ltd. [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]

Abstract: The aim of this note is to show that the optimization algorithm proposed in may not lead to the optimal solution in some cases. In fact, the optimization problem remains open if we do not apply the branch-and-bound method to solve it or compute all the objective values of minimal solutions of the feasible domain. [Copyright &y& Elsevier]

Nonconvex constraints are valuable regularizers in many optimization problems. In particular, sparsity constraints have had a significant impact on sampling theory, where they are used in compressed sensing and allow structured signals to be sampled far below the rate traditionally prescribed. Nearly, all of the theory developed for compressed sensing signal recovery assumes that samples are taken using linear measurements. In this paper, we instead address the compressed sensing recovery problem in a setting where the observations are nonlinear. We show that, under conditions similar to those required in the linear setting, the iterative hard thresholding algorithm can be used to accurately recover sparse or structured signals from few nonlinear observations. Similar ideas can also be developed in a more general nonlinear optimization framework. In the second part of this paper, we therefore present related result that shows how this can be done under sparsity and union of subspaces constraints, whenever a generalization of the restricted isometry property traditionally imposed on the compressed sensing system holds. [ABSTRACT FROM PUBLISHER]

Data clustering is one of the branches of unsupervised learning and it is a process whereby the samples are divided into categories whose members are similar to each other. The K-means algorithm is a simple and fast clustering technique, but it has many initial problems, for example, it depends heavily on the initial value for better clustering. Moreover, it is susceptible to outliers and unbalanced clusters. The artificial bee colony (ABC) algorithm is one of the meta-heuristic algorithms that is used nowadays to solve many optimization problems including clustering and the fundamental problem of this algorithm is exploration and late convergence. In this paper, to solve the problem of exploration and late convergence in ABC are used Random Memory (RM) and Elite Memory (EM) called ABCWOA algorithm. RM in the ABCWOA algorithm has used the search stage for the bait in the whale optimization algorithm (WOA) and EM is also used to increase convergence. In addition, we control the use of EM dynamically. Finally, the proposed method was implemented on ten standard datasets from the UCI Machine Learning Database for evaluation. Moreover, it was compared in terms of statistical criteria and analysis of variance (ANOVA) test with basic ABC and WOA, vortex search (VS) algorithm, butterfly optimization algorithm (BOA), crow search (CS) algorithm, and cuckoo search algorithm (CSA). The simulation results showed that the degree of convergence maintained its performance by increasing the number of repetitions of the proposed method, but the ABC algorithm has shown poor performance by increasing the repetition of performance. ANOVA results also confirmed that the ABCWOA algorithm has a positive effect on the population and it contains less noise than other comparative algorithms. The ABCWOA algorithm show that the ABCWOA algorithm performs better than other meta-heuristic algorithms. [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]

A general programming interface for parameter identification for marine ecosystem models is introduced. A comprehensive solver software for periodic steady-states is implemented that includes a fixed point iteration (spin-up) and a Newton solver. The software is based on the Portable, Extensible Toolkit for Scientific Computation (PETSc) library and uses transport matrices for efficient off-line simulation in 3-D. In addition to the usage of PETSc's parallel data structures and PETSc's Newton solver, an own load balancing algorithm is implemented. A simple verification is carried out using a well investigated biogeochemical model for phosphate (PO4) and dissolved organic phosphorous (DOP) with 7 parameters. The model is coupled via the interface to transport matrices that correspond to a longitudinal and latitudinal resolution of 2.8125° and 15 vertical layers. Initial tests show that both solvers and the load balancing algorithm work correctly. Further experiments demonstrate the robustness of the Newton solver with respect to parameter variations. Moreover, the numerical tests reveal that, with optimal control settings, the Newton solver converges at least 6 times faster towards a solution than the spin-up. However, additional twin experiments reveal differences between both solvers regarding a derivative-based black-box optimization. Whereas an optimization run with spin-up-based model evaluations is capable to identify model parameters of a refer ence solution, Newton-based model evaluations result in an inaccurate gradient approximation. [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]

This paper proposes a scheduling strategy for irregular curved blocks to address the complex spatiotemporal coupling scheduling problem related to the entered time, the entered sequence, the setting positions and the rotated angles for the curved blocks in a shipbuilding yard. The strategy presents a makespan-based curved blocks - classification and selection rule to fulfil the programming time for the entry of the curved blocks into the workplace and realises the suppression on the delay. Useless stepping search of curved blocks in occupied workplace is avoided by combining the lowest centre-of-gravity rule with the calculation method of the remained workplace proposed in this paper. A modified genetic ant colony algorithm was proposed, which apply the ease to premature characteristics of GA and the excellent local optimisation ability of ACO, to let and promote the algorithm falls into local optimum. Then the large-scale and full-range mutation will be implemented to make the algorithm jump out of the original local optimisation to search more local optimal solutions so that the global optimal solution can be achieved. Finally, a software system for algorithm verification was developed which conducts the comparative analysis of the algorithms and verifies the validity of the algorithm proposed. [ABSTRACT FROM AUTHOR]

Context: The recent research trend has highlighted that multiple stakeholders are involved during requirement gathering in agile software development. Hence, leading to an increased number of duplicate user stories in agile product backlog during requirement gathering. Objective: The objective of this paper is to evaluate the existing techniques employed in identifying and eliminating the duplicate user stories from agile product backlog and to overcome the existing gaps with the help of a newly proposed clustering algorithm. Method: An agile user story is expressed as a function of input and output parameters. That said multiple user stories having similar set of input parameters are most likely to be duplicate causing a redundancy. The newly proposed algorithm is used for clustering user stories having similar set of input parameters through various iterations and then removing the identified duplicate user stories from agile product backlog. This paper also introduces the concept of mass clustering which means clustering a number of user stories in single run. Results: Experimental results prove the proposed model is capable of handling small and large releases ranging between 100 to 1000 user stories with similar efficiency. The proposed clustering algorithm outperformed the clustering algorithms and resulted in 37% decrease in agile product backlog by eliminating duplicate user stories causing redundancy. The experimental results are obtained from the logs of the MATLAB tool. However, the provided algorithm is generic in nature and can be implemented using R, Python or SAS programming tools. The provided algorithms employs proven matrix operations. Conclusion: The proposed clustering algorithm overcomes the limitation of existing user story management methods and clearly out performs when compared with other clustering algorithms. Finally, this paper gives recommendations about the usage of the provided clustering algorithm during agile release planning for eliminating duplicate user stories from agile product backlog. [ABSTRACT FROM AUTHOR]

This paper presents an efficient method to improve the far-field radiation pattern of concentric circular antenna array (CCAA) design using two stochastic optimization algorithms known as social spider algorithm (SSA) and modified social spider algorithm (MSSA). Low side lobe level (SLL) plays a crucial role in reducing the interference with the other frequency components along the entire side lobes of the far-field radiation pattern. SSA and MSAA are the state-of-the-art evolutionary optimization techniques which are applied here to determine the optimal current amplitude and the inter-element distance between two consecutive antennae of the 3-ring CCAA. In this paper, the optimal results achieved by using SSA, MSSA for (4, 6, 8) elements and (8, 10, 12) elements 3-ring CCAAs, with and without centre elements are reported. The results achieved by employing SSA and MSSA show a considerable improvement in SLL reduction as compared to the uniform and the other array patterns reported in the state-of-the-art literature. [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]

Evolutionary algorithms rely on the population initialization strategy to determine a set of candidate solutions, which provide the preliminary knowledge of the problem landscape for the subsequent evolutionary process. However, the distribution of the initial population is rarely concerned in the multimodal evolutionary community. Moreover, a large initial population has not been attractive enough for evolutionary multimodal optimization, because it is difficult and challenging to achieve a balance between diversity and convergence within limited computing resources. As an extended version of our previous conference paper, this paper focuses on the construction of a large initial population that is beneficial for evolutionary multimodal optimization. First, we propose an improved density-based population initialization strategy to generate a uniform initial population with a certain degree of randomness. Then, after acquiring the raw fitness landscape of the problem, a fitness-weighted density-based population initialization strategy is proposed to explore potential global peaks. Finally, we design a multi-species cooperative coevolution algorithm for multimodal optimization, which balances the exploration and exploitation of such a population in the evolutionary process. Experimental results demonstrate that the proposed algorithm is better than the mainstream algorithms on the CEC2013 multimodal optimization benchmark. [ABSTRACT FROM AUTHOR]

In this paper, a new human resource scheduling algorithm is proposed based on the optimization simulation of the human resource scheduling algorithm to find the most suitable human resource allocation scheme for different regions. A simulation system for human resource allocation is proposed, which integrates the scheduling algorithm of this paper and conducts simulation experiments using the historical data of enterprise problems in each region collected in a smart city. The simulation experiment proves that the dispatching algorithm in this paper is more reasonable than the current dispatching algorithm, and the relationship between enterprise problems and the number of employees is also found, and finally, the simulation system in this paper is proved to be stable through large-scale simulation experiment. [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]

We introduce a new dynamic statistical optimization algorithm to initialize ionospherecorrected bending angles of Global Navigation Satellite System (GNSS) based radio occultation (RO) measurements. The new algorithm estimates background and obser-vation error covariance matrices with geographically-varying uncertainty profiles and realistic global-mean correlation matrices. The error covariance matrices estimated by the new approach are more accurate and realistic than in simplified existing approaches and can therefore be used in statistical optimization to provide optimal bending angle profiles for high-altitude initialization of the subsequent Abel transform re trieval of refractivity. The new algorithm is evaluated against the existing Wegener Center Occultation Processing System version 5.6 (OPSv5.6) algorithm, using simulated data on two test days from January and July 2008 and real observed CHAMP and COSMIC measurements from the complete months of January and July 2008. The following is achieved for the new method's performance compared to OPSv5.6: (1) significant 15 reduction in random errors (standard deviations) of optimized bending angles down to about two-thirds of their size or more; (2) reduction of the systematic differences in optimized bending angles for simulated MetOp data; (3) improved retrieval of refractivity and temperature profiles; (4) produces realistically estimated global-mean correlation matrices and realistic uncertainty fields for the background and observations. Over all the results indicate high suitability for employing the new dynamic approach in the processing of long-term RO data into a reference climate record, leading to well characterized and high-quality atmospheric profiles over the entire stratosphere. [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]

Entropy-regularized quadratic optimization problems are a special class of optimization problems with wide applications in various fields, such as transportation and machine learning. In this paper, we apply the augmented Lagrangian method to this problem with its subproblem solved by the block coordinate descent method. Under certain mild conditions, we analyze the global convergence of this algorithm. Numerical experiments demonstrate the effectiveness of this algorithm. [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]

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]

An introduction is presented in which the editor discusses reports within the issue on topics including the standard simplex for global optimization, the direct-type global optimization algorithms for expensive global optimization, and the multivariate McCormick relaxations.

In this paper, the most common pepper noise in grayscale image noise is investigated in depth in the median filtering algorithm, and the improved median filtering algorithm, adaptive switching median filtering algorithm, and adaptive polar median filtering algorithm are applied to the OTSU algorithm. Two improved OTSU algorithms such as the adaptive switched median filter-based OTSU algorithm and the polar adaptive median filter-based OTSU algorithm are obtained. The experimental results show that the algorithm can better cope with grayscale images contaminated by pretzel noise, and the segmented grayscale images are not only clear but also can better retain the detailed features of grayscale images. A genetic algorithm is a kind of search algorithm with high adaptive, fast operation speed, and good global space finding ability, and it will have a good effect when applied to the threshold finding of the OTSU algorithm. However, the traditional genetic algorithm will fall into the local optimal solution in different degrees when finding the optimal threshold. The advantages of the two interpolation methods proposed in this paper are that one is the edge grayscale image interpolation algorithm using OTSU threshold adaptive segmentation and the other is the edge grayscale image interpolation algorithm using local adaptive threshold segmentation, which can accurately divide the grayscale image region according to the characteristics of different grayscale images and effectively improve the loss of grayscale image edge detail information and jagged blur caused by the classical interpolation algorithm. The visual effect of grayscale images is enhanced by selecting grayscale images from the standard grayscale image test set and interpolating them with bilinear interpolation, bucolic interpolation, NEDI interpolation, and FEOI interpolation for interpolation simulation validation. The subjective evaluation and objective evaluation, as well as the running time, are compared, respectively, showing that the method of this paper can effectively improve the quality of grayscale image interpolation. [ABSTRACT FROM AUTHOR]

The multiverse optimizer (MVO) is one of the most trending algorithms used nowadays. The searching space in MVO is restricted by the best solution only, leading to a poor searching domain, therefore, a long searching time. This paper proposes an improved multiobjective multi-verse optimizer (IMOMVO) as a novel population optimization technique to solve task scheduling problems. The IMOMVO is introduced to overcome the drawbacks risen in the original MVO and its latest enhanced version mMVO. The proposed method solves the problem of the average positioning (AP) by dynamically enhancing the equation of updating the AP based on the best and the second-best available solutions. To evaluate The proposed IMOMVO, several datasets scenarios containing various tasks and virtual machines (Vms) were used to test the approach's capability. Standard evaluation metrics are used to validate the results of the proposed method; task execution time, throughput, and the Vms processing power. The proposed method obtained better results according to the evaluation measures than other state-of-the-art methods. The execution time achieves less time when compared to the mMVO as the proposed method achieved 186.33 s for executing 100 tasks and 934.92 for executing 600 tasks. The throughput results also achieved astonishing results as for 100 tasks, the throughput achieved 0.19, and the Vm processing power for the proposed method was 0.25 Kw for executing 100 tasks. [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]

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]

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]

Summary: Multi‐user multiple‐input multiple‐output (MU‐MIMO) system has the capability of delivering optimal system capacity that provides the simultaneous service to a large number of users using dirty paper coding (DPC) scheme. However, the DPC scheme is quite difficult to be implemented in the real‐time scenario as the computational complexity is very high and the process cannot be accomplished within the duration of few coherence periods. In this paper, we have adopted a newly developed binary spider monkey optimization (binary SMO) algorithm for the joint user and antenna scheduling (JUAS) problem to maximize the achievable system sum‐rate performance. It has been shown that JUAS with binary SMO achieves nearly 99% of system throughput achieved by the extensive search algorithm (ESA) using DPC. Also it is compared with binary flower pollination algorithm (binary FPA) to check which of the two gives better result. It is observed that the binary SMO in JUAS achieves a globally optimal solution quite rapidly, to remain well within the modern‐day packet data communication interval. The proposed binary SMO shows low computational complexity and computation time as compared to ESA (i.e., DPC). Also when it is compared with binary FPA, it shows quite a significant improvement in system throughput. Moreover, the effectiveness of binary SMO in the MU‐MIMO broadcasting scenario is verified through extensive simulation results. [ABSTRACT FROM AUTHOR]

Abstract: During a Wet End break, the loss of paper feed through the paper machine causes loss of sensory information and the remaining parts of the process are operated in open-loop. This causes the stock composition in the Headbox to deviate substantially from the nominal specifications, causing paper quality (after start up) and paper machine runability issues. In this work, the Base Sheet Ash measurement of the scanner is estimated using a least absolute value (LAV) model which can then be used for control of the chalk valve during the breaks to keep the Headbox Ash within specified limits. The model is computed using a very fast optimization algorithm which is able to compute the LAV solution using only basic elementary operations. The proposed approach has been developed for a UK paper mill. [Copyright &y& Elsevier]

This paper presents an improved arithmetic optimization algorithm that incorporates hybrid elite pool strategies to address the limitations of the arithmetic optimization algorithm (AOA). In AOA, the linear mathematical optimization acceleration (MOA) function cannot balance global exploitation and local exploration well. Therefore, the accuracy and convergence speed of the algorithm cannot be guaranteed. To improve the performance of AOA, this paper reconstructed a nonlinear MOA function, which is expected to balance the exploitation and the exploration of AOA. Furthermore, four hybrid elite pool strategies are integrated to enhance the ability to escape local optima. The proposed algorithm inherits the fast convergence of AOA and develops the performance of escaping local optima. Numerical experiment results on benchmark functions and engineering problems show that the proposed algorithm outperforms other compared meta-heuristic algorithms in terms of convergence speed and accuracy. [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]

The convergence rate has been widely accepted as a performance measure for choosing a better metaheuristic algorithm. So, we propose a novel technique to improve the performance of the existing Grey Wolf Optimization (GWO) algorithm in terms of its convergence rate. The proposed approach also prioritizes the test cases that are obtained after executing the input benchmark programs. This paper has three technical contributions. In our first contribution, we generate test cases for the input benchmark programs. Our second contribution prioritizes test cases using an improved version of the existing GWO algorithm (CGWO). Our third contribution analyzes the obtained result and compares it with state-of-the-art metaheuristic techniques. This work is validated after running the proposed model on six benchmark programs. The obtained results show that our proposed approach has achieved 48% better APFD score for the prioritized order of test cases than the non-prioritized order. We also achieved a better convergence rate, which takes around 4000 fewer iterations, when compared with the existing methods on the same platform. [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]

This paper presents an optimization method for selective harmonic elimination in a cascaded multilevel inverter (MLI) using teaching-learning based optimization (TLBO). The main objective in selective harmonic elimination (SHE) strategy is to eliminate low-order harmonics by solving nonlinear equations and reaching optimal solution, while the fundamental component is satisfied. In this paper, different optimization methods are compared. In this paper, the TLBO as a recently emerged nature-inspired algorithm is presented to provide better results for the SHE in comparison with Genetic algorithm (GA), Particle swarm optimization (PSO), Newton Raphson Method (NR) and Satin Bowerbird algorithm (SBO). For better comparison of those methods and influence of optimal DC source a 7-level inverter is chosen and MATLAB/Simulink software is used for optimization. Simulation results show the superiority of TLBO, higher precision and probability of convergence than other mentioned algorithms. [ABSTRACT FROM AUTHOR]

Exponentially weighted moving average (EWMA) control charts are well-established devices for monitoring process stability. Typically, control charts are evaluated by considering their Average Run Length (ARL), that is the expected number of observations or samples until the chart signals. Because of the limitations of an average, various papers also dealt with the run length distribution and quantiles. Going beyond these papers, we develop algorithms for and evaluate the quantile performance of EWMA control charts with variance adjusted control limits and with fast initial response features, of EWMA charts based on the sample variance, and of EWMA charts simultaneously monitoring mean and variance. Additionally, for the mean charts we consider medium, late and very late process changes and their impact on appropriately conditioned run length quantiles. It is demonstrated that considering run length quantiles can protect from constructing distorted EWMA designs while optimising theirzero-stateARL performance. The implementation of all the considered measures in the R package ‘spc’ allows any control chart user to consider EWMA schemes from the run length quantile prospective in an easy way. [ABSTRACT FROM PUBLISHER]

Multilevel image thresholding is a well-known technique for image segmentation. Recently, various metaheuristic methods have been proposed for the determination of the thresholds for multilevel image segmentation. These methods are mainly based on metaphors and they have high complexity and their convergences are comparably slow. In this paper, a multilevel image thresholding approach is proposed that simplifies the thresholding problem by using a simple optimization technique instead of metaphor-based algorithms. More specifically, in this paper, Chaotic enhanced Rao (CER) algorithms are developed where eight chaotic maps namely Logistic, Sine, Sinusoidal, Gauss, Circle, Chebyshev, Singer, and Tent are used. Besides, in the developed CER algorithm, the number of thresholds is determined automatically, instead of manual determination. The performances of the developed CER algorithms are evaluated based on different statistical analysis metrics namely BDE, PRI, VOI, GCE, SSIM, FSIM, RMSE, PSNR, NK, AD, SC, MD, and NAE. The experimental works and the related evaluations are carried out on the BSDS300 dataset. The obtained experimental results demonstrate that the proposed CER algorithm outperforms the compared methods based on PRI, SSIM, FSIM, PSNR, RMSE, AD, and NAE metrics. In addition, the proposed method provides better convergence regarding speed and accuracy. [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]

Many researchers witness the efficiency of decomposition-based evolutionary multi- and many-objective optimization algorithms in finding quality solutions on problems with regular Pareto front. These decomposition-based evolutionary approaches profoundly depend on aggregation methods and weight vectors. Such algorithms fail to guarantee the diversity in the population, especially when dealing with irregular Pareto front, because some of the weight vectors become ineffective during simulation. Efforts are being made over time to frequently adopt the position of weight vectors at the rate of increased computation burden, in order to achieve improved solutions. Hence an alternative mechanism has been suggested in this paper. Instead of changing the weight vector, a better treatment on environmental selection is proposed that could effectively balance both convergence and diversity in solving both multi- and many-objective optimization problems. The higher rate of performance of the proposed algorithm has been validated over the comparative permanence with six popular state-of-the-art algorithms in terms of hypervolume, complemented with winning ratio using t-test. It is concluded in the latter part of this paper that the proposed method performs better while solving test suite problems with irregular Pareto fronts. Moreover, it is more prone towards better solutions with an increase in the number of objective functions in MaOPs. [ABSTRACT FROM AUTHOR]

The article focuses on network virtualization, a technique used to overcome the Internet's perceived ossification. It says that virtualization is used to combine and/or partition into virtual network resources the physical network resources. It says that the virtual networks' actual deployment needs the network operator to map onto physical resources the virtual resources. It mentions that the question on how to map optimally is usually called the Virtual Network Embedding (VNE) problem. It adds that several algorithms proposed for the solution of the problem concentrate on optimizing the application of resources in terms of performance.