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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]

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]

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]

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]

Tunicate Swarm Algorithm (TSA) is a new bio-based optimization technique that has proven not only to be able to compete with other methods but has also shown successful performance in classic design engineering problems/benchmark test problems. However, like some population-based methods, TSA tends to be trapped in local optima, converging to global optima in a long time, unbalanced exploitation/exploration, and the inability to effectively solve high-capacity engineering problems. In this paper, the M-TSA, which is a Modified version of the TSA, is proposed to overcome such problems. M-TSA was developed in three steps. The first is the new movement strategy that improves the movement of tunicates with a spiral movement, the second is the new herd strategy that improves the herd movement of tunics with the Levy movement, and the third is the consideration of the FAD effect. In this study, the efficiency and robustness of the M-TSA algorithm is tested on the CEC'17 test suite, six real-life design engineering problems, and two complex power system engineering problems. The test results were compared with other techniques reported in the literature and with the original TSA. Comparing the results from the M-TSA technique with other techniques proves the effectiveness of M-TSA with better exploration/exploitation balance and optimal solution finding. In this paper, MATLAB 2020b software is used for optimization problems simulation. [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.

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]

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]

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]

To solve the shortcomings that current robot vision system accuracy is instable, unit operation time is overlong and automatic compensation function is lacked, this paper proposes robot vision system based on trinocular vision from the perspective of system hardware structure improvement and software algorithm optimization. Firstly, logic communication channel among PLC sensor, robot and vision PC shall be established, to realize real-time acquisition and processing of operation condition of material. Then design nine-point calibration method according to scaling, rotating and translation parameters between robot and vision coordinate system, and calibrate three industrial cameras to realize accurate binding of image pixel coordinate and robot world coordinate. Finally, choose region-of-interest, optimize the minimum mean square error matching algorithm and realize balance of system accuracy and speed according to project standard of product. Experimental test result shows that compared with current robot vision system, system in this paper has higher vision accuracy and operation efficiency. [ABSTRACT FROM AUTHOR]

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]

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]

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]

The main objective of this paper is to present a new 2-stage hybrid optimization algorithm based scheme named PSO-BHA for image enhancement. A parameterized mapping function and a novel objective function are utilized in this paper to achieve the best-enhanced images. The suggested scheme combines the merits of particle swarm optimization (PSO) with the black hole algorithm (BHA) in two sequential stages to find the best parameters for the mapping function with the aid of the proposed objective function. The objective function uses contrast, edge, entropy, and universal quality index (UQI) for measuring contrast, and different improved information in the enhanced image. In the proposed scheme, PSO is applied first to adjust the tunable parameters of the mapping function and as a result, new pixel intensities are produced. Then, in the second stage, the obtained pixel intensities are again passed through the mapping function whose parameters are tuned by the use of the BHA. The suggested framework overcomes the limitations of the traditional histogram equalization (HE) based enhancement techniques in which excessive contrast enhancement and image information loss can occur. The suggested method is evaluated on several test images and compared with different state-of-the-art methods. The results indicate that the proposed framework provides superior performance to all existing methods in terms of various metrics. The proposed scheme also contributes to substantial feature enhancement and contrast boosting in the enhanced image, while retaining the natural feel of the original image. [ABSTRACT FROM AUTHOR]

Research on mobile robots began in the late 1960s. Mobile robots are a typical autonomous intelligent system and a hot spot in the high-tech field. They are the intersection of multiple technical disciplines such as computer artificial intelligence, robotics, control theory and electronic technology. The product not only has potentially very attractive application value and commercial value, but the research on it is also a challenge to intelligent technology. The development of mobile robots provides excellent research for various intelligent technologies and solutions. This dissertation aims to study the research of multi-sensor information fusion and intelligent optimization methods and the methods of applying them to mobile robot related technologies, and in-depth study of the construction of mobile robot maps from the perspective of multi-sensor information fusion. And, in order to achieve this function, combined with autonomous exploration and other related theories and algorithms, combined with the Robot Operating System (ROS). This paper proposes the area equalization method, equalization method, fuzzy neural network and other methods to promote the realization of related technologies. At the same time, this paper conducts simulation research based on the SLAM comprehensive experiment of the JNPF-4WD square mobile robot. On this basis, the high precision and high reliability of robot positioning are further realized. The experimental results in this paper show that the maximum error of the X-axis and Y-axis, FastSLAM algorithm is smaller than EKF algorithm, and the improved FASTSALM algorithm error is further reduced compared with the original FastSLAM algorithm, the value is less than 0.1. [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]

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]

The sparrow search algorithm (SSA) is an efficient swarm-intelligence-based algorithm and has been widely studied in recent years. Nevertheless, as with other swarm intelligence optimization approaches, the SSA is prone to fall into local solutions, which weakens the exploration ability. In order to cope with this problem, in this paper, a novel hierarchical SSA, named the HSSA, is proposed. Specifically, by introducing the virtual individual strategy in each iteration, each scrounger can obtain more effective guidance information in the developed HSSA, thus contributing to a thorough search in the entire problem space. On the other hand, the proposed hierarchical strategy is employed to realize the information interaction among individuals according to the division layers (including top, medium and bottom layers) with the purpose of enhancing the diversity of the original SSA. In addition, the life cycle mechanism is introduced in order to avoid the current scrounger individuals being trapped in local optima positions as much as possible, which can further improve the solution accuracy of the traditional SSA. The developed HSSA is verified on a series of benchmark functions (i.e., IEEE CEC2022) and the parameter optimization problem of the carbon fiber drawing process. The proposed HSSA is compared with three classes of existing swarm-intelligence-based approaches: (1) CLSSA, C-SSA, and CSSA as popular SSA-based methods; (2) BWO, HGS, GJO, and DBO as state-of-the-art optimization techniques; and (3) GWO, WOA, HHO, MPA, WSO, and POA as highly-cited swarm intelligence algorithms. Results demonstrate that the HSSA exhibits the best performance among twelve test functions and one practical application in terms of solving accuracy and convergence speed. Finally, the overall experimental results indicate that the developed HSSA is an effective method to deal with the defects of the basic SSA and can obtain satisfactory solutions for different optimization problems. [ABSTRACT FROM AUTHOR]

This paper proposes a parallel computing analysis model HPM and analyzes the parallel architecture of CPU–GPU based on this model. On this basis, we study the parallel optimization of the ray-tracing algorithm on the CPU–GPU parallel architecture and give full play to the parallelism between nodes, the parallelism of the multi-core CPU inside the node, and the parallelism of the GPU, which improve the calculation speed of the ray-tracing algorithm. This paper uses the space division technology to divide the ground data, constructs the KD-tree organization structure, and improves the construction method of KD-tree to reduce the time complexity of the algorithm. The ground data is evenly distributed to each computing node, and the computing nodes use a combination of CPU–GPU for parallel optimization. This method dramatically improves the drawing speed while ensuring the image quality and provides an effective means for quickly generating photorealistic images. [ABSTRACT FROM AUTHOR]

A key area of wireless communications is visible light communication which uses solid-state light-emitting-diode. This paper shows, a comparative study to optimize the received signal strength indication (RSSI) in the optical wireless link between two optimization algorithms. The optimization algorithm we used in the paper is a genetic algorithm (GA) and a particle swarm optimization (PSO) algorithm. To, compute the maximum RSSI over the non-line-of-sight (NLOS) link, within an indoor environment, and to find the optimum NLOS route through distinct angles to achieve better communication quality, we have used those algorithms. Simulations were performed at matlab to check the feasibility of the scheme proposed. Also, we have compared the efficiency of the both system. In addition, GA and PSO detect the maximum RSSI of the receiver (Rx) in a specific direction instead of calculating RSSI at any random angle. The simulation result shows that the proposed system precisely detects the direction that provides an optimal RSSI for the Rx. This paper concludes that the GA is computationally effective while compared with PSO. After several iterations, the result estimates that the average elapsed time of the GA is 0.21 ms and the PSO is 30 ms, respectively. In the case of user positions 1 and 2, the percentage enhancement for GA as compared to PSO is almost 4.2% and 12.5% respectively. [ABSTRACT FROM AUTHOR]

Due to the characteristics of time-varying traffic and nonlinearity, the short-term traffic flow data are difficult to predict accurately. The purpose of this paper is to improve the short-term traffic flow prediction accuracy through the proposed improved wavelet neural network prediction model and provide basic data and decision support for the intelligent traffic management system. In view of the extremely strong nonlinear processing power, self-organization, self-adaptation and learning ability of wavelet neural network (WNN), this paper uses it as the basic prediction model and uses the particle swarm optimization algorithm for the slow convergence rate and local optimal problem of WNN prediction algorithm. With the advantages of fast convergence, high robustness and strong global search ability, an improved particle swarm optimization algorithm is proposed to optimize the wavelet neural network prediction model. The improved wavelet neural network is used to predict short-term traffic flow. The experimental results show that the proposed algorithm is more efficient than the WNN and PSO–WNN algorithms alone. The prediction results are more stable and more accurate. Compared with the traditional wavelet neural network, the error is reduced by 14.994%. [ABSTRACT FROM AUTHOR]

This paper focuses on developing an improved multi-focus image fusion (MFIF) algorithm. Existing spatial domain algorithms dependent on the obtained fusion decision map still lead to unexpected ghosting, blurred, edges as well as blocking effects such that the visual effect of image fusion is seriously degraded. To overcome these shortages, an improved MFIF algorithm is developed with the help of a novel multi-scale weighted focus measure and a decision map optimization technique. First, a novel multi-scale measurement template is designed in order to effectively extract the gradient information of rich texture regions, smooth regions as well as transitional regions between the aforementioned regions simultaneously. Then, an improved calculation scheme of the focus score matrix is designed based on the weighted sum of the focus measure maps in each region window centered on a concerned pixel, under which the advantage of pixel-by-pixel weighting is employed. In what follows, an initial decision map is obtained in light of the focus score matrix combined with threshold filtering, which is employed to eliminate the small isolated regions caused by some misclassified pixels. Furthermore, an accurate decision map is received with the help of the optimization capability of guided filtering to avoid edge unexpected artificial textures. In comparison with block-based fusion algorithms, our algorithm developed in this paper extracts the focus regions pixel-by-pixel, thereby helping to reduce the blocking effects that appear in the fusion image. Finally, some intensive comparison analysis based on common datasets is performed to verify the superiority over state-of-the-art methods in both visual qualitative and quantitative evaluations. [ABSTRACT FROM AUTHOR]

A novel method for economic load dispatch problem (ELDP) based on improved whale optimization algorithm(IWOA) is presented, and the optimization performance of IWOA in ELDP was evaluated comprehensively. The search mechanism is modified to improve the ability of the algorithm to jump out of the local optimal. The adaptive nonlinear inertia weight is introduced to improve the convergence speed of the algorithm. A limited mutation mechanism is proposed to improve the convergence of the algorithm. The evaluation indicator of calculation time and calculation accuracy was established. Taking 26 units of the Three Gorges Hydropower Station as an example, limited adaptive genetic aigorithm (LAGA), particle swarm optimization (PSO), whale optimization algorithm (WOA) and improved whale optimization algorithm (IWOA) were used to solve ELDP. The result shows that IWOA is superior to other algorithms in calculation results of various heads and loads. The calculation accuracy of IWOA was better than WOA when the number of units turned on was more than 6. The analysis results of IWOA and DP show that the calculation time of IWOA is better than that of DP when the number of units turned on is more than 6. The IWOA and the evaluation indicators proposed in this paper provide a new way for solving ELDP of large hydropower stations. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to present an improved neural network algorithm (INNA) for solving the reliability-redundancy allocation problem (RRAP) with nonlinear resource constraints. In this RRAP, both the component reliability and the redundancy allocation are to be considered simultaneously. Neural network algorithm (NNA) is one of the newest and efficient swarm optimization algorithms having a strong global search ability that is very adequate in solving different kinds of complex optimization problems. Despite its efficiency, NNA experiences poor exploitation, which causes slow convergence and also restricts its practical application of solving optimization problems. Considering this deficiency and to obtain a better balance between exploration and exploitation, searching procedure for NNA is reconstructed by implementing a new logarithmic spiral search operator and the searching strategy of the learner phase of teaching–learning-based optimization (TLBO) and an improved NNA has been developed in this paper. To demonstrate the performance of INNA, it is evaluated against seven well-known reliability optimization problems and finally compared with other existing meta-heuristics algorithms. Additionally, the INNA results are statistically investigated with the Wilcoxon sign-rank test and Multiple comparison test to show the significance of the results. Experimental results reveal that the proposed algorithm is highly competitive and performs better than previously developed algorithms in the literature. [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 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]

Human facial analysis (HFA) has recently become an attractive topic for computer vision research due to technological progress and mobile applications. HFA explores several issues as gender recognition (GR), facial expression, age, and race recognition for automatically understanding social life. This study explores HFA from the angle of recognizing a person's gender from their face. Several hard challenges are provoked, such as illumination, occlusion, facial emotions, quality, and angle of capture by cameras, making gender recognition more difficult for machines. The Archimedes optimization algorithm (AOA) was recently designed as a metaheuristic-based population optimization method, inspired by the Archimedes theory's physical notion. Compared to other swarm algorithms in the realm of optimization, this method promotes a good balance between exploration and exploitation. The convergence area is increased By incorporating extra data into the solution, such as volume and density. Because of the preceding benefits of AOA and the fact that it has not been used to choose the best area of the face, we propose utilizing a wrapper feature selection technique, which is a real motivation in the field of computer vision and machine learning. The paper's primary purpose is to automatically determine the optimal face area using AOA to recognize the gender of a human person categorized by two classes (Men and women). In this paper, the facial image is divided into several subregions (blocks), where each area provides a vector of characteristics using one method from handcrafted techniques as the local binary pattern (LBP), histogram-oriented gradient (HOG), or gray-level co-occurrence matrix (GLCM). Two experiments assess the proposed method (AOA): The first employs two benchmarking datasets: the Georgia Tech Face dataset (GT) and the Brazilian FEI dataset. The second experiment represents a more challenging large dataset that uses Gallagher's uncontrolled dataset. The experimental results show the good performance of AOA compared to other recent and competitive optimizers for all datasets. In terms of accuracy, the AOA-based LBP outperforms the state-of-the-art deep convolutional neural network (CNN) with 96.08% for the Gallagher's dataset. [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]

In this paper, to keep the researchers interested in nature-inspired algorithms and optimization problems, a comprehensive survey of the group search optimizer (GSO) algorithm is introduced with detailed discussions. GSO is a nature-inspired optimization algorithm introduced by He et al. (IEEE Trans Evol Comput 13:973–990, 2009) to solve several different optimization problems. It is inspired by animal searching behavior in real life. This survey focuses on the applications of the GSO algorithm and its variants and results from the year of its suggestion (2009) to now (2020). GSO algorithm is used to discover the best solution over a set of candidate solution to solve any optimization problem by determining the minimum or maximum objective function for a specific problem. Meta-heuristic optimizations, nature-inspired algorithms, have become an interesting area because of their rule in solving various decision-making problems. The general procedures of the GSO algorithm are explained alongside with the algorithm variants such as basic versions, discrete versions, and modified versions. Moreover, the applications of the GSO algorithm are given in detail such as benchmark function, classification, networking, engineering, and other problems. Finally, according to the analyzed papers published in the literature by the all publishers such as IEEE, Elsevier, and Springer, the GSO algorithm is mostly used in solving various optimization problems. In addition, it got comparative and promising results compared to other similar published optimization algorithm. [ABSTRACT FROM AUTHOR]

High-dimensional imbalanced biomedical data has dual characteristics of high-dimensional and imbalanced distribution. It is important to improve classification accuracy by filtering out low-dimensional feature subsets that are highly correlated with the classification target and have minimal mutual redundancy. However, traditional feature selection algorithms tend to select the feature subset that is favorable to class with large sample size, resulting in poor classification performance for minority samples. In response to the above problems, the P-AdaBoost-PAUC algorithm is proposed to be applied to high-dimensional imbalanced biomedical data classification. The idea of P-AdaBoost-PAUC algorithm has two major contributions. The first is that an improved decision tree attribute optimization algorithm (DT-P) is proposed, which pays more attention to the correlation among attributes. The second is that an improved AdaBoost algorithm based on probabilistic AUC (AdaBoost-PAUC) is proposed, which comprehensively considers misclassification probability and AUC to pay more attention to minority samples. An ensemble algorithm for high-dimensional imbalanced biomedical data classification is formed, which is conducive to improve classification performance. Experimental results show that Recall, Specificity, F1, and AUC values of P-AdaBoost-PAUC ensemble algorithm have reached the highest values on datasets with different imbalance rate. Especially when the proportion of minority samples is only 12.6 % , Recall, Specificity, F1 and AUC values all reached above 0.95. And algorithm stability experiments show that P-AdaBoost-PAUC algorithm is more stable than other algorithms. Therefore, the P-AdaBoost-PAUC ensemble algorithm proposed in this paper improves classification performance of minority samples on high-dimensional imbalanced biomedical data to a certain extent. [ABSTRACT FROM AUTHOR]

The accurate model of the solar PV system is the principal organ that describes the performance of this resource. Several approaches based on optimizing algorithms were considered valuable tools to illustrate the I–V curve for improving the photovoltaic models. Their electrical parameters are estimated using optimization algorithms referring to the experimental database or manufacturer's datasheet. This paper proposes a novel developed a photovoltaic model based on improved arithmetic optimization algorithm (IAOA) to extract the solar cell parameters. Also, an experimental test bench is presented for obtaining the measured illustration of the I–V characteristics. Thus, the root mean square error value that describes the difference between measured and estimated results is considered the objective function for two different models, the simple-diode model and the one-diode model. The proposed IAOA results are compared with other research papers and optimization algorithms. Furthermore, the evaluation of the proposed IAOA has been discussed considering several statistical analysis tests. The presented results show that the effectiveness and accuracy of IAOA results are excellent, and their I–V characteristics coincide with experimental data. Moreover, the results obtained by the proposed algorithm show its high superiority in optimizing the solar cell parameters under a variety of operating conditions. [ABSTRACT FROM AUTHOR]

This paper considers a stochastic optimization problem over the fixed point sets of quasinonexpansive mappings on Riemannian manifolds. The problem enables us to consider Riemannian hierarchical optimization problems over complicated sets, such as the intersection of many closed convex sets, the set of all minimizers of a nonsmooth convex function, and the intersection of sublevel sets of nonsmooth convex functions. We focus on adaptive learning rate optimization algorithms, which adapt step-sizes (referred to as learning rates in the machine learning field) to find optimal solutions quickly. We then propose a Riemannian stochastic fixed point optimization algorithm, which combines fixed point approximation methods on Riemannian manifolds with the adaptive learning rate optimization algorithms. We also give convergence analyses of the proposed algorithm for nonsmooth convex and smooth nonconvex optimization. The analysis results indicate that, with small constant step-sizes, the proposed algorithm approximates a solution to the problem. Consideration of the case in which step-size sequences are diminishing demonstrates that the proposed algorithm solves the problem with a guaranteed convergence rate. This paper also provides numerical comparisons that demonstrate the effectiveness of the proposed algorithms with formulas based on the adaptive learning rate optimization algorithms, such as Adam and AMSGrad. [ABSTRACT FROM AUTHOR]

This paper considers the task of finding the smallest circle into which one can pack a fixed number of non-overlapping unit squares that are free to rotate. Due to the rotation angles, the packing of unit squares into a container is considerably harder to solve than their circle packing counterparts. Therefore, optimal arrangements were so far proved to be optimal only for one or two unit squares. By a computer-assisted method based on interval arithmetic techniques, we solve the case of three squares and find rigorous enclosures for every optimal arrangement of this problem. We model the relation between the squares and the circle as a constraint satisfaction problem (CSP) and found every box that may contain a solution inside a given upper bound of the radius. Due to symmetries in the search domain, general purpose interval methods are far too slow to solve the CSP directly. To overcome this difficulty, we split the problem into a set of subproblems by systematically adding constraints to the center of each square. Our proof requires the solution of 6, 43 and 12 subproblems with 1, 2 and 3 unit squares respectively. In principle, the method proposed in this paper generalizes to any number of squares. [ABSTRACT FROM AUTHOR]

Assembly sequence planning is one of the key issues in DFA and computer-aided assembly process planning research for concurrent engineering. The purpose of this paper is to solve the problem of insufficient individual intelligence in evolutionary algorithms for assembly sequence planning, and a evolutionary algorithm for assembly sequence planning is designed. In this paper, the particle swarm optimization (PSO) algorithm is used to optimize the hybrid assembly sequence planning and assembly line balance problems. According to the assembly sequence problem, the number of assembly tool changes and the number of assembly orientation changes are transformed into the operation time of the assembly line. At the same time, the transportation of heavy parts in the assembly balance problem is considered. Then, by extracting the connection relationship and information of the parts, the disassembly method is used to inversely obtain the disassembly support matrix, and then, it is used to obtain the priority relationship diagram of the assembly operation tasks that indicate the order constraints of the job tasks on the assembly line. Aiming at the shortcoming that particle swarm optimization algorithm is easy to fall into local optimum, a various population strategy is adopted to shorten the evolution stagnation time, improve the evolution efficiency of particle swarm optimization algorithm, and enhance the optimization ability of the algorithm. Combined with the three evaluation indicators of assembly geometric feasibility, assembly process continuity, and assembly tool change times, a fitness function is constructed to achieve multi-objective optimization. Finally, experiments show that the multi-agent evolutionary algorithm is incorporated into the planning process to obtain an accurate solution through the various population strategy–particle swarm optimization algorithm, which proves the feasibility of the compound algorithm and has better performance in solving assembly sequence planning problems. [ABSTRACT FROM AUTHOR]

Recently, Drones and UAV research were becoming one of the interest topics for academia and industry, where it has been extensively addressed in the literature back the few years. Path planning of drones in an area with complex terrain or unknown environment and restricted by some obstacles is one of the most problems facing the operation of drones. The problem of path planning is not only limited to searching for an appropriate path from the starting point to the destination but also related to how to choose an ideal path among all available paths and provide a mechanism for collision avoidance. By considering how to construct the best path, several related issues need to be taken into account, that relate to safety, obstacle avoidance, response speed to overtake obstacles, etc. Swarm optimization algorithms have been used to provide intelligent modeling for drone path planning and enable to build the best path for each drone. This is done according to the planning and coordination dimensions among the swarm members. In this paper, we have discussed the features and characteristics of different swarm optimization algorithms such as ant colony optimization (ACO), fruit fly optimization algorithm (FOA), artificial bee colony (ABC), and particle swarm optimization (PSO). In addition, the paper provides a comprehensive summary related to the most important studies on drone path planning algorithms. We focused on analyzing the impact of the swarm algorithm and its performance in drone path planning. For that, the paper presented one of the most used algorithms and its models employed to improve the trajectory of drones that rely on swarm intelligence and its impact on the optimal path cost of drones. The results of performance analysis for the ACO algorithm in a 3D and 2D-dimensional environment are illustrated and discussed, and then the performance evaluation of the ACO is compared to the enhanced ACO algorithm. The proposed algorithm achieves fast convergence, accelerating the process of path planning. [ABSTRACT FROM AUTHOR]

In wireless sensor networks (WSN), broadcast authentication is an important security service that provides secure communication. Several mobile users are allowed by this broadcast authentication service. There are some concerns in the sensor network, which depend on security based on maintaining consumer untracking and privacy for data transmission. To overcome the concern of security issues, an Identity (ID)-based cryptography method is introduced. This paper presented a Pairing-Free Identity-based Digital Signature (PF-IBDS) Algorithm based on Modified Elliptic Curve Cryptography (MECC) by using Battle Royal Optimization Algorithm. The main aim of this paper is to secure the data transmission for message authentication. The proposed protocol is to enhance the speed of authentication, reduce the signature size and speed up the signature verification. Moreover, this paper analyses the security by BAN (Burrows–Abadi–Needham) logic and comparing with the existing protocols. It is verified that the developed protocol is protected and well-organized for peer-to-peer communications. Therefore, the proposed method offers secure key management, fast authentication, and also minimizes the computation overhead. The proposed authentication process is simulated in the Java platform. The performance of this protocol has compared with other existing approaches in terms of key verification time, key generation time and computational time. [ABSTRACT FROM AUTHOR]

In this paper we study nonconvex and nonsmooth multi-block optimization over Euclidean embedded (smooth) Riemannian submanifolds with coupled linear constraints. Such optimization problems naturally arise from machine learning, statistical learning, compressive sensing, image processing, and tensor PCA, among others. By utilizing the embedding structure, we develop an ADMM-like primal-dual approach based on decoupled solvable subroutines such as linearized proximal mappings, where the duality is with respect to the embedded Euclidean spaces. First, we introduce the optimality conditions for the afore-mentioned optimization models. Then, the notion of ϵ -stationary solutions is introduced as a result. The main part of the paper is to show that the proposed algorithms possess an iteration complexity of O (1 / ϵ 2) to reach an ϵ -stationary solution. For prohibitively large-size tensor or machine learning models, we present a sampling-based stochastic algorithm with the same iteration complexity bound in expectation. In case the subproblems are not analytically solvable, a feasible curvilinear line-search variant of the algorithm based on retraction operators is proposed. Finally, we show specifically how the algorithms can be implemented to solve a variety of practical problems such as the NP-hard maximum bisection problem, the ℓ q regularized sparse tensor principal component analysis and the community detection problem. Our preliminary numerical results show great potentials of the proposed methods. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editors discuss various reports within the issue on topics including algorithms for unconstrained optimization assuming that the objective function is differentiable but its derivatives are not available, bound constrained mixed-integer optimization problems, and an active set method for the minimization of a function subject to bound constraints.

APTEEN is a typical routing protocol for wireless sensor networks, but when clustering, cluster heads are randomly selected, which makes it easy to select nodes with low residual energy as cluster heads, thus forming network holes. At the same time, in multi hop transmission, the cluster head near the sink node is overloaded due to forwarding a large amount of data. Aiming at these problems, this paper introduces PSO algorithm and proposes ADCH-EPE-APTEEN routing protocol. In order to make the particle swarm optimization algorithm more suitable for routing protocol, this paper first proposes DCA-PSO based on particle swarm optimization algorithm. DCA-PSO uses the classification adaptive change inertia weight for different states of particles in the optimization process. At the same time, dynamic learning factor is used to improve the influence of particle experience and other particle experience on convergence speed and to improve the optimization accuracy and speed of the algorithm. Secondly, when APTEEN routing protocol is networked, the cluster head is selected by DCA-PSO, in which the residual energy of nodes, the location of nodes, and the energy distribution around nodes are fully taken into consideration. Thirdly, considering the distance between nodes and the sink node, the residual energy and the energy distribution around nodes, a front area is set up in the cluster located in the upper half and the assistant cluster head is selected in the front area through the DCA-PSO algorithm. The simulation results show that the DCA-PSO algorithm can significantly improve the search speed and precision compared with the PSO algorithm. Compared with APTEEN routing protocol, the ADCH-EPE-APTEEN routing protocol can reduce the network energy consumption rate and extend network lifetime by 173%. [ABSTRACT FROM AUTHOR]

Minimizing total harmonic distortion (THD) with less system complexity and computation time is a stringent constraint for many power systems. The multilevel inverter can have low THD when switching angles are selected at the fundamental frequency. For low-order harmonic minimization, selective harmonic elimination (SHE) is the most adopted and proficient technique but it involves the non-linear transcendental equations which are very difficult to solve analytically and numerically. This paper proposes a genetic algorithm (GA)-based optimization technique to minimize the THD of cascaded H-bridge multilevel inverter. The GA is the finest approach for solving such complex equations by obtaining optimized switching angles. The switching angles are calculated by the genetic algorithm by solving the nonlinear transcendental equations. This paper has modeled and simulated a five-level inverter in MATLAB Simulink. The THD comparison is carried out between step modulation method and optimization method. The results reveal that THD has been reduced from 17.88 to 16.74% while third and fifth harmonics have been reduced from 3.24%, 3.7% to 0.84% and 3.3%, respectively. The optimization method along with LC filter significantly improves the power quality providing a complete sinusoidal signal for varying load. [ABSTRACT FROM AUTHOR]

In recent years, new meta-heuristic algorithms have been developed to solve optimization problems. Recently-introduced Cuckoo Optimization Algorithm (COA) has proven its excellent performance to solve different optimization problems. Precedence Constrained Sequencing Problem (PCSP) is related to locating the optimal sequence with the shortest traveling time among all feasible sequences. The problem is motivated by applications in networks, scheduling, project management, logistics, assembly flow and routing. Regarding numerous practical applications of PCSP, it can be asserted that PCSP is a useful tool for a variety of industrial planning and scheduling problems. However it can also be seen that the most approaches may not solve various types of PCSPs and in related papers considering definite conditions, a model is determined and solved. In this paper a new approach is presented for solving various types of PCSPs based on COA. Since COA at first was introduced to solve continuous optimization problems, in order to demonstrate the application of COA to find the optimal sequence of the PCSP, some proposed schemes have been applied in this paper with modifications in operators of the basic COA. In fact due to the discrete nature and characteristics of the PCSP, the basic COA should be modified to solve PSCPs. To evaluate the performance of the proposed algorithm, at first, an applied single machine scheduling problem from the literature that can be formulated as a PCSP and has optimal solution is described and solved. Then, several PCSP instances with different sizes from the literature that do not have optimal solutions are solved and results are compared to the algorithms of the literature. Computational results show that the proposed algorithm has better performance compared to presented well-known meta-heuristic algorithms presented to solve various types of PCSPs so far. [ABSTRACT FROM AUTHOR]

Because of the low accuracy in intrusion detection, a model of extreme learning machine based on the optimization of quantum beetle swarm algorithm is proposed. First of all, this paper proposes a quantum beetle swarm optimization algorithm, which introduces quantum mechanics and combines the advantages of beetle antennae search and particle swarm optimization. In this way, the individual can learn both their own experience and group experience, which enables the beetle to move purposefully and instructively, and improves the convergence performance of the algorithm. In extreme learning machine, it is more difficult to solve the problem in high-dimensional data. This paper proposed an improved extreme learning machine that uses the least squares QR algorithm to decompose the matrix, which can reduce the computational complexity of the traditional extreme learning machine. The improved extreme learning machine model optimized by quantum beetle swarm optimization algorithm is applied to intrusion detection, and the simulation results show that the model proposed in this paper can significantly improve detection accuracy and increase convergence rate. [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]

A lot of researchers try to use the conditional convexity to develop their solution procedure to locate the optimal solution. However, this paper indicates that the solution procedure to locate the optimal solution based on the conditional convexity has logical shortcomings from a mathematics point of view. So, the main purpose of this paper will provide the alternative approach to develop a recursive algorithm for determining the optimal solution without using the conditional convexity. Finally, this paper improves many existing articles. [ABSTRACT FROM AUTHOR]

Nonnegative tensor decomposition is a versatile tool for multiway data analysis, by which the extracted components are nonnegative and usually sparse. Nevertheless, the sparsity is only a side effect and cannot be explicitly controlled without additional regularization. In this paper, we investigated the nonnegative CANDECOMP/PARAFAC (NCP) decomposition with the sparse regularization item using l 1 -norm (sparse NCP). When high sparsity is imposed, the factor matrices will contain more zero components and will not be of full column rank. Thus, the sparse NCP is prone to rank deficiency, and the algorithms of sparse NCP may not converge. In this paper, we proposed a novel model of sparse NCP with the proximal algorithm. The subproblems in the new model are strongly convex in the block coordinate descent (BCD) framework. Therefore, the new sparse NCP provides a full column rank condition and guarantees to converge to a stationary point. In addition, we proposed an inexact BCD scheme for sparse NCP, where each subproblem is updated multiple times to speed up the computation. In order to prove the effectiveness and efficiency of the sparse NCP with the proximal algorithm, we employed two optimization algorithms to solve the model, including inexact alternating nonnegative quadratic programming and inexact hierarchical alternating least squares. We evaluated the proposed sparse NCP methods by experiments on synthetic, real-world, small-scale, and large-scale tensor data. The experimental results demonstrate that our proposed algorithms can efficiently impose sparsity on factor matrices, extract meaningful sparse components, and outperform state-of-the-art methods. [ABSTRACT FROM AUTHOR]

With the rapid development of computer hardware in the past three decades, various classic algorithms such as neural computing and bionic optimization computing have been widely used in practical problems. This paper extended the new bionic algorithm-flock algorithm proposed in 2014 and obtained a multi-objective flock algorithm to solve the multi-objective problem. This study used aggregate functions to define social ranks, and simulated the foraging behavior of chickens in the process of searching for food in the objective space and found the balance between diversity and convergence when looking for the best Pareto solution. The algorithm took five types of bi-objective functions and four types of three-objective functions as objects and compared it with four more widely used algorithms in multi-objective problems. The results demonstrate that the MOCSO (multi-objective chicken swarm optimization) algorithm shows better results in the optimization of multi-objective problems. [ABSTRACT FROM AUTHOR]

Deep neural networks (DNNs) have evolved as a beneficial machine learning method that has been successfully used in various applications. Currently, DNN is a superior technique of extracting information from massive sets of data in a self-organized method. DNNs have different structures and parameters, which are usually produced for particular applications. Nevertheless, the training procedures of DNNs can be protracted depending on the given application and the size of the training set. Further, determining the most precise and practical structure of a deep learning method in a reasonable time is a possible problem related to this procedure. Meta-heuristics techniques, such as swarm intelligence (SI) and evolutionary computing (EC), represent optimization frames with specific theories and objective functions. These methods are adjustable and have been demonstrated their effectiveness in various applications; hence, they can optimize the DNNs models. This paper presents a comprehensive survey of the recent optimization methods (i.e., SI and EC) employed to enhance DNNs performance on various tasks. This paper also analyzes the importance of optimization methods in generating the optimal hyper-parameters and structures of DNNs in taking into consideration massive-scale data. Finally, several potential directions that still need improvements and open problems in evolutionary DNNs are identified. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) are considered a promising example of an automatic emergency task in a dynamic marine environment. However, the maritime communication performance between UAVs and offshore platforms has become a severe challenge. Due to the complex marine environment, the task allocation and route planning efficiency of multiple UAVs in an intelligent ocean are not satisfactory. To address these challenges, this paper proposes an intelligent marine task allocation and route planning scheme for multiple UAVs based on improved particle swarm optimization combined with a genetic algorithm (GA-PSO). Based on the simulation of an intelligent marine control system, the traditional particle swarm optimization (PSO) algorithm is improved by introducing partial matching crossover and secondary transposition mutation. The improved GA-PSO is used to solve the random task allocation problem of multiple UAVs and the two-dimensional route planning of a single UAV. The simulation results show that compared with the traditional scheme, the proposed scheme can significantly improve the task allocation efficiency, and the navigation path planned by the proposed scheme is also optimal. [ABSTRACT FROM AUTHOR]