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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]

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

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

Nowadays, imbalanced data classification is a hot topic in data mining and recently, several valuable researches have been conducted to overcome certain difficulties in the field. Moreover, those approaches, which are based on ensemble classifiers, have achieved reasonable results. Despite the success of these works, there are still many unsolved issues such as disregarding the importance of samples in balancing, determination of proper number of classifiers and optimizing weights of base classifiers in voting stage of ensemble methods. This paper intends to find an admissible solution for these challenges. The solution suggested in this paper applies the support vector data descriptor (SVDD) for sampling both minority and majority classes. After determining the optimal number of base classifiers, the selected samples are utilized to adjust base classifiers. Finally, genetic algorithm optimization is used in order to find the optimum weights of each base classifier in the voting stage. The proposed method is compared with some existing algorithms. The results of experiments confirm its effectiveness. [ABSTRACT FROM AUTHOR]

Threshold segmentation has been widely used in recent years due to its simplicity and efficiency. The method of segmenting images by the two-dimensional maximum entropy is a species of the useful technique of threshold segmentation. However, the efficiency and stability of this technique are still not ideal and the traditional search algorithm cannot meet the needs of engineering problems. To mitigate the above problem, swarm intelligent optimization algorithms have been employed in this field for searching the optimal threshold vector. An effective technique of lightning attachment procedure optimization (LAPO) algorithm based on a two-dimensional maximum entropy criterion is offered in this paper, and besides, a chaotic strategy is embedded into LAPO to develop a new algorithm named CLAPO. In order to confirm the benefits of the method proposed in this paper, the other seven kinds of competitive algorithms, such as Ant–lion Optimizer (ALO) and Grasshopper Optimization Algorithm (GOA), are compared. Experiments are conducted on four different kinds of images and the simulation results are presented in several indexes (such as computational time, maximum fitness, average fitness, variance of fitness and other indexes) at different threshold levels for each test image. By scrutinizing the results of the experiment, the superiority of the introduced method is demonstrated, which can meet the needs of image segmentation excellently. [ABSTRACT FROM AUTHOR]

Numerous surrogate-assisted expensive multi-objective optimization algorithms were proposed to deal with expensive multi-objective optimization problems in the past few years. The accuracy of the surrogate models degrades as the number of decision variables increases. In this paper, we propose a surrogate-assisted expensive multi-objective optimization algorithm based on decision space compression. Several surrogate models are built in the lower dimensional compressed space. The promising points are generated and selected in the lower compressed decision space and decoded to the original decision space for evaluation. Experimental studies show that the proposed algorithm achieves a good performance in handling expensive multi-objective optimization problems with high-dimensional decision space. [ABSTRACT FROM AUTHOR]

Van Genuchten (VG) equation is the most commonly used equation of soil moisture characteristic curve, and the accuracy of its parameters directly affects the calculation accuracy of soil moisture motion equation. In order to obtain the parameters of the equation more accurately, this paper establishes an optimization model of the VG equation parameters. This optimization model uses the advantages of the cuckoo search algorithm and the differential evolution algorithm to combine the two into a new hybrid cuckoo search algorithm, namely DECS algorithm, and uses this algorithm to solve the parameter optimization problem of VG equation of soil moisture characteristic curve. By collecting and analyzing the relevant experimental data of various soil qualities, the dehumidification and moisture absorption curves of three different soil qualities were selected for simulation calculation. The results show that in the experiment of solving the parameter estimation problem of the VG equation, the DECS hybrid algorithm has better exploration and development capabilities than the cuckoo search algorithm. The hybrid algorithm has relatively significant performance in terms of calculation accuracy and convergence. [ABSTRACT FROM AUTHOR]

In cloud computing, a lot of challenges like the server failures, loss of confidentiality, improper workloads, etc. are still bounding the efficiency of cloud systems in real-world scenarios. For this reason, many research works are being performed to overcome the shortcoming of existing systems. Among them, load balancing seems to be the most critical issue that worsen the performance of the cloud sector, and hence there necessitates the optimal load balancing with optimal task scheduling. With the intention of accomplishing optimal load balancing by effectual task deployment, this paper plans to develop an advanced load balancing model with the assistance acquired from the metaheuristic algorithms. Usually, handling of tasks in cloud system is an NP-hard problem and moreover, nonpreemptive independent tasks are crucial in cloud computing. This paper goes with the introduction of a new optimal load balancing model by considering three major objectives: minimum makespan, priority, and load balancing, respectively. Moreover, a new single-objective function is also defined that incorporates all the three objectives mentioned above. Furthermore, the deployment of tasks must be optimal and for this a new hybrid optimization algorithm referred as Firefly Movement insisted WOA (FM-WOA) is introduced. This FM-WOA is the conceptual amalgamation of standard Whale Optimization Algorithm (WOA) and Firefly (FF) algorithm. Finally, the performances of the proposed FM-WOA model is compared over the conventional models with the intention of proving its efficiency in terms of makespan, task completion (priority), and degree of imbalance as well. [ABSTRACT FROM AUTHOR]

To overcome the shortcomings of the basic glowworm swarm optimization (GSO) algorithm, such as low accuracy, slow convergence speed and easy to fall into local minima, chaos algorithm and cloud model algorithm are introduced to optimize the evolution mechanism of GSO, and a chaos GSO algorithm based on cloud model (CMCGSO) is proposed in the paper. The simulation results of benchmark function of global optimization show that the CMCGSO algorithm performs better than the cuckoo search (CS), invasive weed optimization (IWO), hybrid particle swarm optimization (HPSO), and chaos glowworm swarm optimization (CGSO) algorithm, and CMCGSO has the advantages of high accuracy, fast convergence speed and strong robustness to find the global optimum. Finally, the CMCGSO algorithm is used to solve the problem of face recognition, and the results are better than the methods from literatures. [ABSTRACT FROM AUTHOR]

In the last decade a new variety of nature inspired optimization algorithms has been appeared. After the swarm based models, researchers turned their inspiration in nature phenomena and laws of science. In this way a new category of algorithms was born, equally effective or even sometimes superior to known algorithms for optimization problems, like genetic algorithms and swarm intelligence schemes. The present survey depicts the evolution of research on nature inspired optimization algorithms related to physical phenomena and laws of science and discusses the possibilities of using the presented approaches in a number of different applications. An attempt has been made to draw conclusions on what algorithm could be used in which different problem areas, for those approaches that this information could be extracted from the related papers studied. The paper also underlines the usage of this kind of nature inspired algorithms in industrial research problems, due to their better confrontation with optimization problems represented with nodes and edges. [ABSTRACT FROM AUTHOR]

In this paper, we extend the work of [Tong, J, J Hu and J Hu (2017). Computing equilibrium prices for a capital asset pricing model with heterogeneous beliefs and margin-requirement constraints. European Journal of Operational Research, 256(1), 24–34] and develop various asynchronous algorithms to calculate the equilibrium asset prices in a heterogeneous capital asset pricing model. These asynchronous algorithms are based on different asynchronous updating schemes such as delayed updating, cyclic updating, fixed-length updating and random updating. In addition to potential benefits of improving computational efficiency, these asynchronous updating schemes also reflect several scenarios in financial markets in which investors may receive asset pricing information with various degrees of delays and their preferences on how and when to rebalance their portfolios may also be different. The proofs for the convergence of these algorithms are given. Numerical experiments are also provided to compare these algorithms and they show that these asynchronous algorithms work quite well. [ABSTRACT FROM AUTHOR]

This paper attempts to develop an efficient route planning algorithm to guide the operations of the multi-helicopter search and rescue in emergency. Route planning model of multi-helicopter cooperative search and rescue activity was established first, based on preference ordering of search and rescue objectives, as well as behavioral model of rescue helicopter and on-board detector. Given the route planning model, a multi-helicopter search and rescue route planning general algorithm was developed. The operation mechanism of ant colony algorithm was improved by introducing cooperative modes and the pheromone updating mechanism into existing methods. Furthermore, two cooperative search and rescue modes were studied: one is Overall Cooperative Search and Rescue Mode (OCSARM), in which many ants search and rescue the same region all together; the other is Blocking Cooperative Search and Rescue Mode (BCSARM), which partitions the region into small blocks and appoints helicopter with corresponding performance capabilities. Simulated experiments were developed to test the operability of proposed multi-helicopter search and rescue route planning algorithm. The comparison with existing algorithm shows that the algorithm proposed in this paper reduces computational complexity and evidently enhances algorithm efficiency. Results also indicate that this algorithm not only has the capability of comparing efficiency of two search and rescue modes in different mission requirements but also helps select search and rescue modes before rescue operation. [ABSTRACT FROM AUTHOR]

As intelligence technology develops, there is a higher requirement for computing speed and accuracy of water injection system simulation. In this paper, aiming at the tree-shaped water injection pipe network system of large-scale oilfields, based on the energy equation for calculating the pressure drop Δ H of pipe section, a mathematical model of the pipeline unit and the node unit is established, and finally, a mathematical model of pipe network for the entire water injection system is established; then, the improved iterative algorithm is used to solve the simulation model of water injection system. In this way, we determine the boundary calculation conditions, take the water injection station as reference node, and use the maximum pressure of water injection well as the initial value of the reference node for calculation, which reduces the number of iterations in model calculation; by comparing the simulation results of different iteration steps, 0.01 is selected as the iteration step size due to its higher calculation accuracy; and the calculation process has also been optimized. The process of solving the characteristic matrix K is combined with the process of calculating the pressure drop Δ H of pipe section, and placed outside the algorithm loop, thereby shortening the calculation time of a single cycle and reducing the calculation amount of the algorithm. The application cases show that the proposed optimization algorithm for water injection system pipe network simulation can be used as an effective method to improve the solution speed and calculation accuracy of the simulation algorithm of tree-shaped water injection system in large-scale oilfields. [ABSTRACT FROM AUTHOR]

This paper presents a novel method for video-based face recognition (VFR) based on M-estimator and image set collaborative representation. Since a video is essentially an image set, the VFR problem can be cast as a special case of the image set-based face recognition (FR) problem. To measure the distance between the query image set and the gallery image set, we develop an M-estimator-based image set collaborative representation (MISCR) model. To implement MISCR, we devise an efficient half-quadratic-based optimization algorithm to tackle the complicated optimization problem. We also establish the convergence property of the devised algorithm. Our other contribution is to propose an MISCR-based classifier for the general image set classification problem, including VFR as a special case. The experiments using real-world benchmark databases demonstrate the efficacy and robustness of the proposed method for VFR. [ABSTRACT FROM AUTHOR]

Applying unified formula while computing point addition and doubling provides immunity to Elliptic Curve Cryptography (ECC) against power analysis attacks (a type of side channel attack). One of the popular techniques providing this unifiedness is the Binary Huff Curves (BHC) which got attention in 2011. In this paper we are presenting highly optimized architectures to implement point multiplication (PM) on the standard NIST curves over and using BHC. To achieve a high throughput over area ratio, first of all, we have used a simplified arithmetic and logic unit. Secondly, we have reduced the time to compute PM through Double and Add algorithm. This is achieved by increasing the frequency of operation through a 2-stage pipelined architecture. The increase in clock cycles caused by consequent pipeline hazards is controlled through optimal scheduling of computations involved in PM. The synthesis results show that our designs can work up to a frequency of 377MHz on Xilinx Virtex 7 FPGA. Moreover, the overall throughput/area ratio achieved through the adopted approach is up to 20% higher while comparing with available state-of-the-art solutions. [ABSTRACT FROM AUTHOR]

A WSN system incorporates a gateway that provides wireless connectivity back to the wired world and distributed nodes. Many existing schemes were utilized for the purpose of increasing the data gathering capacity of sensor nodes from source to sink node. But the existing schemes suffered from maximum distance problem of reaching the sink node. Hence, to overcome those issues, we propose an energy efficient data gathering technique in WSN. Initially, in this paper, the sensor nodes are clustered using LEACH protocol. After the clustering process, cluster heads (CHs) are selected. These selected CHs in all the clusters are used for transferring the messages to the mobile node for data gathering. Then, optimal data transmission path is selected with the help of Optimal Minimum Covering Spanning Tree (OMST) algorithm. The OMST algorithm enhances data gathering and reduces the duration for reaching the sink node via selection of polling points. For reducing time complexity in MST, an efficient optimization algorithm is used. For an optimization purpose, particle gene swarm (PGS) model is used. From the experimental results, the proposed method achieved better results than the existing method in terms of energy, overhead, delay, delivery ratio, NLT and throughput. [ABSTRACT FROM AUTHOR]

This paper presents a novel method to infer regular expressions from positive examples. The method consists of a candidate's construction phase and an optimization phase. We first propose multiscaling sample augmentation to capture the cycle patterns from single examples during the candidate's construction phase. We then use common substrings to build regular expressions that capture patterns across multiple examples, and we show this algorithm is more general than those based on common prefixes or suffixes. Furthermore, we propose a pruning mechanism to improve the efficiency of useful common substring mining, which is an important part of common substring-based expression building algorithm. Finally, in the optimization phase, we model the problem of choosing a set of regular expressions with the lowest cost as an integer linear program, which can be solved to obtain the optimal solution. The experimental results on synthetic and real-life samples demonstrate the effectiveness of our approach in inferring concise and semantically meaningful regular expressions for string datasets. [ABSTRACT FROM AUTHOR]

In this paper, we address the scheduling issues in a class of maintenance, repair and overhaul systems. By considering all key characteristics such as disassembly, material recovery uncertainty, material matching requirements, stochastic routings and variable processing times, the scheduling problem is formulated into a simulation optimization problem. To solve this difficult problem, we developed two hybrid algorithms based on nested partitions method and optimal computing budged allocation technology. Asymptotic convergence of these two algorithms is proved and numerical results show that the proposed algorithms can generate high quality solutions which outperform the performance of many heuristics. [ABSTRACT FROM AUTHOR]

In this paper, we develop improved rearrangement algorithms to find the dependence structure that minimizes a convex function of the sum of dependent variables with given margins. We propose a new multivariate dependence measure, which can assess the convergence of the rearrangement algorithms and can be used as a stopping rule. We show how to apply these algorithms for example to finding the dependence among variables for which the marginal distributions and the distribution of the sum or the difference are known. As an example, we can find the dependence between two uniformly distributed variables that makes the distribution of the sum of two uniform variables indistinguishable from a normal distribution. Using MCMC techniques, we design an algorithm that converges to the global optimum. [ABSTRACT FROM AUTHOR]

This paper introduces an alternative fuzzy clustering method that does not require fixing the number of clusters a priori and produce reliable clustering results. This newly proposed method empowers the existing Improved Artificial Fish Swarm algorithm (IAFSA) by the simulated annealing (SA) algorithm. The hybrid approach can prevent IAFSA from unexpected vibration and accelerate convergence rate in the late stage of evolution. Computer simulations are performed to compare this new method with well-known fuzzy clustering algorithms using several synthetic and real-life datasets. Our experimental results show that our newly proposed approach outperforms some other well-known existing fuzzy clustering algorithms in terms of both accuracy and robustness. [ABSTRACT FROM AUTHOR]

Cloud computing has become a powerful mechanism for initiating secure communication among users. The advancements in the technology lead to provide various services, like accessing network, resources, and platform. However, handling large datasets and security are major issues in the cloud systems. Hence, this paper proposes a technique, namely, Jaya–Whale Optimization (JWO), which is the integration of Jaya algorithm and Whale optimization algorithm (WOA) and adapts homomorphic encryption for initiating secure data transmission in the cloud. The original data are preserved by generating Data Protection (DP) coefficient using the proposed JWO algorithm. In the proposed algorithm, the fitness is calculated based on privacy and utility parameters for selecting the optimal solution. Also, the sanitized data are generated by EXORing the Key Information Product (KIP) matrix and key vector. Finally, the data owner provides the key to the users for retrieving the original data from the sanitized data. The experimentation is carried out using Cleveland, Hungarian, and Switzerland datasets in terms of BD, accuracy, and fitness and the analysis shows that the proposed JWO provides superior performance in terms of BD, accuracy, and fitness parameters with values 0.720, 0.822, and 0.722. [ABSTRACT FROM AUTHOR]

Online stream processing is an emerging research area in the field of computer science. Semi-stream processing is a particular type of stream processing where a stream of data is processed with a disk-based relation. A semi-stream join operator is required to implement this operation. Many semi-stream joins use a queue of stream tuples to amortize access cost for the disk-based relation, and use an index to allow directed access to the relation, avoiding the loading of unnecessary partition of R. In such a situation, the question arises which R partitions should be accessed, as any stream tuple from the queue could serve as a lookup element for accessing the relation index. Existing algorithms use simple safe and correct strategies, but are not optimal in the sense that they maximize the join service rate. This paper makes two contributions: first contribution is in terms of optimization in which we analyze strategies for selecting an appropriate lookup element, particularly for skewed stream data. We show that a good selection strategy can improve service rate of the existing join algorithms significantly. Second contribution is in terms of extension in which we develop multi-stage join for semi-stream join algorithms. Multi-stage join is important when stream data needs to be joined with two or more tables in the relation e.g., stream of sales data needs information to be added from product and customer tables in the relation. To the best of our knowledge, none of the existing algorithms implement this feature. For the service rate evaluation we use two well-performed existing algorithms CACHEJOIN and HYBRIDJOIN. We evaluate the service rate using real, TPC-H, and synthetic datasets with a known skewed distribution. We also present the cost model for our multi-stage join. [ABSTRACT FROM AUTHOR]

In order to overcome the drawbacks of the K-means (KM) for clustering problems such as excessively depending on the initial guess values and easily getting into local optimum, a clustering algorithm of invasive weed optimization (IWO) and KM based on the cloud model has been proposed in the paper. The so-called cloud model IWO (CMIWO) is adopted to direct the search of KM algorithm to ensure that the population has a definite evolution direction in the iterative process, thus improving the performance of CMIWO K-means (CMIWOKM) algorithm in terms of convergence speed, computing precision and algorithm robustness. The experimental results show that the proposed algorithm has such advantages as higher accuracy, faster constringency, and stronger stability. [ABSTRACT FROM AUTHOR]

Clustering is a fundamental learning task in a wide range of research fields. The most popular clustering algorithm is arguably the K-means algorithm, it is well known that the performance of K-means algorithm heavily depends on initialization due to its strong non-convexity nature. To overcome the initialization issue, in this paper, we first relax the K-means model as an optimization problem with non-convex constraints, then employ the Proximal Alternating Linearized Minimization (PALM) method to solve the relaxed non-convex optimization model. The convergence analysis of PALM algorithm for the clustering problem is also provided. Experimental results on several benchmark datasets are conducted to evaluate the efficiency of our approach. [ABSTRACT FROM AUTHOR]

In recent years, the notion of scale elasticity-the relative changes of outputs with respect to relative changes of inputs-has received a lot of ink in the literature. However, all prior studies, except a few of them, assume that changes are equi-proportional. This simplifying assumption makes scale elasticity measure to preserve the throughput mix and implicitly ignores both input independencies and decision-maker preferences. This paper seeks to investigate two main objectives. It initially proposes the notion of directional scale elasticity to allow evaluation of elasticity in any direction which may naturally alter the mix. Subsequently, by providing a tangible geometric interpretation, it attempts to clarify the measure. Relevant computations are used to construct cross sections of frontier in a given direction by enlisting the advantages of parametric optimization algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we propose a unified architecture for computation of double-precision floating-point division, reciprocal, square root, inverse square root and multiplication with a significant area reduction. First, a double-precision multiplication-based divider, the common datapath shared with these arithmetic computations, is optimized by a modified Goldschmidt algorithm to achieve better area efficiency. In this algorithm, a linear-degree minimax approximation instead of second-degree is used to obtain a 15-bit precision estimate of the reciprocal so that we can get a rather small lookup table (LUT) as well as reduced amount of computation when accumulating the partial products. Two Goldschmidt iterations specially designed for hardware reuse are performed to gain the final accurate result of division. By virtue of the pipelined processing, the time cost for the two iterations is minimized. Second, a reconfigurable datapath with a little extra area cost is introduced to dynamically support multiple double-precision computations by executing the optimized divider iteratively. The design is finally implemented and synthesized in SMIC 0.13-μm CMOS process. The experimental results show that the proposed design can achieve a speed of 400 MHz with area of 61.6 K logic gates and 9-Kb LUT. Compared with other works, the area efficiency (performance/area ratio) of the proposed unified architecture is increased by about 20% in average, which is a better performance-area trade-off for embedded microprocessors. [ABSTRACT FROM AUTHOR]

Electrocardiography (ECG) signals provides assistance to the cardiologists for identification of various cardiovascular diseases (CVD). ECG machine records the electrical activity of the heart with the assistance of electrodes placed on the patient's body. Qualitative characterization of ECG signal reflects its sensitiveness towards distinct artifacts that resulted in low diagnostic accuracy and may lead to incorrect decision of the clinician. The artifacts are removed utilizing a robust noise estimator employing DTCWT using various threshold values and functions. The segments and intervals of ECG signals are calculated using the peak detection algorithm followed by particle swarm optimization (PSO) and the proposed optimization technique to select the best features from a considerable pool of features. Out of the 12 features, the best four features are selected using PSO and the proposed optimization technique. Comparative analysis with other feature selection methods and state-of-the-art techniques demonstrated that the proposed algorithm precisely selects principle features for handling the ECG signal and attains better classification utilizing distinctive machine learning algorithms. The obtained accuracy using our proposed optimization technique is 95.71% employing k -NN and neural networks. Also, 4% and 10% improvements have been observed while using k -NN over ANN and SVM, respectively, when the PSO technique is executed. Similarly, a 14.16% improvement is achieved while using k -NN and ANN over the SVM machine learning technique for the proposed optimization technique. Heart rate is calculated using the proposed estimator and optimization technique, which is in consensus with the gold standard. [ABSTRACT FROM AUTHOR]

A* algorithm and ant colony optimization (ACO) are more widely used in path planning among global path planning algorithms. The optimization process is analyzed and summarized from the principles and characteristics of the two algorithms, A* algorithm is mainly optimized in terms of point selection and improvement of heuristic function; and ACO is mainly investigated in terms of transfer probability and pheromone positive feedback for improvement and optimization. Taking a single algorithm solving complex optimization problems difficulties into consideration, a splitting strategy can be used. So that local path or intelligent path optimization algorithms are incorporated in global path planning to improve search efficiency and optimization quality. [ABSTRACT FROM AUTHOR]

In this paper, we consider optimal scheduling algorithms for scientific workows with two typical structures, fork&join and tree, on a set of provisioned (virtual) machines under budget and deadline constraints in cloud computing. First, given a total budget B, by leveraging a bi-step dynamic programming technique, we propose optimal algorithms in pseudo-polynomial time for both workows with minimum scheduling length as a goal. Our algorithms are efficient if the total budget B is polynomially bounded by the number of jobs in respective workows, which is usually the common case in practice. Second, we consider the dual of this optimization problem to minimize the cost when the deadline of the computation D is fixed. We change this problem into the standard multiple-choice knapsack problem via a parallel transformation. [ABSTRACT FROM AUTHOR]

Cloud computing is the distributed computing paradigm continually exposed to different attacks and threats of various origins. The data stored in the cloud framework is easier for external and internal intruders, as access to the cloud framework is done through internet services. Various intrusion detection (ID) methods are developed to detect network intruders in the cloud, but these methods are not primarily effective in generating accurate detection results. Hence, an effective intrusion detection system (IDS) is designed to solve the security issues that unfavorably influence the sustainable development of the cloud and enhance the protection of the cloud from malicious attacks. The IDS is modeled using the proposed Feedback Deer Hunting Optimization (FDHO)-based Deep Residual network to detect network intrusions. However, the proposed FDHO algorithm is designed by integrating Feedback Artificial Tree (FAT) with Deer Hunting Optimization (DHOA), respectively. Moreover, the detection of malicious attacks is carried out using a Deep Residual network that significantly increases the training speed, reduces the computational complexity, and generates effective detection results. The performance of the proposed method is comparatively analyzed with the existing techniques, such as Stacked Contractive Auto-Encoder and Support Vector Machine (SCAE+SVM), Artificial Neural Network with ant bee colony optimization algorithm+fuzzy clustering (ANN+ABC+fuzzy clustering), Improved dynamic immune algorithm (IDIA), and Normalized K-means (NK) clustering algorithm with RNN named, (NK-RNN), FAT-based Deep Residual network, and DHOA-based Deep Residual network using the BoT-IoT dataset and KDD cup-99 dataset. The proposed method achieved outstanding performance by considering the metrics, like specificity, accuracy, and sensitivity, with the values of 0.9526, 0.9498, and 0.9214 using the BoT-IoT dataset. [ABSTRACT FROM AUTHOR]

For handling digital images for various applications, image denoising is considered as a fundamental pre-processing step. Diverse image denoising algorithms have been introduced in the past few decades. The main intent of this proposal is to develop an effective image denoising model on the basis of internal and external patches. This model adopts Non-local means (NLM) for performing the denoising, which uses redundant information of the image in pixel or spatial domain to reduce the noise. While performing the image denoising using NLM, "denoising an image patch using the other noisy patches within the noisy image is done for internal denoising and denoising a patch using the external clean natural patches is done for external denoising". Here, the selection of optimal block from the entire datasets including internal noisy images and external clean natural images is decided by a new hybrid optimization algorithm. The two renowned optimization algorithms Chicken Swarm Optimization (CSO), and Dragon Fly Algorithm (DA) are merged, and the new hybrid algorithm Rooster-based Levy Updated DA (RLU-DA) is adopted. The experimental results in terms of some relevant performance measures show the promising results of the proposed model with remarkable stability and high accuracy. [ABSTRACT FROM AUTHOR]

Coherent Signal-Subspace (CSS) is a technique to separate the wide frequency band into narrowband components which can be applied in many applications such as smart antennas for wireless communications and radar. However, CSS is computationally intensive and may not achieve the real-time requirement. Therefore, this work aims to propose an efficient implementation of the CSS method on Field-Programmable Gate Array (FPGA) to achieve the desired performance. Different parallelization and optimization techniques such as loop unrolling, loop pipelining, dataflow, and loop flattening are adopted and applied to explore the opportunities of any computation and storage that could be eliminated in order to achieve high efficiency. The results of the proposed optimized implementation achieve the highest performance compared with other related implementations. [ABSTRACT FROM AUTHOR]

Self-organizing feature map (SOFM) neural network is a kind of competitive unsupervised learning neural network, which has strong self-organizing and self-learning capabilities. It has been widely used in the fields of data classification and data clustering. A crucial step for SOFM neural network is to set its weight parameters correctly because the output accuracy and efficiency of the network depend much on these parameters. Most of current methods for parameter setting are based on static data. However, in a dynamic environment, the statistical characteristics of the generated data will change unpredictably over time. If the SOFM network cannot react to the changes of the environment, its performance will degrade. To deal with this problem, a more powerful multi-swarm artificial bee colony algorithm (MABC) is proposed. In the algorithm, the classic ABC algorithm is improved with multi-swarm and exclusive operation strategies to make it suitable for tracking optimal parameter settings of the SOFM network, so that the SOFM network can be applied to a dynamic environment. Two real data streams, which are regarded as coming from dynamic environments, are used to evaluate the effectiveness of the algorithm. Results show that the proposed algorithm is superior to the classic SOFM algorithm in terms of clustering purity and effectiveness. It is a promising method for the classification of data streams from dynamic environments. [ABSTRACT FROM AUTHOR]

This article considers a problem of solving the optimal solution of the sum of locally convex cost functions over an undirected network. Each local convex cost function in the network is accessed only by each unit. To be able to reduce the amount of computation and get the desired result in an accelerated way, we put forward a fresh accelerated decentralized event-triggered algorithm, named as A-DETA, for the optimization problem. A-DETA combines gradient tracking and two momentum accelerated terms, adopts nonuniform step-sizes and emphasizes that each unit interacts with neighboring units independently only at the sampling time triggered by the event. On the premise of assuming the smoothness and strong convexity of the cost function, it is proved that A-DETA can obtain the exact optimal solution linearly in the event of sufficiently small positive step-size and momentum coefficient. Moreover, an explicit linear convergence speed is definitely shown. Finally, extensive simulation example validates the usability of A-DETA. [ABSTRACT FROM AUTHOR]

Among other machine learning techniques, the extreme learning machine has evidently proved its diagnostic accuracy on many cases in medical domain. Its accuracy mainly depends on the optimal parameters that are used in training. The proposed work is based on optimizing the extreme learning machine using the recently proposed meta-heuristic optimization technique named artificial algae algorithm with multi-light source. In this work, two experiments are conducted using four binary classification datasets related to medical domain. The feasible number of hidden neurons is found from the first experiment using relevant performance parameters. In the second experiment, the classifier with feasible number of hidden neurons is further evaluated with the ten-fold cross-validation method based on its computation time and classification accuracy. In both the experiments, the proposed classifier performance compared with that of other four similar hybrid approaches. It is also statistically compared using Friedman test and Wilcoxon signed rank test based on the area under curve and accuracy values respectively. It is found that the proposed classifier produces better results than the other classifiers. [ABSTRACT FROM AUTHOR]

This work proposed a memetic version of Artificial Bee Colony algorithm, or called LSABC, which employed a "shrinking" local search strategy. By gradually shrinking the local search space along with the optimization process, the proposed LSABC algorithm randomly explores a large space in the early run time. This helps to avoid premature convergence. Then in the later evolution process, the LSABC finely exploits a small region around the current best solution to achieve a more accurate output value. The optimization behavior of the LSABC algorithm was studied and analyzed in the work. Compared with the classic ABC and several other state-of-the-art optimization algorithms, the LSABC shows a better performance in terms of convergence rate and quality of results for high-dimensional problems. [ABSTRACT FROM AUTHOR]

The shuffled differential evolution (SDE) is a recently proposed version of differential evolution (DE). However the SDE employs several efforts to compensate limited amount of search moves in the original DE, but these efforts are performed by the same operator. To vary search moves of the SDE, this research proposes employing a secondary mutation operator beside of first mutation operator. This new mutation operator can generate some different offspring than those generated by the first one. Experiments demonstrate a better performance of the proposed algorithm than the SDE. In a later part of the comparative experiments, performance comparisons of the proposed algorithm with some modern DE and other evolutionary algorithms reported in the literature confirm a better or at least a competitive performance of our proposed algorithm. Also a real-world optimization problem, namely, Spread Spectrum Radar Polly phase Code Design, is solved to show the wide applicability of the DSDE. [ABSTRACT FROM AUTHOR]

The shortest path problem is a kind of optimization problem and its aim is to find the shortest path connecting two specific nodes in a network, where each edge has its distance. When considering not only the distances between the nodes but also some other information, the problem is formulated as a multi-objective shortest path problem that involves multiple conflicting objective functions. The multi-objective shortest path problem is a kind of optimization problem of multi-objective network. In the general cases, multi-objectives are rarely optimized by a solution. So, to solve the multi-objective shortest path problem leads to obtaining Pareto solutions. An algorithm for this problem has been proposed by using the extended Dijkstra's algorithm. However, this algorithm for obtaining Pareto solutions has many useless searches for paths. In this study, we consider two-objective shortest path problem and propose efficient algorithms for obtaining the Pareto solutions. Our proposed algorithm can reduce more search space than existing algorithms, by solving a single-objective shortest path problem. The results of the numerical experiments suggest that our proposed algorithms reduce the computing time and the memory size for obtaining the Pareto solutions. [ABSTRACT FROM AUTHOR]