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Task scheduling plays a key role in effectively managing and allocating computing resources to meet various computing tasks in a cloud computing environment. Short execution time and low load imbalance may be the challenges for some algorithms in resource scheduling scenarios. In this work, the Hierarchical Particle Swarm Optimization-Evolutionary Artificial Bee Colony Algorithm (HPSO-EABC) has been proposed, which hybrids our presented Evolutionary Artificial Bee Colony (EABC), and Hierarchical Particle Swarm Optimization (HPSO) algorithm. The HPSO-EABC algorithm incorporates both the advantages of the HPSO and the EABC algorithm. Comprehensive testing including evaluations of algorithm convergence speed, resource execution time, load balancing, and operational costs has been done. The results indicate that the EABC algorithm exhibits greater parallelism compared to the Artificial Bee Colony algorithm. Compared with the Particle Swarm Optimization algorithm, the HPSO algorithmnot only improves the global search capability but also effectivelymitigates getting stuck in local optima. As a result, the hybrid HPSO-EABC algorithm demonstrates significant improvements in terms of stability and convergence speed.Moreover, it exhibits enhanced resource scheduling performance in both homogeneous and heterogeneous environments, effectively reducing execution time and cost, which also is verified by the ablation experimental. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) network integrates physical objects such as sensors, networks, and electronics with software to collect and exchange data. Physical objects with a unique IP address communicate with external entities over the internet to exchange data in the network. Due to a lack of security measures, these network entities are vulnerable to severe attacks. To address this, an efficient security mechanism for dealing with the threat and detecting attacks is necessary. The proposed hybrid optimization approach combines Spider Monkey Optimization (SMO) and Hierarchical Particle Swarm Optimization (HPSO) to handle the huge amount of intrusion data classification problems and improve detection accuracy by minimizing false alarm rates. After finding the best optimum values, the Random Forest Classifier (RFC) was used to classify attacks from the NSL-KDD and UNSW-NB 15 datasets. The SVM model obtained accuracy of 91.82%, DT of 98.99%, and RFC of 99.13%, and the proposed model obtained 99.175% for the NSL-KDD dataset. Similarly, SVM obtained accuracy of 85.88%, DT of 88.87%, RFC of 91.65%, and the proposed model obtained 99.18% for the UNSW NB-15 dataset. The proposed model achieved accuracy of 99.175% for the NSL-KDD dataset which is higher than the state-of-the-art techniques such as DNN of 97.72% and Ensemble Learning at 85.2%. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) network integrates physical objects such as sensors, networks, and electronics with software to collect and exchange data. Physical objects with a unique IP address communicate with external entities over the internet to exchange data in the network. Due to a lack of security measures, these network entities are vulnerable to severe attacks. To address this, an efficient security mechanism for dealing with the threat and detecting attacks is necessary. The proposed hybrid optimization approach combines Spider Monkey Optimization (SMO) and Hierarchical Particle Swarm Optimization (HPSO) to handle the huge amount of intrusion data classification problems and improve detection accuracy by minimizing false alarm rates. After finding the best optimum values, the Random Forest Classifier (RFC) was used to classify attacks from the NSL-KDD and UNSW-NB 15 datasets. The SVM model obtained accuracy of 91.82%, DT of 98.99%, and RFC of 99.13%, and the proposed model obtained 99.175% for the NSL-KDD dataset. Similarly, SVM obtained accuracy of 85.88%, DT of 88.87%, RFC of 91.65%, and the proposed model obtained 99.18% for the UNSW NB-15 dataset. The proposed model achieved accuracy of 99.175% for the NSL-KDD dataset which is higher than the state-of-the-art techniques such as DNN of 97.72% and Ensemble Learning at 85.2%.

Advanced correlation filters are an effective tool for target detection within a particular class. Most correlation filters are derived from a complex filter equation leading to a closed form filter solution. The response of the correlation filter depends upon the selected values of the optimal trade-off (OT) parameters. In this paper, the OT parameters are optimized using particle swarm optimization with respect to two different cost functions. The optimization has been made generic and is applied to each target separately in order to achieve the best possible result for each scenario. The filters obtained using standard particle swarm optimization (PSO) and hierarchal particle swarm optimization algorithms have been compared for various test images with the filter solutions available in the literature. It has been shown that optimization improves the performance of the filters significantly.

Data clustering is one of the most widely used data mining techniques, classifying similar data items into groups on the basis of similarity among the data items. Different issues have been observed while achieving the classification of data into the most suitable grouping. Efficiency of the clustering techniques and accuracy of the resulting groups are two of the main issues. To tackle these issues, recently, optimization based techniques have been used, resulting in enhanced quality of the output and improved efficiency of the clustering process. Swarm Intelligence (SI) is one such technique whose different algorithms have been found effective for this purpose. Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) are the two most prominent SI based techniques. In this paper we analyze the use of PSO for data clustering in particular for clustering in a hierarchical manner. We chose PSO based hierarchical techniques, Evolutionary PSO for clustering (EPSO-clustering) and Hierarchical PSO for clustering (HPSO-clustering). Both these techniques work in a hierarchical agglomerative manner, with HPSO-clustering an extension of EPSO-clustering. It combines the properties of hierarchical and partitional clustering and adds SI based optimization to the process. We evaluate our proposed clustering techniques on different benchmark datasets from UCI machine learning data repository as well as real data that we collected locally from a web server. We used inter-cluster and intra-cluster distances, and execution time to measure the performance of our proposed techniques. For evaluation we selected different clustering techniques that were previously used as benchmarks such as k-means, PSO-clustering, Hierarchical Agglomerative Clustering (HAC) and DBSCAN. The results verify that the proposed techniques perform better on the suggested measures against the benchmarks mentioned. [ABSTRACT FROM AUTHOR]

We propose two variations on particle swarm optimization (PSO): the use of a heuristic function as an additional biasing term in PSO solution construction; and the use of a local search step in the PSO algorithm. We apply these variations to the hierarchical PSO model and evaluate them on the quadratic assignment problem (QAP). We compare the performance of our method to diversified-restart robust tabu search (DivTS), one of the leading approaches at present for the QAP. Our experimental results, using instances from the QAPLIB instance library, indicate that our approach performs competitively with DivTS. [ABSTRACT FROM AUTHOR]

Many decision-making problems in traffic and logistics network system planning can be formulated as bilevel programming models, which are intrinsically nonconvex and existing algorithms are either difficult to obtain the global optimal solution or to solve large scale problems with the complexity and efficiency issues. In this paper, based on the idea of evolution game and multi-objective optimization nondominated sort, a hierarchical particle swarm algorithm for the model has been designed, by the interaction of two particle swarm algorithm iterations to simulate the interaction between policy-makers during the gmne searching, and to obtain the optimal solution of bilevel programming problems, in order to maximize the interests of all parties. Finally, the application of the model and its algorithm are illustrated with test function. [ABSTRACT FROM AUTHOR]

Abstract: A new architecture for improvement of slew rate (SR) of an op-amp or an operational transconductance amplifier (OTA) in FinFET technology is proposed. The principle of operation of the proposed architecture is based on a set of additional current sources which are switched on, only when OTA should provide a high current, usually for charge or discharge of large load capacitor. Therefore, the power overhead is less compared to conventional high SR designs. The commonly used two-stage Miller-compensated op-amp, designed and optimized in sub 45nm FinFET technology with 1V single supply voltage, is used as an example for demonstration of the proposed method. For the same FinFET technology and with optimal design, it is shown that the slew rate of the op-amp is significantly improved. The slew rate is improved from 273 to for an input signal with a rise time of 100ps. The other performance measures such as gain and phase margin remain unchanged with the additional circuitry used for slew rate enhancement. [Copyright &y& Elsevier]

Abstract: The particle swarm optimization (PSO) algorithm is applied to the problem of MOSFET parameter extraction for the first time. It is shown to perform significantly better than the genetic algorithm (GA). Several modifications of the basic PSO algorithm have been implemented: (a) Hierarchical PSO (HPSO) in which particles are hierarchically arranged and influenced by the positions of the local and global leaders, (b) memory loss operation due to which a particle forgets its past best position, (c) intensive local search in which the solution space around the global leader is searched with a high resolution, and (d) adaptive inertia which causes the inertia of the particles to change adaptively, depending on the fitness of the population. It is demonstrated that the above features improve the performance of the basic PSO algorithm both for the MOSFET parameter extraction problem and for benchmark functions. [Copyright &y& Elsevier]

As China's environmental governance turn to a high-quality management, establishing reasonable and effective bonus-penalty mechanism is an effective means to promote coordinated emission reduction and improve regional air quality. Therefore, this paper constructs a bi-level programming model with bonus-penalty standards and emission quotas, based on the maximization of social benefits and the minimization of the gap between reward and punishment funds. Given the NP-hard problem of bi-level programming model, this work also designs a nested hierarchical particle swarm optimization (HPSO) algorithm for the above model. Subsequently, from the emission data of SO2 and macroeconomic data in the northeastern region of China from 2006 to 2015, the SO2 emission quota and bonus-penalty standards of each province are calculated. The results demonstrate that (1) the optimal emission quotas of Heilongjiang, Jilin and Liaoning province are 37.7, 30.3 and 69.3 (10,000 tons), respectively. (2) When the compensation fee of pollutant abatement is set at 521.48 yuan/ton and the standard of excess discharge levy is 2977.43 yuan/ton, the effect of pollution control in each province can be effectively improved. The empirical analysis also reveals that the proposed model has a remarkable effect on regional environmental governance. Finally, this paper puts forward some suggestions on the implementation of the bonus-penalty mechanism. • A bi-level programming model with bonus-penalty standards was developed. • This paper provides a new way to solve the same bilevel programming problem. • This paper's simulation focuses on an old industrial area. [ABSTRACT FROM AUTHOR]