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Intrusion Detection System (IDS) can ensure the network security by identifying network intrusions according to the abnormal traffic data. However, the intrusion detection data has the problem of high dimensionality and changes with network and attack environments, which leads to the poor performance and poor portability of intrusion detection algorithms. Therefore, this paper proposes an intrusion detection algorithm based on joint symmetric uncertainty and hyperparameter optimized fusion neural network. Firstly, a feature selection method based on symmetric uncertainty and approximate Markov blanket is proposed, which fully considers the correlation and redundancy of features, and also the correlation between combined features and the class label, so as to reduce the data dimensionality. Secondly, the CNN-LSTM classifier fused with Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) is used to extract the spatial features and temporal features to improve the classification performance. Finally, the Particle Swarm Optimization (PSO) algorithm is improved and used to automatically optimize the hyperparameters of the classifier, so that the classifier can be applied to different intrusion detection datasets with better generalization ability and portability. Experiments have verified the effectiveness and superiority of the proposed algorithm on multiple evaluation indicators. • An effective algorithm for intrusion detection is proposed in this paper. • Feature selection is based on symmetric uncertainty and approximate Markov blanket. • A fusion neural network is constructed to extract the spatial and temporal features. • The PSO algorithm is improved to automatically optimize the hyperparameters. [ABSTRACT FROM AUTHOR]

• Distinct DG Output Modes Validation: The simulation validates distinct DG output modes, impacting prediction. • Mode-Based State Estimation: The paper proposes an innovative mode-based state estimation using IPSO-DBSCAN clustering, overcoming k-means limitations. • Enhanced Data Recovery using DBSCAN: Enhanced data recovery employs DBSCAN with improved secondary clustering, guided by similarity and homogeneity, outperforming traditional methods. • BiGRU-PF State Estimation: BiGRU-PF enhances state estimation accuracy and robustness in distribution networks by detecting DG output modes, addressing k-means limitations, and improving data recovery. This paper proposes a new forecasting-aided state estimation (FASE) method for distribution systems that mitigates issues with uncertain distributed generation (DG) and lost measurements. We utilize an Improved Particle Swarm Optimization (IPSO)-optimized Density-Based Spatial Clustering of Applications with Noise (DBSCAN) for examination of historical DG output data. Based on identified DG output modes, it facilitates precise state prediction and data reconstruction,. The proposed method employs a Bidirectional Gated Recurrent Unit (BiGRU) neural network for state prediction and a particle filter (PF) for final state filtering. The method verification is provided through Python simulations of the Distribution Transformer Unit (DTU)7k distribution network system, demonstrating improved accuracy and robustness against sudden load change and bad data in measurements. [ABSTRACT FROM AUTHOR]

Fractional order filters offer greater freedom of design and a precise control over stopband attenuation in electronic circuits and systems. This paper presents the design of a fractional order low pass Chebyshev filter (FOLCF) that achieves near-ideal response characteristics. The methodology introduced utilizes metaheuristic optimization methods, including particle swarm optimization, firefly algorithm, and grey wolf optimization. These techniques are employed to precisely adjust the filter coefficients for the orders (1+α), (2+α), and (3+α). The adjustment is carried out by comparing the desired behaviour of the FOLCF with generalized fractional order low pass transfer functions. Throughout these instances, the parameter α is varied within the range of (0, 1). The designed filters are then tested and compared on the basis of various factors. Simulation results demonstrate that the designed filters closely follow the behaviour of an ideal Chebyshev filter with maximum passband and stopband magnitude errors being −31.93 dB and −52.74 dB respectively for (1+α) order filters. These values for (2+α) and (3+α) order FOLCF have been observed to be −30.04 dB and −55.91 dB; −17.72 dB and −49.52 dB respectively. Furthermore, it has been observed that the proposed work outperforms existing state-of-the-art approaches in various aspects, including magnitude error, stopband attenuation, and cut-off frequency. The stability of the designed filters has been verified through stability analysis. Additionally, practical feasibility of the proposed FOLCF is demonstrated through SPICE simulations for α = [0.2,0.5,0.8] using second generation current conveyor (CCII) and operational transconductance amplifier (OTA) based topologies while approximating the constant phase element using fifth order continued fraction expansion. The SPICE implementations closely follow the behaviour of ideal filter with −48.67 dB and −62.8 dB as mean square errors for CCII and OTA circuits respectively, showcasing the proposed filters' superiority and practical applicability in advanced electronic design. • A generalized design of Fractional order low Pass Chebyshev filters having near idle response has been proposed. • Nature inspired Algorithms: particle swarm optimization (PSO), firefly algorithm (FA) & grey wolf optimization (GWO) are used. • Comparisons with existing literature on basis of magnitude errors, stopband attenuation and cut off frequencies has been made. • Stability analysis for all the designed filters has been done by converting s-plane to W-plane. • Realization of proposed filters has been done by using CCII and OTA based circuits to test feasibility. [ABSTRACT FROM AUTHOR]

This paper considers the inverse p -median location problem with variable edge lengths and variable vertex weights on general graphs in which the modification costs are the fuzzy random variables. We present a model for the problem in fuzzy random environment in which the objective value is computed by conditional value at risk criterion. Then, we show that the problem is NP-hard under this criterion. Therefore, a new hybrid modified particle swarm optimization algorithm is proposed to obtain the approximate optimal solution of the proposed model. Finally, computational experiments are given to illustrate high efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

• Differential privacy and deep learning are combined to alleviate the problem of model privacy leakage. • Differential evolution is used to optimize the learning rate, accelerate the model convergence, and avoid the privacy loss. • The particle swarm optimization method is used to find the optimal weight that satisfies the privacy. • The algorithm in this paper is verified on three data sets, and the accuracy and efficiency are better. In deep learning differential privacy protection, adding noise based on gradient has become a mainstream algorithm, but excessive gradient perturbation noise causes accuracy degradation. To solve this problem, a differential privacy protection algorithm based on differential evolution and particle swarm optimization is proposed to realize hyperparameter optimization in differential privacy, reduce the impact of noise on the model, and effectively improve the accuracy. On the one hand, the differential evolution scheme performs selection, crossover and mutation on learning rate η , make it approach the global optimal solution, and improve the computational efficiency of the algorithm. On the other hand, the particle swarm optimization scheme is adopted. Without changing the parameters and gradient structure, the parameters are optimized by using the network propagation attributes, which reduces the influence of noise on the accuracy. Experiments are performed on three datasets: Cifar10, Mnist and FashionMnist. Compared with the existing differential privacy algorithms, under the same privacy budget, the proposed algorithm has better accuracy and higher efficiency. [ABSTRACT FROM AUTHOR]

PSO (particle swarm optimization), is an intelligent search method for finding the best solution according to population state. Various parallel implementations of this algorithm have been presented for intensive-computing applications. The ALC-PSO algorithm (PSO with an aging leader and challengers) is an improved population-based procedure that increases convergence rapidity, compared to the traditional PSO. In this paper, we propose a low-power heterogeneous parallel implementation of ALC-PSO algorithm using OmpSs and CUDA, for execution on both CPU and GPU cores. This is the first effort to heterogeneous parallel implementing ALC-PSO algorithm with combination of OmpSs and CUDA. This hybrid parallel programming approach increases the performance and efficiency of the intensive-computing applications. The proposed approach of this article is also useful and applicable for heterogeneous parallel execution of the other improved versions of PSO algorithm, on both CPUs and GPUs. The results demonstrate that the proposed approach provides higher performance, in terms of delay and power consumption, than the existence implementations of ALC-PSO algorithm. [ABSTRACT FROM AUTHOR]

Artificial rabbits optimization (ARO) is a swarm intelligence-based algorithm inspired by the survival strategies of rabbits. Although ARO has a good convergence rate, it is prone to get stuck in the local optima and converge prematurely. To overcome this, the present paper redesigns the exploration operator of the ARO algorithm with the roulette fitness-distance balance (RFDB) and dynamic fitness-distance balance (d FDB) strategies. In this context, three different versions of the fitness-distance balance-based artificial rabbits optimization (FDBARO) algorithm are developed. The performance of the original ARO and FDBARO versions (FDBARO-1, FDBARO-2, and FDBARO-3) are evaluated on CEC 2017 and CEC 2020 benchmark functions. The obtained results are analyzed with the Wilcoxon and Friedman statistical tests. Statistical and convergence analysis results showed that the FDBARO-3 algorithm designed with the d FDB selection method can explore the search space more successfully compared to other algorithms. This version was named the dynamic FDBARO (d FDBARO) algorithm. Moreover, the practicability of the proposed d FDBARO is highlighted by the solution of the optimal power flow (OPF) problem formulated with renewable energy sources (RESs) and flexible alternating current transmission system (FACTS) devices considering fixed and uncertain load demands. Experimental results showed that the proposed d FDBARO is a competitive algorithm for solving global optimization and constrained OPF problems. The source code of the d FDBARO algorithm is available at https://www.mathworks.com/matlabcentral/fileexchange/154845-dfdbaro-an-enhanced-metaheuristic-algorithm. [ABSTRACT FROM AUTHOR]

This paper proposes a novel hybrid algorithm which is mathematically modelled by amalgamating the superior features of recently developed Grey Wolf Optimizer (GWO), Sine Cosine Algorithm (SCA), and Crow Search Algorithm (CSA). Researchers have already implemented the aforementioned three algorithms and obtained superior quality results for solving diverse optimization problems. The novel hybrid Variegated GWO Algorithm (VGWO) developed in this proposed research work is initially realized and validated for solving IEEE CEC-C06 2019 benchmark functions. Thereafter, the proposed VGWO is utilized as an optimization tool to solve three emerging and complex power system optimization problems which includes energy management of microgrid systems operated in both islanded and grid-connected mode, dynamic economic emission dispatch and reactive power planning (RPP) problem. A comparative analysis of the proposed VGWO approach with other established metaheuristics is undertaken for each optimization problem. Numerical results show that the novel hybrid VGWO algorithm outperformed an ample number of optimization techniques in providing better quality solutions. The proposed hybrid algorithm yielded a 36.93% reduction in active power loss and 36.80% reduction in operating cost with respect to base case condition for RPP problem. Likewise while solving microgrid energy management problems 9–30% savings was realized in the generation cost compared to the ones mentioned in literature. The capability of handling many complex constraints within a minimum amount of computational time to provide consistently best solutions prioritize the proposed hybrid algorithm among its kinds. Statistical analysis validates the authenticity and viability of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper presents an efficient inverse design method of arrayed waveguide grating (AWG) based on BPNN-PSO. Five important structural parameters of AWG are selected as inputs, and the spectral responses of three output channels are used as outputs. A forward network is trained, and the prediction accuracy of the network is improved by optimizing the number of hidden layer neurons. By combining the forward network with the traditional particle swarm optimization (PSO) algorithm, a series of structure designs targeting the desired performance can be generated. This method significantly improves the efficiency and accuracy of the PSO algorithm by introducing neural networks, making it suitable for efficient inverse design and multi-structure analysis of AWG. • Combining BPNN and PSO for AWG inverse design. • Training the forward network to replace the electromagnetic simulation process significantly reduces design time. • The design requirements for AWG allow for both spectral response and desired performance. • This method enables the multi-solution design of AWG according to the desired performance. [ABSTRACT FROM AUTHOR]

Natural frequencies, which are relatively easy parameters to obtain, provide efficient information about the dynamic behavior of structures. Controlling these parameters can help to minimize the destructive effect of dynamic loads on structures. However, the optimal design of the size and shape of truss structures with controlling dynamic constraints is an expensive problem. While frequency constraints are nonlinear and non-convex, handling natural frequencies as constraints is a challenging task, which prevents the phenomenon of resonance, large deformations, and destruction of a structure. Also, reducing the vibration amplitude of a structure would reduce the stress and deflections. In this paper, the Improved Marine Predators Algorithm (IMPA) is proposed and implemented for size and shape optimization of truss structures subject to natural frequency constraints. In this method, a Novel CF (NCF) factor is introduced to control the predator's step size when looking for prey. To evaluate the performance of IMPA, the optimum weight of structures under dynamic constraints was computationally investigated. To demonstrate the efficiency and robustness of IMPA, 37-bar truss bridge, 52-bar dome, 72-bar space truss, 120-bar dome, 200-bar planar truss, and 600-bar dome truss were optimized. Compared to other state-of-the-art algorithms, the results indicate that IMPA performs better in solving these nonlinear structural optimization problems. • Marine Predators Algorithm Improved based on a new strategy. • A novel formula is proposed for CF factor. • engineering problems with dynamic constraints are investigated to prove the capabilities of new swarm algorithm. • The results are compared with state-of-the-art swarm algorithms. [ABSTRACT FROM AUTHOR]

Trajectory similarity analysis of moving target is the foundation for mining high-value and regular behavioral information such as motion preferences, activity hotspots and frequent paths. Unlike most trajectory similarity analysis methods aimed at discovering correlations of target activities in time, space or spatio-temporal domains, this paper focuses on the shape matching of target trajectories. If some specific shapes frequently appear in historical trajectories, extracting these local shapes would be beneficial for analyzing the target motion templates and behavior modes. Trajectory segments with similar shapes may not have spatio-temporal correlation, and the shapes also have geometric transformation characteristics such as rotation, scaling and translation. Since the existing trajectory similarity analysis methods cannot be directly applied, an algorithm for extracting similar segments based on shape matching is proposed. First, a new shape descriptor based on signed barycenter distance (SBD) is established. It describes a trajectory as a one-dimensional shape feature sequence, which has the advantage of low computational complexity. Then, the distributed nearest neighbor search strategy is used in the particle swarm optimization (PSO) method, which aims to accelerate the retrieval of trajectory segments with similar shapes and improve the matching accuracy. Experiments on MPEG-7, handwritten character and maneuvering target simulation trajectory data sets show that compared with the existing typical shape descriptors, SBD shape descriptor has advantages in accuracy and noise insensitivity, and the improved PSO method can efficiently and accurately obtain the local shape matching results. [ABSTRACT FROM AUTHOR]

Cloud data center comprises various physical and virtual machines, alongside storage datacenter services provided by cloud providers. Effectively mapping tasks to optimize resource utilization and load balance is essential for efficient task scheduling. This process, referred to as scheduling constraints, can significantly enhance overall efficiency. However, to harness the benefits of this scheduling, one must address the challenges arising during task execution. The interdependencies between tasks and the diverse resources available in the datacenter pose significant hurdles to efficient resource allocation. To address these challenges, this paper introduces the Hybrid HEFT-PSO-GA algorithm (HEPGA), aiming to efficiently allocate tasks to available resources across the datacenter. The HEPGA algorithm builds upon prior research by integrating the strengths of PSO (Particle Swarm Optimization) and GA (Genetic Algorithm) to optimize task scheduling in cloud computing environments. Through the fusion of PSO, GA, and HEFT-based Initialization, the algorithm strives to efficiently allocate tasks to processors, thereby minimizing the makespan. This approach capitalizes on parallel processing capabilities to further enhance resource utilization in the cloud environment. By varying the weights of the fitness function and considering the resources within the datacenter, we meticulously analyze the algorithm's performance concerning both makespan and Resource Utilization (RU). The results of these tests underscore the algorithm's consistent and robust resource utilization across diverse weight configurations, highlighting its adaptability to varying priorities. Moreover, the observed variations in makespan performance based on different weights emphasize the algorithm's potential for excellence when tailored to specific optimization goals. [ABSTRACT FROM AUTHOR]