Hierarchical Particle Swarm Optimization Research Articles

A Time-Critical Low Complexity Distributed Self Organizing Hierarchical Particle Swarm Optimization Algorithm for Wireless Sensor Networks

A Time-Critical Low Complexity Distributed Self Organizing Hierarchical Particle Swarm Optimization Algorithm for Wireless Sensor Networks

FinSafeNet: securing digital transactions using optimized deep learning and multi-kernel PCA(MKPCA) with Nyström approximation.

With the swift advancement of technology and growing popularity of internet in business and communication, cybersecurity posed a global threat. This research focuses new Deep Learning (DL) model referred as FinSafeNet to secure loose cash transactions over the digital banking channels. FinSafeNet is based on a Bi-Directional Long Short-Term Memory (Bi-LSTM), a Convolutional Neural Network (CNN) and an additional dual attention mechanism to study the transaction data and influence the observation of various security threats. One such aspect is, relying these databases in most of the cases imposes a great technical challenge towards effective real time transaction security. FinSafeNet draws attention to the attack and reproductive phases of Hierarchical Particle Swarm Optimization (HPSO) feature selection technique simulating it in a battle for extreme time performance called the Improved Snow-Lion optimization Algorithm (I-SLOA). Upon that, the model then applies the Multi-Kernel Principal Component Analysis (MKPCA) accompanied by Nyström Approximation for handling the MKPCA features. MKPCA seeks to analyze and understand a non-linear structure of data whereas, Nyström Approximation reduces the burden on computational power hence allows the model to work in situations where large sizes of datasets are available but with no loss of efficiency of the model. This causes FinSafeNet to work easily and still be able to make accurate forecasts. Besides tackling feature selection and dimensionality reduction, the model presents advanced correlation measures as well as Joint Mutual Information Maximization to enhance variable correlation analysis. These improvements further help the model to detect the relevant features in transaction data that may present a threat to the security of the system. When tested on commonly used database for testing banks performance, for instance the Paysim database, FinSafeNet significantly improves upon the previous and fundamental approaches, achieving accuracy of 97.8%.

Fuel constrained sustainable water-gas-heat-power generation expansion planning of isolated microgrid

On account of slowly reducing of fossil-fuel, profitable utilization of available fossil-fuel to produce electric power is a major concern for electric power producers. Here, short-period fuel constrained power, heat, natural gas and clean water augmentation planning (FCPHGWAP) of isolated microgrid considering carbon capture is presented. FCPHGWAP finds out the most economical and consistent augmentation plan to meet the prophesied power, heat, natural gas and clean water demand over a short-term time horizon whilst satisfying several technical as well as societal constraints. An archetypical system having extant diesel generators (DGs), PVT panel, wind turbine generator (WG), micro-cogeneration unit (MCU), battery energy storage system (BESS), plug-in electric vehicles (PEVs), thermal energy storage system, natural gas storage unit, carbon capture unit (CCU), electrolyzer, hydrogen storage unit and aspirant PVT panel, WGs, MCUs and PEVs is taken into consideration. Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients and grey wolf optimization are used to solve this FCPHGWAP problem. It is observed that net present value having fuel constraints is less than that of without fuel constraints.

Hybrid artificial neural network models for bearing capacity evaluation of a strip footing on sand based on Bolton failure criterion

This paper employs the Bolton failure criterion, incorporating strength-dilatancy relationships, to analyze the bearing capacity factor of a strip footing on dense sand. Utilizing finite element limit analysis (FELA) based on the lower and upper bound theorems, the study presents the results as average bound solutions. By using the Bolton model, the b parameter is first calibrated and found that it should be about 3.50 to align the ultimate bearing capacity (qu) from FELA to have a good agreement with that from experimental test results from previous studies. The influence of parameters relevant to the Bolton failure criterion is analysed, showing that an increase in relative density (DR) significantly affects the variation in the bearing capacity factor (Nγ) at higher Q values, while lower Q values inhibit dilatancy due to soil crushing. The width of the strip footing (B) has a decreasing effect on Nγ at higher Q values, and the unit weight (γ) changes minimally impact Nγ within the range of 16–22 kN/m3. Additionally, an increase in the critical state friction angle (ϕcv) consistently increases Nγ, highlighting its direct correlation with soil shear strength. A hybrid artificial neural network (ANN) model integrates machine learning with four optimization algorithms: Imperialist Competitive Algorithm (ICA), Ant Lion Optimization (ALO), Teaching Learning Based Optimization (TLBO), and New Self-Organizing Hierarchical Particle Swarm Optimizer with Jumping Time-Varying Acceleration Coefficients (NHPSO-JTVAC). Comparative rank analysis of hybrid ANN models based on the selection of the optimal number of hidden neurons demonstrates that the ANN-TLBO model excels in predicting the bearing capacity factor, achieving a score of 48. This conclusion is corroborated by an error heatmap matrix, which indicates a minimized percentage of error relative to other hybrid ANN models. Importance analysis identifies particle crushing strength (Q) as the most significant factor influencing the bearing capacity factor (Nγ).

3D position deployment and performance optimization of mmWave UAV-assisted HetIoT under jamming condition

Heterogeneous Internet of Things (HetIoT) has received widespread attention due to its provision of various convenient services in fields such as smart cities, intelligent transportation, environmental monitoring and security systems. Due to HetIoT inherently demands high data rates, bandwidth, and low latency, the application of millimeter-wave (mmWave) unmanned aerial vehicle (UAV) as emergency aerial base station (ABS) providing services to HetIoT users has become a low-cost and efficient means. However, due to the sensitivity of mmWave to obstacles and jamming, guaranteeing network performance has become a pressing issue. This paper considers a mmWave UAV-assisted HetIoT under jamming conditions, where auxiliary ABSs serve multiple ground users (GUs) who generate a large amount of sensor data. We establish a coverage maximization problem under the constraints of signal-to-interference ratio (SIR) threshold, maximum power of ABSs and maximum number of GUs that the base station can serve, and propose a novel ABS hovering deployment algorithm M-HiAPSO that combines the artificial potential field (APF) method and the improved particle swarm optimization (PSO) algorithm in a hierarchical manner. Specifically, the multi-element APF method is used to characterize the horizontal force between nodes, combined with the improved hierarchical adaptive PSO algorithm to adjust the horizontal position of the ABS to obtain the optimal UAV hovering position and power allocation strategy. Numerical results show that the coverage rate reached 96.2% when the number of iterations was 283, and it can reach up to 99.6%.

Optimized Convolutional Neural Network Using Hierarchical Particle Swarm Optimization for Sensor Based Human Activity Recognition

Hyperparameter optimization poses a significant challenge when developing deep neural networks. Building a convolutional neural network (CNN) for implementation can be an arduous and time-intensive task. This work proposed an approach to optimize the hyperparameters of one dimensional (1D-CNN) to improve the accuracy of human activity recognition (HAR). The framework includes a parametric depiction of 1D-CNNs along with an optimization process for hyperparameters aimed at maximizing the model's performance. This work designed the method called OPTConvNet for hyperparameter optimization of 1D-CNN using Hierarchical Particle Swarm Optimization (H-PSO). The H-PSO algorithm is designed to optimize the architectural, layer and training parameters of 1D-CNN. The H-PSO optimizes the architecture of the 1D-CNN at initial level. Layer and training hyperparameters will be optimized at the next level. The proposed approach employs an exponential-like inertia weight to fine-tune the balance between exploration and exploitation of particles to prevent premature convergence to a local optimum solution in the PSO algorithm. The H-PSO- CNN is evaluated on publicly available sensor- human activity recognition (S-HAR) datasets namely, UCI-HAR, Daphnet Gait, Opportunity and PAMPA2 datasets.

Wireless Power Transfer System for the Intelligent Video Monitoring and Analysis Based on the Safety of Power Grid Construction Site

This study describes a power grid construction site surveillance system that includes wireless power transmission and an improved Yolo V3 detection model. An artificial intelligence and wireless power transfer methodology is employed used for continuous monitoring. Improved Yolo V3 detection is optimized using Hierarchical Particle Swarm Optimization (HPSO) for enhanced efficacy in detecting power grid development threats. The methodology is compared to existing methods in terms of precision, accuracy, recall, and speed. The findings indicate that the proposed method is achieved a real-time and accurate power grid development site monitoring. Experiments on test sets in various situations improved the detection accuracy, and indicate that the proposed technique is quite robust. Also the detection accuracy and speed of the proposed method have improved over existing approaches, indicate that the strategy provides superior detection efficacy. This method considerably improves risk source detection, that's critical for assuring safety on power grid building sites.

Applying Particle Swarm Optimization for Investigating the Stability of DC-DC Buck-Boost Converter: A Software Based Approach

ABSTRACT Stability of power converters has always been a major concern when dealing with nonlinear behavior of the system and swarm-based optimization techniques are proven to be handy as they are widely employed. Hence, this study presents an investigative approach to explore the compatibility of applying different variants of Particle Swarm Optimization (PSO) to design an optimized PID controller for DC-DC Buck-Boost converter. This work assesses converter stability through optimization of the PID controller using classical PSO (cPSO), Time-Varying Inertia Weight PSO (TVIW-PSO), Chaotic Descending Inertia Weight PSO (CDIW-PSO), Self-organizing Hierarchical PSO (HPSO) and Random inertia weight PSO (RIW-PSO). The State Space Average (SSA) method is utilized to model and attain the transfer function; eigenvalue analysis and time domain simulation analysis are considered to investigate the stability. Moreover, four objective functions and various performance parameters such as percentage of overshoot, rise time, settling time, and peak amplitude are tabulated to assess the system’s efficiency and the outcomes of different approaches are compared extensively. Amongst all, CDIW-PSO showcased better performances e.g. overshoot of 1.08%, settling time 0.000228s and rise time 0.000143s. The findings illustrate algorithm’s capacity to reduce transient and steady-state faults and makes it compatible with more nonlinear applications.

Hybrid Hierarchical Particle Swarm Optimization with Evolutionary Artificial Bee Colony Algorithm for Task Scheduling in Cloud Computing

Hybrid Hierarchical Particle Swarm Optimization with Evolutionary Artificial Bee Colony Algorithm for Task Scheduling in Cloud Computing

Optimum dynamic dispatch of clean water, methane, electricity and heat considering carbon capture

This paper suggests a novel clean water, natural gas, heat and power generation system using wind turbine generators, PVT panels, bioenergy power plants, micro-hydro power plants, micro-cogeneration units (MCUs), battery energy storage system, thermal energy storage system and plug-in electric vehicles (PEVs). In this system, a power to gas technology consisting of a carbon capture unit, an electrolyzer, a hydrogen storage unit, and a methanation process, is used to supply the natural gas demand using CO2 captured from the MCUs and the hydrogen generated by the clean water electrolyzer. Furthermore, the electricity is consumed by a desalination unit to procure the clean water demand, carbon captured unit (CCU), electrolyzer water inflow, and fill up the water storage tank. At peak clean water demand periods, the excess water is pumped from the storage tank to the consumption side. Demand response program (DRP) is used for levelling the demand. Here, total cost for water-gas-heat-power nexus model throughout the day with and without DRP for three different scenarios is minimized by using quasi-oppositional fast convergence evolutionary programming, fast convergence evolutionary programming, self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients and differential evolution. It is seen from numerical results that cost attained with DRP is less than the cost attained without DRP.

Effective hybrid search technique based constraint mixed-integer programming for smart home residential load scheduling

In this paper, the problem of scheduling smart homes (SHs) residential loads is considered aiming to minimize electricity bills and enhance the user comfort. The problem is addressed as a multi-objective constraint mixed-integer optimization problem (CP-MIP) to model the constrained load operation. As the CP-MIP optimization problem is non-convex, a novel hybrid search technique, that combines the Relaxation and Rounding (RnR) approach and metaheuristic algorithms to enhance the accuracy and relevance of decision variables, is proposed. This search technique is implemented through two stages: the relaxation stage in which a metaheuristic technique is applied to get the optimal rational solution of the problem. Whereas, the second stage is the rounding process which is applied via stochastic rounding approach to provide a good-enough feasible solution. The scheduling process has been done under time-of-use (ToU) dynamic electricity pricing scheme and two powering modes (i.e., powering from the main grid only or powering from a grid-tied photovoltaic (PV) residential power system), in addition, four metaheuristics [i.e., Binary Particle Swarm Optimization (BPSO), Self-Organizing Hierarchical PSO (SOH-PSO), JAYA algorithm, and Comprehensive Learning JAYA algorithm (CL-JAYA)] have been utilized. The results reported in this study verify the effectiveness of the proposed technique. In the 1st powering mode, the electricity bill reduction reaches 19.4% and 20.0% when applying the modified metaheuristics, i.e. SOH-PSO and CL-JAYA, respectively, while reaches 56.1%, and 54.7% respectively in the 2nd powering scenario. In addition, CL-JAYA superiority is also observed with regard to the user comfort.

Reinforcement Learning-Based Particle Swarm Optimization for End-to-End Traffic Scheduling in TSN-5G Networks

With the rapid development of the Industrial Internet of Things (IIoT), massive IIoT devices connect to industrial networks via wired and wireless. Furthermore, industrial networks pose new requirements on communications, such as strict latency boundaries, ultra-reliable transmission, and so on. To this end, time-sensitive networking (TSN) embedded fifth-generation (5G) wireless communication technology (i.e., TSN-5G networks), is considered the most promising solution to address these challenges. TSN can provide deterministic end-to-end latency and reliability for real-time applications in wired networks. 5G supports ultra-reliable and low-latency communications (uRLLC), providing increased flexibility and inherent mobility support in the wireless network. Thus, the integration of TSN and 5G provides numerous benefits, including increased flexibility, lower commissioning costs, and seamless interoperability of various devices, regardless of whether they use a wired or wireless interface. Nonetheless, the potential barriers between the TSN and 5G systems, such as clock synchronization and end-to-end traffic scheduling, are inevitable. Time synchronization has been studied in many works, so this paper focuses on the end-to-end traffic scheduling problem in TSN-5G networks. We propose a novel integrated TSN and 5G industrial network architecture, where the 5G system acts as a logical TSN-capable bridge. Based on this network architecture, we design a Double Q-learning based hierarchical particle swarm optimization algorithm (DQHPSO) to search for the optimal scheduling solution. The DQHPSO algorithm adopts a level-based population structure and introduces Double Q-learning to adjust the number of levels in the population, which evades the local optimum to further improve the search efficiency. Extensive simulations demonstrate that the DQHPSO algorithm can increase the scheduling success ratio of time-triggered flows compared to other algorithms.

Dynamic Optimal Power Flow for Grid-Connected Multi-Microgrid System considering Outage of Energy Sources

Recently multi-microgrid (MMG) system has attained additional attention because of significant assimilation of renewable energy sources, enhanced efficacy, reliability and stability through harmonization as well as cooperation of power swap amongst the microgrids (MGs). In this manuscript, dynamic optimal power flow (DOPF) for grid-connected MMG integrated demand response program (DRP) with and without energy source’s outage is presented. Total cost over the entire time-period considering instinctive characteristics of different phases is minimized. Power swap between grid and each MG is bidirectional. Each MG comprises small hydro power plants (SHPPs), solar PV plants (SPVPs), wind turbine generators (WTGs), bioenergy power plants (BPPs) and plug-in electric vehicles (PEVs). MMG used here comprises MG 1 including IEEE 33-bus system, MG 2 including 15-bus system and MG 3 including IEEE 69-bus system. This DOPF problem is solved by using quasi-oppositional fast convergence real coded genetic algorithm (QOFCRCGA), self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients (HPSO-TVAC), differential evolution (DE), artificial bee colony (ABC) optimization, fast convergence real coded genetic algorithm (FCRCGA) and real coded genetic algorithm (RCGA).

Multicounty fuel constraint‐based dynamic economic dispatch problem using horse herd optimization

AbstractDue to the diminution of fossil fuels, the profitable usage of fuel for the generation of power is reducing. It is an extremely vital concern for power generation companies. The fossil fuel‐based power plant must operate within its fuel limits and contractual restrictions. Therefore, economic optimization is required to optimize the fuel cost for generation under fuel constraints. To address this issue, in this work, a horse herd optimization algorithm (HOA) is developed to solve multicounty fuel‐constrained dynamic economic dispatch with demand‐side management incorporating wind turbine generators, solar photovoltaic plants, and pumped hydro storage plants. The efficiency of the suggested HOA algorithm has been revealed on a selected test system. The HOA technique shows better convergence as compared with the other methods. Moreover, the computation time requirement of the HOA technique is also lower than that of other compared methods. From numerical results, it is observed that the consumption of fuel is sufficiently high if constraints are incorporated into the problem. Simulation results are matched up to those obtained from hierarchical particle swarm optimization with time‐varying acceleration coefficients, fast convergence evolutionary programming, and differential evolution. After comparison, it is observed that the recommended HOA offers a better‐quality solution.

Scenario-based fuel-constrained heat and power scheduling of a remote microgrid

This manuscript suggests elephant clan optimization (ECO) algorithm to solve heat and electric power scheduling of a remote microgrid (MG) for three different scenarios considering fuel constraints. ECO algorithm is a populace-based method motivated by the elephants’ behaviour and their societal organization. MG comprises diesel generators (DGs), small hydro power plants (SHPPs), wind turbine generators (WTGs), solar micro-cogeneration (SMC) unit, biomass-fuel-fired micro-cogeneration (BMC) unit, battery energy storage system (BESS), plug-in electrical vehicles (PEVs) and thermal energy storage system (TESS). BMC units and SMC units are incorporated alternately into the MG. Numerical results of a typical system are compared with those obtained from self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients (HPSO-TVAC) and grey wolf optimization (GWO). It is seen from the evaluation that ECO offers superior solution.

Optimal day-ahead scheduling of renewable energy-based virtual power plant considering electrical, thermal and cooling energy

For synchronized functioning of distributed renewable energy resources, virtual power plant (VPP) is manifested as an upshot for maximizing net profit of the partakers taking into account uncertainties. Here, optimal day-ahead scheduling for VPP has been performed for meeting electrical, thermal and cooling demands. VPP comprises small hydro power plants, wind turbine generators, solar micro-cogeneration units, biomass-fuel-fired micro-cogeneration units, battery energy storage system, plug-in electric vehicles, electric chiller, absorption chiller, ice storage conditioner and thermal energy storage system. SMC units and BMC units are incorporated alternately. Demand response program is applied for electric, thermal and cooling demands to flatten demand curves and to improve system flexibility and decrease cost. The scheduling problem is solved by utilizing bottlenose dolphin optimizer (BDO), self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients (HPSO-TVAC) and grey wolf (GWO) optimization so as to maximize net profit of VPP operator. The maximum profit obtained from BDO is about 5.25 % and 9.82 % more than the maximum profit obtained from HPSO-TVAC and GWO respectively.

Two-Stage Data-Driven Evolutionary Optimization for High-Dimensional Expensive Problems.

Surrogate-assisted evolutionary algorithms (SAEAs) have been widely used for solving complex and computationally expensive optimization problems. However, most of the existing algorithms converge slowly in the later stage. This article proposes a novel two-stage data-driven evolutionary optimization (TS-DDEO) that meets the requirements of early exploration and later exploitation. In the first stage, a surrogate-assisted hierarchical particle swarm optimization method is used to find a promising area from the entire search space. In the second stage, we propose a best-data-driven optimization (BDDO) method with a strong exploitation ability to accelerate the optimization process. BDDO has a real-time update mechanism for the surrogate model and population and uses a predefined number of ranking-top solutions to update population and surrogates. BDDO combines three surrogate-assisted evolutionary sampling strategies: 1) surrogate-assisted differential evolution sampling; 2) surrogate-assisted local search; and 3) a surrogate-assisted full-crossover (FC) strategy which is proposed to integrate existing best genotypes in the population. Experiments and analysis have validated the effectiveness of the two-stage framework, the BDDO method, and the FC strategy. Moreover, the proposed algorithm is compared with five state-of-the-art SAEAs on high-dimensional benchmark functions. The result shows that TS-DDEO performs better both in effectiveness and robustness.

A coupled deep learning approach for shield moving performance prediction of underground tunnel construction

Recently, deep learning methods have attracted great attentions for geotechnical engineering, especially for the safety construction of underground tunnels. In this regard, the control and regulation of operational parameters are vital for safety and intelligent tunnel construction. In this study, hybrid data-driven models on the basic of optimization approaches [particle swarm optimization (PSO), multilayer hierarchical heterogeneous PSO] and deep learning methods (long short-term memory and gated recurrent unit neural network) were developed. The optimization approaches were utilized to obtain the optimal parameters of deep learning methods. Apart from that, geological data sets were extended to match the scale of operational data sets using Kriging method, which considered the effects of local geology comprehensively. The developed hybrid models (DHMs) were utilized to estimate the speed of earth pressure balance (EPB) shield machine for a tunnel project in China. Results indicate that errors in estimated results by DHMs are acceptable in geotechnical engineering. Besides, the DHMs exist potentials to cope with time-series data sets from underground tunnel project. After that, Pearson correlation coefficient was used to evaluate the relative importance of influential factors on target variable. Results show that penetration has significant correlation relationship with excavation speed. The geological and operational parameters are equally important for the prediction performance of DHMs. The developed DHMs can provide a reference for regulating shield moving performance and attaining safety construction of underground tunnels.

Frequency control scheme of an AC Islanded microgrid based on modified new self-organizing hierarchical PSO with jumping time-varying acceleration coefficients

Frequency control scheme of an AC Islanded microgrid based on modified new self-organizing hierarchical PSO with jumping time-varying acceleration coefficients

Dynamic optimal power flow for isolated microgrid incorporating renewable energy sources

Enhanced investiture to distribution generation (DG) had moved for alteration of existing networks for developing microgrid which can function autonomously or together with grid. Dynamic optimal power flow (DOPF) for isolated microgrid incorporating renewable energy sources has been studied here. The DOPF problem has been solved by using elephant clan optimization (ECO) algorithm, self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients (HPSO-TVAC) and grey wolf optimization (GWO). The IEEE 33-bus system, IEEE 69-bus system, IEEE 118-bus system and real existing isolated microgrid of 15-bus system have been used for validation. In all the four test systems, the cost obtained from ECO is the lowest among all the three techniques.