Ant Lion Optimization Algorithm for Renewable Distributed Generations

An enhanced jellyfish search optimizer for stochastic energy management of multi-microgrids with wind turbines, biomass and PV generation systems considering uncertainty

The energy management (EM) solution of the multi-microgrids (MMGs) is a crucial task to provide more flexibility, reliability, and economic benefits. However, the energy management (EM) of the MMGs became a complex and strenuous task with high penetration of renewable energy resources due to the stochastic nature of these resources along with the load fluctuations. In this regard, this paper aims to solve the EM problem of the MMGs with the optimal inclusion of photovoltaic (PV) systems, wind turbines (WTs), and biomass systems. In this regard, this paper proposed an enhanced Jellyfish Search Optimizer (EJSO) for solving the EM of MMGs for the 85-bus MMGS system to minimize the total cost, and the system performance improvement concurrently. The proposed algorithm is based on the Weibull Flight Motion (WFM) and the Fitness Distance Balance (FDB) mechanisms to tackle the stagnation problem of the conventional JSO technique. The performance of the EJSO is tested on standard and CEC 2019 benchmark functions and the obtained results are compared to optimization techniques. As per the obtained results, EJSO is a powerful method for solving the EM compared to other optimization method like Sand Cat Swarm Optimization (SCSO), Dandelion Optimizer (DO), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), and the standard Jellyfish Search Optimizer (JSO). The obtained results reveal that the EM solution by the suggested EJSO can reduce the cost by 44.75% while the system voltage profile and stability are enhanced by 40.8% and 10.56%, respectively.

Optimal Allocation and Size of Renewable Energy Sources as Distributed Generations Using Shark Optimization Algorithm in Radial Distribution Systems

The need for energy has significantly increased in the world in recent years. Various research works were presented to develop Renewable Energy Sources (RESs) as green energy Distributed Generations (DGs) to satisfy this demand. In addition, alleviating environmental problems caused by utilizing conventional power plants is diminished by these renewable sources. The optimal location and size of the DG-RESs significantly affect the performance of Radial Distribution Systems (RDSs) through the fine bus voltage profile, senior power quality, low power losses, and high efficiency. This paper investigates the use of PV (photovoltaic) and (Wind Turbine) WT systems as a DG source in RDSs. This investigation is presented via the optimal location and size of the PV and WT systems, which are the most used DG sources. This optimization problem aims to maximize system efficiency by minimizing power losses and improving both voltage profile and power quality using White Shark Optimization (WSO). This algorithm emulates the attitude of great white sharks when foraging using their senses of hearing and smell. It confirms the balance between exploration and exploitation to discover optimization that is considered as the main advantage of this approach in attaining the global minimum. To assess the suggested approach, three common RDSs are utilized, namely, IEEE 33, 69, and 85 node systems. The results prove that the applied WSO approach can find the best location and size of the RESs to reduce power loss, ameliorate the voltage profile, and outlast other recent strategies. Adding more units provides a high percentage of reducing losses by at least 93.52% in case of WTs, rather than 52.267% in the case of PVs. Additionally, the annual saving increased to USD 74,371.97, USD 82,127.257, and USD 86,731.16 with PV penetration, while it reached USD 104,872.96, USD 116,136.57, and USD 155,184.893 with WT penetration for the 33, 69, and 85 nodes, respectively. In addition, a considerable enhancement in the voltage profiles with the growth of PV and WT units was confirmed. The ability of the suggested WSO for feasible implementation was validated and inspected by preserving the restrictions and working constraints.

Analysis of transmission line modeling routines by using offsets measured least squares regression ant lion optimizer in ORPD and ELD problems

This paper proposed an offset measured least regression based ALO to solve ORPD and ELD problems of IEEE 57 bus system designed with different transmission line models. These two problems are highly non-linear and non-convex defiance optimization of problem. The solution of ALO depends on exploration and exploitation if the difference between local and global variables is large, therefore chance to miss the best optimal solution. The weighted elitism phase of the algorithm gives diversified results because exploration is more biased toward elite particles. Which is due to decreasing of random walk to achieve the convergence characteristics. The proposed LSR-EALO can balance both exploration and exploitation, which improves the solution of optimization problem. Simulation is performed with proposed method on different IEEE 57 bus power system models, such as the positive sequence, 3-Phase PI, and distributed CP transmission lines based power systems, and lumped PI lines based low voltage hardware model (LVHM). In this paper, the ORPD problem was used to describe control variables like generator voltage, tap changers of transformers, and switching of capacitor banks subjected to power loss minimization function. Also, described voltage deviation and voltage stability index. Similarly, the ELD was described the active power allocation among generators to meet the sum of load demand and losses in the systems at minimum fuel cost function. And in depth analysis of the optimization results shows accuracy of control variables in ORPD and ELD problems. Also, the effectiveness of proposed method was also verified by comparing results with other meta heuristic algorithms.

Techno-Economic Evaluation of Optimal Integration of PV Based DG with DSTATCOM Functionality with Solar Irradiance and Loading Variations

Nowadays, the trend of countries and their electrical sectors moves towards the inclusion of renewable distributed generators (RDGs) to diminish the use of the fossil fuel based DGs. The solar photovoltaic-based DG (PV-DG) is widely used as a clean and sustainable energy resource. Determining the best placements and ratings of the PV-DG is a significant task for the electrical systems to assess the PV-DG potentials. With the capability of the PV-DG inverters to inject the required reactive power in to the system during the night period or during cloudy weather adds the static compensation (STATCOM) functionality to the PV unit, which is being known as distributed static compensator (DSTATCOM). In the literature, there is a research gap relating the optimal allocation of the PV-DGs along with the seasonal variation of the solar irradiance. Therefore, the aim of this paper is to determine the optimal allocation and sizing of the PV-DGs along with the optimal injected reactive power by their inverters. An efficient optimization technique called Gorilla troop’s optimizer (GTO) is used to solve the optimal allocation problem of the PV-DGs with DSTATCOM functionality on a 94 bus distribution network. Three objective functions are used as a multi-objective function, including the total annual cost, the system voltage deviations, and the system stability. The simulation results show that integration of PV-DGs with the DSTATCOM functionality show the superiorities of reducing the total system cost and considerably enhancing system performance in voltages deviations and system stability compared to inclusion of the PV-DGs without the DSTATCOM functionality. The optimal integration of the PV-DGs with DSTATCOM functionality can reduce the total cost and the voltage deviations by 15.05% and 77.05%, respectively. While the total voltage stability is enhanced by 25.43% compared to the base case.

A Novel Approach Based on Honey Badger Algorithm for Optimal Allocation of Multiple DG and Capacitor in Radial Distribution Networks Considering Power Loss Sensitivity

Recently, the integration of distributed generators (DGs) in radial distribution systems (RDS) has been widely evolving due to its sustainability and lack of pollution. This study presents an efficient optimization technique named the honey badger algorithm (HBA) for specifying the optimum size and location of capacitors and different types of DGs to minimize the total active power loss of the network. The Combined Power Loss Sensitivity (CPLS) factor is deployed with the HBA to accelerate the estimation process by specifying the candidate buses for optimal placement of DGs and capacitors in RDS. The performance of the optimization algorithm is demonstrated through the application to the IEEE 69-bus standard RDS with different scenarios: DG Type-I, DG Type-III, and capacitor banks (CBs). Furthermore, the effects of simultaneously integrating single and multiple DG Type-I with DG Type-III are illustrated. The results obtained revealed the effectiveness of the HBA for optimizing the size and location of single and multiple DGs and CBs with a considerable decline in the system’s real power losses. Additionally, the results have been compared with those obtained by other known algorithms.

Multi-strategy improved parallel antlion algorithm and applied to feature selection

Antlion Optimization Algorithm (ALO) is a promising bionic swarm intelligence algorithm, which has good robustness and convergence, but there are still many areas to be improved and modified. Aiming at the fact that the ALO algorithm is more likely to fall into the local optimum, proposes three strategies to improve the classic ALO algorithm in this paper. First of all, we adopt a parallel idea in the algorithm, through the communication strategy between groups based on Quantum-Behaved to enhance the diversity of the population. Secondly, we adopted two strategies, Opposition Learning, and Gaussian Mutation, to balance the performance of exploration and exploitation during the execution of the algorithm, further formed the MSALO algorithm. The CEC2013 Benchmark function is selected as the standard, and MSALO is compared with other intelligent optimization algorithms. The experimental results show that MSALO has stronger optimization performance compared with other intelligent algorithms. Besides, we applied MSALO to the practical scenarios of feature selection, and use SVM classifiers as training evaluators to improve the accuracy of feature extraction from high-dimensional data.

A Mahalanobis Surrogate-Assisted Ant Lion Optimization and Its Application in 3D Coverage of Wireless Sensor Networks

Metaheuristic algorithms are widely employed in modern engineering applications because they do not need to have the ability to study the objective function’s features. However, these algorithms may spend minutes to hours or even days to acquire one solution. This paper presents a novel efficient Mahalanobis sampling surrogate model assisting Ant Lion optimization algorithm to address this problem. For expensive calculation problems, the optimization effect goes even further by using MSAALO. This model includes three surrogate models: the global model, Mahalanobis sampling surrogate model, and local surrogate model. Mahalanobis distance can also exclude the interference correlations of variables. In the Mahalanobis distance sampling model, the distance between each ant and the others could be calculated. Additionally, the algorithm sorts the average length of all ants. Then, the algorithm selects some samples to train the model from these Mahalanobis distance samples. Seven benchmark functions with various characteristics are chosen to testify to the effectiveness of this algorithm. The validation results of seven benchmark functions demonstrate that the algorithm is more competitive than other algorithms. The simulation results based on different radii and nodes show that MSAALO improves the average coverage by 2.122% and 1.718%, respectively.

Application of the Multiverse Optimization Method to Solve the Optimal Power Flow Problem in Alternating Current Networks

In this paper, we solve the optimal power flow problem in alternating current networks to reduce power losses. For that purpose, we propose a master–slave methodology that combines the multiverse optimization algorithm (master stage) and the power flow method for alternating current networks based on successive approximation (slave stage). The master stage determines the level of active power to be injected by each distributed generator in the network, and the slave stage evaluates the impact of the proposed solution on each distributed generator in terms of the objective function and the constraints. For the simulations, we used the 10-, 33-, and 69-node radial test systems and the 10-node mesh test system with three levels of distributed generation penetration: 20%, 40%, and 60% of the power provided by the slack generator in a scenario without DGs. In order to validate the robustness and convergence of the proposed optimization algorithm, we compared it with four other optimization methods that have been reported in the specialized literature to solve the problem addressed here: Particle Swarm Optimization, the Continuous Genetic Algorithm, the Black Hole Optimization algorithm, and the Ant Lion Optimization algorithm. The results obtained demonstrate that the proposed master–slave methodology can find the best solution (in terms of power loss reduction, repeatability, and technical conditions) for networks of any size while offering excellent performance in terms of computation time.

A Novel Model for Landslide Displacement Prediction Based on EDR Selection and Multi-Swarm Intelligence Optimization Algorithm

With the widespread application of machine learning methods, the continuous improvement of forecast accuracy has become an important task, which is especially crucial for landslide displacement predictions. This study aimed to propose a novel prediction model to improve accuracy in landslide prediction, based on the combination of multiple new algorithms. The proposed new method includes three parts: data preparation, multi-swarm intelligence (MSI) optimization, and displacement prediction. In the data preparation, the complete ensemble empirical mode decomposition (CEEMD) is adopted to separate the trend and periodic displacements from the observed cumulative landslide displacement. The frequency component and residual component of reconstructed inducing factors that related to landslide movements are also extracted by the CEEMD and t-test, and then picked out with edit distance on real sequence (EDR) as input variables for the support vector regression (SVR) model. MSI optimization algorithms are used to optimize the SVR model in the MSI optimization; thus, six predictions models can be obtained that can be used in the displacement prediction part. Finally, the trend and periodic displacements are predicted by six optimized SVR models, respectively. The trend displacement and periodic displacement with the highest prediction accuracy are added and regarded as the final prediction result. The case study of the Shiliushubao landslide shows that the prediction results match the observed data well with an improvement in the aspect of average relative error, which indicates that the proposed model can predict landslide displacements with high precision, even when the displacements are characterized by stepped curves that under the influence of multiple time-varying factors.

Optimal Incorporation of Photovoltaic Energy and Battery Energy Storage Systems in Distribution Networks Considering Uncertainties of Demand and Generation

In this paper, the Archimedes optimization algorithm (AOA) is applied as a recent metaheuristic optimization algorithm to reduce energy losses and capture the size of incorporating a battery energy storage system (BESS) and photovoltaics (PV) within a distribution system. AOA is designed with revelation from Archimedes’ principle, an impressive physics law. AOA mimics the attitude of buoyant force applied upward on an object, partially or entirely dipped in liquid, which is relative to the weight of the dislodged liquid. Furthermore, the developed algorithm is evolved for sizing several PVs and BESSs considering the changing demand over time and the probability generation. The studied IEEE 69-bus distribution network system has different types of the load, such as residential, industrial, and commercial loads. The simulation results indicate the robustness of the proposed algorithm for computing the best size of multiple PVs and BESSs with a significant reduction in the power system losses. Additionally, the AOA algorithm has an efficient balancing between the exploration and exploitation phases to avoid the local solutions and go to the best global solutions, compared with other studied algorithms.

Developing a Hybrid Optimization Algorithm for Optimal Allocation of Renewable DGs in Distribution Network

Distributed generation (DG) is becoming a prominent key spot for research in recent years because it can be utilized in emergency/reserve plans for power systems and power quality improvement issues, besides its drastic impact on the environment as a greenhouse gas (GHG) reducer. For maximizing the benefits from such technology, it is crucial to identify the best size and location for DG that achieves the required goal of installing it. This paper presents an investigation of the optimized allocation of DG in different modes using a proposed hybrid technique, the tunicate swarm algorithm/sine-cosine algorithm (TSA/SCA). This investigation is performed on an IEEE-69 Radial Distribution System (RDS), where the impact of such allocation on the system is evaluated by NEPLAN software.

Multi-Modal Evolutionary Deep Learning Model for Ovarian Cancer Diagnosis

Ovarian cancer (OC) is a common reason for mortality among women. Deep learning has recently proven better performance in predicting OC stages and subtypes. However, most of the state-of-the-art deep learning models employ single modality data, which may afford low-level performance due to insufficient representation of important OC characteristics. Furthermore, these deep learning models still lack to the optimization of the model construction, which requires high computational cost to train and deploy them. In this work, a hybrid evolutionary deep learning model, using multi-modal data, is proposed. The established multi-modal fusion framework amalgamates gene modality alongside with histopathological image modality. Based on the different states and forms of each modality, we set up deep feature extraction network, respectively. This includes a predictive antlion-optimized long-short-term-memory model to process gene longitudinal data. Another predictive antlion-optimized convolutional neural network model is included to process histopathology images. The topology of each customized feature network is automatically set by the antlion optimization algorithm to make it realize better performance. After that the output from the two improved networks is fused based upon weighted linear aggregation. The deep fused features are finally used to predict OC stage. A number of assessment indicators was used to compare the proposed model to other nine multi-modal fusion models constructed using distinct evolutionary algorithms. This was conducted using a benchmark for OC and two benchmarks for breast and lung cancers. The results reveal that the proposed model is more precise and accurate in diagnosing OC and the other cancers.

Test scheduling of System-on-Chip using Dragonfly and Ant Lion optimization algorithms

Test scheduling of System-on-Chip (SoC) is a major problem solved by various optimization techniques to minimize the cost and testing time. In this paper, we propose the application of Dragonfly and Ant Lion Optimization algorithms to minimize the test cost and test time of SoC. The swarm behavior of dragonfly and hunting behavior of Ant Lion optimization methods are used to optimize the scheduling time in the benchmark circuits. The proposed algorithms are tested on p22810 and d695 ITC’02 SoC benchmark circuits. The results of the proposed algorithms are compared with other algorithms like Ant Colony Optimization, Modified Ant Colony Optimization, Artificial Bee Colony, Modified Artificial Bee Colony, Firefly, Modified Firefly, and BAT algorithms to highlight the benefits of test time minimization. It is observed that the test time obtained for Dragonfly and Ant Lion optimization algorithms is 0.013188 Sec for D695, 0.013515 Sec for P22810, and 0.013432 Sec for D695, 0.013711 Sec for P22810 respectively with TAM Width of 64, which is less as compared to the other well-known optimization algorithms.

Multi-Objective Energy Management of a Micro-Grid Considering Stochastic Nature of Load and Renewable Energy Resources

Optimal inclusion of a photovoltaic system and wind energy resources in electrical grids is a strenuous task due to the continuous variation of their output powers and stochastic nature. Thus, it is mandatory to consider the variations of the Renewable energy resources (RERs) for efficient energy management in the electric system. The aim of the paper is to solve the energy management of a micro-grid (MG) connected to the main power system considering the variations of load demand, photovoltaic (PV), and wind turbine (WT) under deterministic and probabilistic conditions. The energy management problem is solved using an efficient algorithm, namely equilibrium optimizer (EO), for a multi-objective function which includes cost minimization, voltage profile improvement, and voltage stability improvement. The simulation results reveal that the optimal installation of a grid-connected PV unit and WT can considerably reduce the total cost and enhance system performance. In addition to that, EO is superior to both whale optimization algorithm (WOA) and sine cosine algorithm (SCA) in terms of the reported objective function.

Reliability-Aware Multipath Routing of Time-Triggered Traffic in Time-Sensitive Networks

With the development of industrial networks, the demands for strict timing requirements and high reliability in transmission become more essential, which promote the establishment of a Time-Sensitive Network (TSN). TSN is a set of standards with the intention of extending Ethernet for safety-critical and real-time applications. In general, frame replication is used to achieve fault-tolerance, while the increased load has a negative effect on the schedule synthesis phase. It is necessary to consider schedulability and reliability jointly. In this paper, a heuristic-based routing method is proposed to achieve fault tolerance by spatial redundancy for TSNs containing unreliable links. A cost function is presented to evaluate each routing set, and a heuristic algorithm is applied to find the solution with higher schedulability. Compared to the shortest path routing, our method can improve the reliability and the success rate of no-wait scheduling by 5–15% depending on the scale of topology.

Dynamic Characteristics of a Gear System with Double-Teeth Spalling Fault and Its Fault Feature Analysis

Tooth spalling is one of the most destructive surface failure models of the gear faults. Previous studies have mainly concentrated on the spalling damage of a single gear tooth, but the spalling distributed over double teeth, which usually occurs in practical engineering problems, is rarely reported. To remedy this deficiency, this paper constructs a new dynamical model of a gear system with double-teeth spalling fault and validates this model with various experimental tests. The dynamic characteristics of gear systems are obtained by considering the excitations induced by the number of spalling teeth, and the relative position of two faulty teeth. Moreover, to ensure the accuracy of dynamic model verification results and reduce the difficulty of fault feature analysis, a novel parameter-adaptive variational mode decomposition (VMD) method based on the ant lion optimization (ALO) is proposed to eliminate the background noise from the experimental signal. First, the ALO is used for the self-selection of the decomposition number K and the penalty factor â of the VMD. Then, the raw signal is decomposed into a set of Intrinsic Mode Functions (IMFs) by applying the ALO-VMD, and the IMFs whose effective weight kurtosis (EWK) is greater than zero are selected as the reconstructed signal. Combined with envelope spectrum analysis, the de-nosing ability of the proposed method is compared with that of the method known as particle swarm optimization-based variational mode decomposition (PSO-VMD), the fixed-parameter VMD, the empirical mode decomposition (EMD), and the local mean decomposition (LMD), respectively. The results indicate that the proposed dynamic model and background elimination method can provide a theoretical basis for spalling defect diagnosis of gear systems.

Comparative Performance Evaluation of Fractional Order PID Controller for Heat Flow System Using Evolutionary Algorithms

The main objective of the paper is to design of Fractional order PID (FOPID) controller for heat flow system using evolutionary algorithms. The recent developed metaheuristic algorithms such as Ant Lion Optimizer (ALO), Grey Wolf Optimizer (GWO) and Moth Flame Optimizer (MFO) are used in the FOPID design. The FOPID design for heat flow system is formulated as an optimization problem to minimize different indices error such as Integral Absolute Error (IAE), Integral Squared Error (ISE), Integral Time Absolute Error (ITAE), Integral Time Squared Error (ITSE). The evolutionary algorithm based FOPID control performances are compared with PID control performance for the heat flow system. In addition, the proposed methods have superiority value in terms of transient and frequency domain analysis than the traditional and PID methods. Comparative performance evaluation of meta-heuristic based FOPID design for heat flow system has not carried out before.