Sine cosine adopted Harris' hawks optimization for function optimization and power system frequency controller design

This chapter presents a comparison of the performance of integral (I) and proportional-integral-derivative (PID) controllers in frequency stabilization or load frequency control (LFC) of two-area interconnected power system considering generation rate constraints (GRCs) with Doubly fed induction generator (DFIG)-based wind energy. Two mathematically models are identified for investigations. Power system model 1 is two-area interconnected power system which contains two identical non-reheat thermal plants without DFIG participation. Whereas, power system model 2 contains two identical non-reheat thermal plants with dynamic participation of DFIG at both areas. Moreover, Harris Hawks Optimizer (HHO), Salp Swarm Algorithm (SSA), and Sine Cosine Algorithm (SCA) are applied to find the optimal values of the controller settings mentioned above. The effectiveness of the proposed controllers, which are optimally designed by several optimization techniques (i.e., HHO, SSA, and SCA) is tested and verified through an interconnected power system comprises two identical non-reheat thermal power plants with/without DFIG participation. Time-domain simulation results of the studied power system with all mentioned optimization techniques are carried out using Matlab/Simulink® software to validate the robustness of the proposed controllers.

An efficient hybrid sine-cosine Harris hawks optimization for low and high-dimensional feature selection

Recycling tasks are the most effective method for reducing waste generation, protecting the environment, and boosting the overall national economy. The productivity and effectiveness of the recycling process are strongly dependent on the cleanliness and precision of processed primary sources. However, recycling operations are often labor intensive, and computer vision and deep learning (DL) techniques aid in automatically detecting and classifying trash types during recycling chores. Due to the dimensional challenge posed by pre-trained CNN networks, the scientific community has developed numerous techniques inspired by biology, swarm intelligence theory, physics, and mathematical rules. This research applies a new meta-heuristic algorithm called the artificial hummingbird algorithm (AHA) to solving the waste classification problem based on feature selection. However, the performance of the AHA is barely satisfactory; it may be stuck in optimal local regions or have a slow convergence. To overcome these limitations, this paper develops two improved versions of the AHA called the AHA-ROBL and the AHA-OBL. These two versions enhance the exploitation stage by using random opposition-based learning (ROBL) and opposition-based learning (OBL) to prevent local optima and accelerate the convergence. The main purpose of this paper is to apply the AHA-ROBL and AHA-OBL to select the relevant deep features provided by two pre-trained models of CNN (VGG19 & ResNet20) to recognize a waste classification. The TrashNet dataset is used to verify the performance of the two proposed approaches (the AHA-ROBL and AHA-OBL). The effectiveness of the suggested methods (the AHA-ROBL and AHA-OBL) is compared with that of 12 modern and competitive optimizers, namely the artificial hummingbird algorithm (AHA), Harris hawks optimizer (HHO), Salp swarm algorithm (SSA), aquila optimizer (AO), Henry gas solubility optimizer (HGSO), particle swarm optimizer (PSO), grey wolf optimizer (GWO), Archimedes optimization algorithm (AOA), manta ray foraging optimizer (MRFO), sine cosine algorithm (SCA), marine predators algorithm (MPA), and rescue optimization algorithm (SAR). A fair evaluation of the proposed algorithms’ performance is achieved using the same dataset. The performance analysis of the two proposed algorithms is applied in terms of different measures. The experimental results confirm the two proposed algorithms’ superiority over other comparative algorithms. The AHA-ROBL and AHA-OBL produce the optimal number of selected features with the highest degree of precision.

When it comes to improving the performance of a wireless mesh network, the positioning of routers is critical. In order to provide the greatest network accessibility, it is essential that mesh routers be installed in appropriate locations. One way to improve the network’s performance is to increase the coverage while utilizing an optimum number of routers. The network coverage may be optimised using a number of different techniques that have been developed over time in the scientific literature. An algorithm called Harris Hawk’s Optimization (HHO) is being used in this study to optimally locate mesh routers in a mesh network in order to optimise coverage and reduce network congestion. The HHO identifies the best positions to place the routers in order to have the best possible network topology by removing the overlapping routers to minimize the number of routers while keeping the coverage maximized. In this research, 60, and 100 mesh routers are deployed in two different network topologies. The simulation results are analysed and compared with Sine Cosine Algorithm (SCA), Gray Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) algorithm. With regard to network coverage and congestion reduction, simulation results show that HHO surpasses the benchmark algorithms by having 98% coverage and 34% reduction in the network size.

Combined voltage and frequency control is a critical control problem of modern power system to avoid blackouts. This paper discusses the simultaneous voltage and frequency control for an interconnected multi-source multi-area power system having two areas of equal generating capacity, each containing thermal, diesel, and wind units. A centralized model predictive controller (MPC) scheme is suggested to minimize voltage and frequency fluctuations. For effective control, a normalized performance criterion has been considered, and recently appeared Harris hawks optimization (HHO) algorithm has been integrated for the optimal tuning of MPC weights for the first time. The effectiveness of the proposed MPC-HHO controller has been verified after comparing its transient response performance indices with that of various existing controllers available in the literature. Quantitatively, the proposed controller yields a minimum performance index value, i.e. 0.000741, and it has been observed that the proposed MPC-HHO controlled system has 79.28%, 68.89%, 77.19%, and 81.96% better settling time for frequency deviation in area 1 when compared with PIλDF-LSA, FOIDF-LSA, MFO-FOPID, and PID-FA controllers, respectively. Further, the stochasticity in wind power generation has been considered with the help of Monte Carlo simulation of Autoregressive Integrated Moving Average model (ARIMA) model for practical wind speed data from National Renewable Energy Laboratory. The stability of the proposed controller has been verified using eigenvalues and Bode plot analysis, where the MPC-HHO controlled system was having more gain and phase margin values as compared to the PID controlled system. Furthermore, the robustness of the approach has been successfully evaluated by considering the nonlinearities, and parameter sensitivity analysis has been conducted after varying the parameters of voltage and frequency control loops and reevaluating the transient performance of the controlled system. Besides this, a separate case study wherein the most recent concepts like Electric Vehicle (EV) integration with grid and Virtual Inertia (VI) have been applied in addition to the proposed controller to provide auxiliary control support in the system. It has been validated that EV-VI coordinated MPC-HHO controller provides quality transient response under different scenarios including reduced inertia and stochastic load variations.

Feature selection is a required preprocess stage in most of the data mining tasks. This paper presents an improved Harris hawks optimization (HHO) to find high-quality solutions for global optimization and feature selection tasks. This method is an efficient optimizer inspired by the behaviors of Harris' hawks, which try to catch the rabbits. In some cases, the original version tends to stagnate to the local optimum solutions. Hence, a novel HHO called IHHO is proposed by embedding the salp swarm algorithm (SSA) into the original HHO to improve the search ability of the optimizer and expand the application fields. The update stage in the HHO optimizer, which is performed to update each hawk, is divided into three phases: adjusting population based on SSA to generate SSA-based population, generating hybrid individuals according to SSA-based individual and HHO-based individual, and updating search agent in the light of greedy selection and HHO’s mechanisms. A large group of experiments on many functions is carried out to investigate the efficacy of the proposed optimizer. Based on the overall results, the proposed IHHO can provide a faster convergence speed and maintain a better balance between exploration and exploitation. Moreover, according to the proposed continuous IHHO, a more stable binary IHHO is also constructed as a wrapper-based feature selection (FS) approach. We compare the resulting binary IHHO with other FS methods using well-known benchmark datasets provided by UCI. The experimental results reveal that the proposed IHHO has better accuracy rates over other compared wrapper FS methods. Overall research and analysis confirm the improvement in IHHO because of the suitable exploration capability of SSA.

Recently, renewable energy resources have been widely penetrated because of their technical advantages and environmental benefits. So, modeling and simulation of this resource are very important. Obtaining accurate photovoltaic (PV) solar cell's parameters is very significant to monitor maximum power point, performance assessment, regulation, efficiency measurements, and diagnose problems. In this paper, three photovoltaic parameters estimation models are considered. These three models, single-diode, two-diode, and triple-diode models, are presented in this work to investigate the effect of the model on precision. In this, work the Elephant Herding Optimization (EHO) algorithm is employed for the PV three models' parameters estimation. Real experimental data of a 55 mm diameter commercial Radiotechnique-Coprim (RTC France) solar cell was used for the EHO implementation. The suggested algorithm was assessed compared with several recent optimizers like the crow search algorithm, spotted hyena optimization, moth flame optimization, grasshopper optimization algorithm, and Harris hawk's optimization. The results revealed that the estimated I-V and P-V curves are very close to the experimental data at very low error. Also, the results demonstrate different models' effects in fitting the curves and proved the efficiency of the suggested EHO algorithm over other algorithms.

Power crisis faced by modern energy sector can be effectively reduced by incorporating renewable energy sources to the conventional electric grid. In our paper the hybrid power system comprises of solar photovoltaic, wind turbine generator, fuel cell, diesel energy generation along with energy storage systems. A robust frequency controller by using fractional order PID controller has been proposed in our paper. The performance of the proposed control approach is compared with the classical PID controller by considering the source and load variations. Both the controllers are optimized by a modified moth flame optimization technique. Our proposed controller effectively outperforms the PID controller by maintaining faster settling time and a lower peak overshoot for frequency deviation.

Metaheuristic approaches receive a great interest in the area of optimization, especially when exact methods are missing, or the cost is extremely high. Besides the possibility to report good solutions in reasonable time, metaheuristic techniques are widely applicable. There are diverse categories of techniques that differ in number of search agents (or solutions), solution representation, and movement mechanism in search space. Just mentioned ingredients are determined according to the motivation or inspiration philosophy behind the technique. Nature-inspired optimization category is very popular and has proven high efficiency in many problems. It contains famous subclasses like evolutionary algorithms, swarm intelligence, and single-based techniques. Famous and classical examples of each subclass are genetic algorithm, particle swarm, and ant colony optimization, and simulated annealing, respectively. Nature-inspired optimization family grows so fast, and many members have joined it recently, for example, emperor penguin colony (2019), seagull optimization algorithm (2019), sailfish optimizer (2019), pity beetle algorithm (2018), emperor penguin optimizer (2018), multi-objective artificial sheep algorithm (2018), salp swarm algorithm (2017), electromagnetic field optimization (2016), sine cosine algorithm (2016), moth-flame optimization (2015), grey wolf optimizer (2014), flower pollination algorithm (2012), bat algorithm (2010), cuckoo search algorithm (2009), firefly algorithm (2008), and many others. There are many proposed hybridization and cooperation methods between techniques to produce improved versions of original ones. Nature-inspired techniques have been used in many application areas like theoretical computer science, engineering and control, forecasting, medical field, finance, management, operation research, and others. Also, new scientific disciplines like renewable energy, robotics, and navigation are feasible areas to make use of nature-inspired techniques. This chapter sheds light on six so recently new techniques that belong to nature-inspired optimization class.

In evaluating solar photovoltaic (SPV) cell performance and monitoring operational deviations, parameters of the solar cell equivalent circuit models play an important role. Given that solar cells have nonlinear current-voltage characteristics, calculating their parameters is a significant challenge. Therefore, to effectively handle this engineering challenge, an accurate and efficient optimization technique is typically needed. In order to determine SPV cell parameters, this study revealed a new optimization technique called Ali Baba and the Forty Thieves (AFT) algorithm. The suggested optimization technique was used to estimate the parameters of the single-diode equivalent circuit model of the SPV. Study work has been done on various photovoltaic modules, including the Photowatt PWP-201 and the RTC France solar cell. In addition, a comparison study is used to demonstrate the proposed AFT algorithm's performance against a number of existing heuristic algorithms, including the Moth Flame Optimization (MFO), Dragonfly Algorithm (DA), Whale Optimization Algorithm (WOA), Grey Wolf Optimization (GWO), Ant Lion Optimization (ALO), Harris Hawk Optimization (HHO), Hybrid of Particle Swarm Optimization and Grey Wolf Optimization (PSOGWO), Marine Predator Algorithm (MPA), and African Vulture Optimization Algorithm (AVOA). The comparison has been made using the same datasets and the same computational workload for each optimization technique in order to evaluate performance fairly. The acquired comparative results demonstrated that the AFT algorithm provides higher performance than any of those techniques in terms of the root mean square error (RMSE), computation time, and the accuracy of the results for parameter estimation of SPV cells.

The sand cat swarm optimization (SCSO) is a new heuristic optimization algorithm that simulates the behavior of sand cat groups in the desert using hearing to hunt. To address the shortcomings of low accuracy of SCSO solution, slow convergence in late iterations and easy convergence stagnation, a sand cat swarm optimization based on stochastic variation and elite collaboration (SE-SCSO) is proposed. SE-SCSO first introduces a nonlinear periodic adjustment mechanism to balance the exploration and local exploitation ability of the algorithm and accelerate the convergence of the algorithm.Secondly, the pseudo-opposition and pseudo-reflection learning mechanisms are used to speed up the optimization-seeking efficiency of the SCSO algorithm and improve the global convergence capability. Designing elite collaborative strategies with random variation to enable the algorithm to jump away from the local extrema, further improving the algorithm’s optimization-seeking accuracy and convergence speed. In the simulation experiments, SE-SCSO is compared with Sand Cat Swarm Optimization (SCSO), Sine Cosine Algorithm (CSA), Circle Search Algorithm (SCA), Salp Swarm Algorithm (SSA), Harris Hawks Optimization (HHO), Whale Optimization Algorithm (WOA), and Golden Jackal Optimization (GJO) are tested for comparison. The experimental results validate the effectiveness of the proposed improvement strategies. Finally, SE-SCSO is applied to three engineering optimization problems. The results show that the improved strategy can effectively improve the performance performance of the algorithm, which gives SE-SCSO the advantages of high convergence accuracy, fast convergence, and the ability to jump out of local optimal solutions.

This paper proposes a hybridized model for air quality forecasting that combines the Support Vector Regression (SVR) method with Harris Hawks Optimization (HHO) called (HHO-SVR). The proposed HHO-SVR model utilizes five datasets from the environmental protection agency’s Downscaler Model (DS) to predict Particulate Matter () levels. In order to assess the efficacy of the suggested HHO-SVR forecasting model, we employ metrics such as Mean Absolute Percentage Error (MAPE), Average, Standard Deviation (SD), Best Fit, Worst Fit, and CPU time. Additionally, we contrast our methodology with recently created models that have been published in the literature, such as the Grey Wolf Optimizer (GWO), Salp Swarm Algorithm (SSA), Henry Gas Solubility Optimization (HGSO), Barnacles Mating Optimizer (BMO), Whale Optimization Algorithm (WOA), and Manta Ray Foraging Optimization (MRFO). In particular, the proposed HHO-SVR model outperforms other approaches, establishing it as the optimal model based on its superior results.

With the rapid development of the Internet of Things (IoT), the curse of dimensionality becomes increasingly common. Feature selection (FS) is to eliminate irrelevant and redundant features in the datasets. Particle swarm optimization (PSO) is an efficient metaheuristic algorithm that has been successfully applied to obtain the optimal feature subset with essential information in an acceptable time. However, it is easy to fall into the local optima when dealing with high-dimensional datasets due to constant parameter values and insufficient population diversity. In the paper, an FS method is proposed by utilizing adaptive PSO with leadership learning (APSOLL). An adaptive updating strategy for parameters is used to replace the constant parameters, and the leadership learning strategy is utilized to provide valid population diversity. Experimental results on 10 UCI datasets show that APSOLL has better exploration and exploitation capabilities through comparison with PSO, grey wolf optimizer (GWO), Harris hawks optimization (HHO), flower pollination algorithm (FPA), salp swarm algorithm (SSA), linear PSO (LPSO), and hybrid PSO and differential evolution (HPSO-DE). Moreover, less than 8% of features in the original datasets are selected on average, and the feature subsets are more effective in most cases compared to those generated by 6 traditional FS methods (analysis of variance (ANOVA), Chi-Squared (CHI2), Pearson, Spearman, Kendall, and Mutual Information (MI)).

Application of hybrid ANN paradigms built with nature inspired meta-heuristics for modelling soil compaction parameters

A newly hybrid nature-inspired algorithm called HSSGWOA is presented with the combination of the salp swarm algorithm (SSA) and grey wolf optimizer (GWO). The major idea is to combine the salp swarm algorithm's exploitation ability with the grey wolf optimizer's exploration ability to generate both variants' strength. The proposed algorithm uses to tune the parameters of the integral sliding mode controller (ISMC) that design to improve the dynamic performance of the two-link flexible joint manipulator. The efficiency and the capability of the proposed hybrid algorithm are evaluated based on the selected test functions. It is clear that when compared to other algorithms like SSA, GWO, differential evolution (DE), gravitational search algorithm (GSA), particles swarm optimization (PSO), and whale optimization algorithm (WOA). The ISMC parameters were tuned using the SSA, which was then compared to the HSSGWOA algorithm. The simulation results show the capabilities of the proposed algorithm, which gives an enhancement percentage of 57.46% compared to the standard algorithm for one of the links, and 55.86% for the other.