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From an engineering point of view, non‐linear systems are essential to the operation of control systems, because all systems actually have a non‐linear state in nature. In reality, there are many different kinds of non‐linear systems hidden by this negative definition. For successful analysis and control, the identification of non‐linear systems using unknown models is typically necessary. Till now, numerous approaches are developed for identifying non‐linear systems, but it cannot be employed with a large number of components. Moreover, system identification is typically restricted to output and input signals alone, also such systems are rarely used in reality. This is the primary justification for using non‐linear systems in this research. So, this research proposed a non‐linear model of system identification for large‐scale systems under the consideration of two systems: bilinear system and Volterra system. Therefore, a novel algorithm named Self Adaptive Penguin Search Optimization (SAPeSO) is introduced to attain the system characteristics properly and minimize the output variation. Finally, the effectiveness of the proposed work is compared with existing works in terms of various error measures. This research mainly focuses on the application‐oriented engineering problems. In particular, the Mean Absolute Error (MAE) of the proposed work for the Volterra system at 4000 samples is 18.83%, 14.05%, 8.88%, 29.72%, 19.91%, and 6.70% which is better than the existing bald eagle search (BES), arithmetic optimization algorithm (AOA), whale optimization algorithm (WOA), nonlinear autoregressive moving average with exogenous inputs‐ frequency response function + principal component analysis (NARMAX‐FRF+PCA), Global Gravitational Search Algorithm‐Assisted Kalman Filter (CGS‐KF), and sparse regression and separable least squares method (SR‐SLSM) methods, respectively. Finally, the error is minimum for the proposed model when compared with the other traditional approaches. [ABSTRACT FROM AUTHOR]

The regulation of the frequency and line power flow in interconnected power networks is considered to be a key aspect of load frequency control (LFC). This article broaches a modern power network composed of three interconnected control areas including traditional generation units taking into account non‐linearities, also renewable energy sources (RESs) and energy storage (ES) units are involved in the power grid paradigm. Two forms of RESs are included in the analysis, which are photovoltaic (PV) and wind power plants. In addition, the study framework involves three types of ES units, which are batteries of plug‐in electric vehicles (PEVs), flywheel energy storage system (FESS) and capacitive energy storage system (CESS). In this analysis, LFC is accomplished by the use of proportional‐integral‐derivative (PID) controllers in the system control loops. A recent optimization algorithm called Manta Ray Foraging optimization (MRFO) is employed to obtain the optimal gain configuration of the controllers. Real site measurements are imported to the RESs involved in the study aiming to examine the proposed control scheme under realistic conditions. Compared with other rival algorithms, the effectiveness of the MRFO‐based PID controller is validated. Simulation results confirm the efficacy of the proposed control scheme. The findings also ensure the role of ES units in optimizing the time‐domain responses. The main contributions of this paper are applying a new metaheuristic optimization algorithm to solve the LFC problem and introducing a new criteria for judging the system performance in compliance with the harmonic spectrum of the responses in the frequency domain. The results of the simulation are retrieved through a MATLAB model. [ABSTRACT FROM AUTHOR]

Due to the issue of environmental protection coupled with high energy demand, there was an initiation for exploration of different renewable energy sources. This article aims to optimize the total annual cost of hybrids of wind and solar renewable energy system to satisfy the predesigned load. Minimization of the total annual cost of the system by determining appropriate numbers of the components, so that the desired load can be economically and reliably satisfied under the given constraints. Gravitational Search Algorithm (GSA) was employed for the optimization process. GSA is a recently proposed metaheuristic algorithm which is based on Newton's universal gravitational law of gravity and mass interactions. It uses stochastic rules to escape local optima and find the global optimal solutions. MATLAB codes were designed for the developed fitness function and employed algorithm. The proposed methodology was run for the fitness function through the code and the results were discussed. The result was compared with the results of Particle Swarm Optimization (PSO) and also shown that: GSA has some advantage over PSO algorithm. Even though, the algorithm has several parameters to be adjusted, it is strong in both local and global optimal searches. [ABSTRACT FROM AUTHOR]

Feature selection (FS) in data mining is one of the most challenging and most important activities in pattern recognition. In this article, a new hybrid model of whale optimization algorithm (WOA) and flower pollination algorithm (FPA) is presented for the problem of FS based on the concept of opposition‐based learning (OBL) which name is HWOAFPA. The procedure is that the WOA is run first and at the same time during the run, the WOA population is changed by the OBL. And, to increase the accuracy and speed of convergence, it is used as the initial population of FPA. To evaluate the performance of the proposed method, experiments were carried out in two steps. The experiments were performed on 10 datasets from the UCI data repository and Email spam detection datasets. The results obtained from the first step showed that the proposed method was more successful in terms of the average size of selection and classification accuracy than other basic metaheuristic algorithms. In addition, the results from the second step showed that the proposed method which was a run on the Email spam dataset performed much more accurately than other similar algorithms in terms of accuracy of Email spam detection. [ABSTRACT FROM AUTHOR]

Spotted hyena optimizer (SHO) is a recently developed swarm‐based algorithm in the field of metaheuristic research, for solving realistic engineering design constraint and unconstrained difficulties. To resolve complicated nonlinear physical world tasks, at times, SHO reveals deprived performance concerning to explorative strength. So, to enhance the explorative strength along with exploitation in the search region, an attempt has been made by proposing the enhanced version of classical SHO. The suggested method is designated as space transformation search (STS)‐SHO. In STS‐SHO, a new evolutionary technique named as STS technique has been incorporated with original SHO. The suggested method has been assessed by IEEE CEC 2017 benchmark problems. The efficacy of the said method has been proven by using standard measures such as given performance metrics in CEC 2017, complexity analysis, convergence analysis, and statistical implications. Further as real‐world application, the said algorithm has been applied to train pi‐sigma neural network by means of 13 benchmark datasets considered from UCI depository. From the article it can be concluded that the suggested method STS‐SHO is an effective and trustworthy algorithm, which has the ability to resolve real‐life optimization complications. [ABSTRACT FROM AUTHOR]