Recent Metaheuristic Optimization Algorithm Research Articles

A novel effective nonlinear state observer based robust nonlinear sliding mode controller for a 6 kW Proton Exchange Membrane Fuel Cell voltage regulation

This paper presents an effective strategy for controlling an Interleaved DC/DC Boost Converter (IBC) that is employed for reducing the current fluctuations in a 6 kW Proton Exchange Membrane Fuel Cell (PEMFC). The proposed design incorporates a new nonlinear control law combined with a nonlinear state observer, it aims at enhancing the durability of the Fuel Cells (FCs) and extending their lifetime. In this regard, a Nonlinear State Observer (NSO) is designed to simultaneously estimate current and voltage signals of the Fuel Cell Source (FCS) and a Nonlinear Sliding Mode Controller (NSMC) is constructed based on these estimates. The proposed sensorless control design is advantageous in avoiding current and voltage sensors redundancy to maintain low cost and complexity levels. Accurate output voltage tracking performance, tracking stability, and dynamic errors for a PEMFC-IBC are considered during changes of the internal model parameters in addition to effects of measurement uncertainties. A third advantage of this design is robustness and external disturbance rejection under large load variations. For these objectives, the observer and the controller parameters are optimally tuned using a recent Metaheuristic Particle Swarm Optimization Algorithm (MPSOA) inspired by the swarm intelligence for improving the dynamic performance of the controlled system. Furthermore, stability and tracking analysis properties of the closed-loop overall system are proved through Lyapunov theory for dynamic operating conditions. Finally, numerical simulations of a typical 6 kW PEMFC system validate the effectiveness of the proposed scheme with comparisons to contemporary approaches across the different cases.

GWO-Based Optimal Tuning of Type-1 and Type-2 Fuzzy Controllers for Electromagnetic Actuated Clutch Systems

Abstract The goal of this paper is to develop the optimal fuzzy controllers for nonlinear processed represented by electromagnetic actuated clutch systems in order to improve the control system performance. A Takagi-Sugeno type-1 fuzzy logic controller and a Takagi-Sugeno type-2 fuzzy logic controller are designed here to enhance position tracking performance of electromagnetic actuated clutch systems, which are subjected to parametric uncertainties. The parameters of membership functions and the rules of the proposed fuzzy controllers are optimally tuned using a recent metaheuristic optimization algorithm, namely the Grey Wolf Optimizer. A comparison of Takagi-Sugeno type-1 and Takagi-Sugeno type-2 fuzzy control systems is carried out in various simulation scenarios, which are discussed and analyzed. The results highlight that the type-2 fuzzy controller performs efficiently for these complex processes.

A multiprocess Salp swarm optimization with a heuristic based on crossing partial solutions

Abstract The Salp swarm algorithm (SSA) is one of the most recent metaheuristic optimization algorithms. SSA has been used successfully to solve optimization problems in different research areas such as machine learning, engineering design, wireless networks, image processing, mobile robotics, and energy. In this article, we present a multi-threaded implementation of the SSA algorithm. Each thread executes an SSA algorithm that shares information among the swarms to get a better solution. The best partial solutions of each swarm intersect in a similar way of genetic algorithms. The experiments with nineteen benchmark functions (unimodal, multimodal, and composite) show the results obtained with this new algorithm are better than those achieved with the original algorithm.

Design of Cascaded PI-Fractional Order PID Controller for Improving the Frequency Response of Hybrid Microgrid System Using Gorilla Troops Optimizer

This paper proposes a cascaded Proportional Integral-Fractional Order Proportional-Integral-Derivative (PI-FOPID) controller to improve the frequency response of a hybrid microgrid system. The optimum gains of the proposed controller are fine-tuned using Gorilla Troops Optimizer (GTO) which is a recent metaheuristic optimization algorithm. The case study is a two-area microgrid system that contains diesel generators, various renewable energy sources such as photovoltaic and wind generation systems, as well as different energy storage devices. Moreover, real wind speed and solar irradiance measurements have been collected for proper system modeling. The performance of the proposed cascaded PI-FOPID controller is compared to the single structure fractional order PID (FOPID) controller based on GTO and numerous other optimization techniques presented in the previous literature such as Genetic Algorithm and Particle Swarm Optimization. The robustness of the proposed cascaded PI-FOPID controller is investigated under different scenarios such as different step load perturbations, random load disturbances, and renewable energy sources variation. The simulation results are carried out using MATLAB/Simulink. The results show that the proposed controller provides an improvement in the maximum overshoot/undershoot and settling time of 99.8% and 75.9%, respectively, compared to other competing techniques.

Frequency Regulation of Electric Vehicle-Penetrated Power System Using MPA-Tuned New Combined Fractional Order Controllers

Energy transition from traditional generation sources into new renewable energy generation has become essential for facing climate changes. However, increased penetration levels of renewable energy sources (RESs) make power systems subjected to low inertia problems. Moreover, the continuously growing numbers of electric vehicles (EVs) have made the substantial need for their contribution in power systems. Therefore, this paper proposes a combined fractional-order controller using the parallel combination of tilt-integral-derivative with filter (TIDF) and hybrid fractional-order (HybFO) controllers for robust frequency regulation in interconnected power systems. The proposed controller is advantageous in combining the merits of two fractional-order controllers that result in more robust and effective load frequency control (LFC) at wide range and different types of disturbances. Furthermore, a new application of marine predator optimization algorithm (MPA) is proposed for simultaneously determining the optimum controller parameters in the different power system areas. The existing EVs contribute in performing additional functionality in power systems. EVs help in damping out the frequency and tie-line power oscillations in the proposed work. The two-area interconnected power system is selected as a case study with the installed photovoltaic (PV), and wind generations in addition to distributed EVs among areas. The obtained results show the superiority and suitability of the proposed controller over the traditional controllers in the literature. Additionally, the effectiveness of the MPA is validated and compared with recent meta-heuristic optimization algorithms.

Energy‐Spectral Efficiency Optimization in Wireless Underground Sensor Networks Using Salp Swarm Algorithm

Achieving high data rate transmission is critically constrained by green communication metrics in Wireless Sensor Networks (WSNs). A unified metric ensuring a successful compromise between the energy efficiency (EE) and the spectral efficiency (SE) is, then, an interesting design criterion in such systems. In this paper, we focus on EE‐SE tradeoff optimization in Wireless Underground Sensor Networks (WUSNs) where signals penetrate through a challenging lossy soil medium and nodes’ power supply is critical. Underground sensor nodes gather and send sensory information to underground relay nodes which amplify‐and‐retransmit received signals to an aboveground sink node. We propose to optimize source and relay powers used for each packet transmission using an efficient recent metaheuristic optimization algorithm called Salp Swarm Algorithm (SSA). Thus, the optimal source and relay transmission powers, which maximize the EE‐SE tradeoff under the maximum allowed transmission powers and the initial battery capacity constraints, are obtained. Further, we study the case where the underground medium properties are dynamic and change from a transmission to another. For this situation, we propose to allocate different maximum node powers according to the soil medium conditions. Simulation results prove that our proposed optimization achieves a significant EE‐SE tradeoff and prolongs the network’s lifetime compared to the fixed allocation node power scheme. Additional gain is obtained in case of dynamic medium conditions.

Text documents clustering using modified multi-verse optimizer

In this study, a multi-verse optimizer (MVO) is utilised for the text document clus- tering (TDC) problem. TDC is treated as a discrete optimization problem, and an objective function based on the Euclidean distance is applied as similarity measure. TDC is tackled by the division of the documents into clusters; documents belonging to the same cluster are similar, whereas those belonging to different clusters are dissimilar. MVO, which is a recent metaheuristic optimization algorithm established for continuous optimization problems, can intelligently navigate different areas in the search space and search deeply in each area using a particular learning mechanism. The proposed algorithm is called MVOTDC, and it adopts the convergence behaviour of MVO operators to deal with discrete, rather than continuous, optimization problems.
 For evaluating MVOTDC, a comprehensive comparative study is conducted on six text document datasets with various numbers of documents and clusters. The quality of the final results is assessed using precision, recall, F-measure, entropy accuracy, and purity measures. Experimental results reveal that the proposed method performs competitively in comparison with state-of-the-art algorithms. Statistical analysis is also conducted and shows that MVOTDC can produce significant results in comparison with three well-established methods.

A diffusion algorithm based on P systems for continuous global optimization

This paper proposes a novel optimization algorithm, called Diffusion Algorithm Based on P Systems (DAPS), for continuous global optimization. This algorithm utilizes four different search strategies and a P system to explore the search space and find the global minima. These search strategies mimic the diffusion phenomena and a hybrid membrane structure inspired by chloroplasts and mitochondria is used as a distributed-parallel computing framework in the P system. The basic concepts and principles of the diffusion search strategies and the P system framework are explained in detail. For verifying the results of DAPS, comparisons with several well-known and recent metaheuristic optimization algorithms on 13 widely used benchmark functions are conducted. The experimental results demonstrate that DAPS is highly competitive and can provide better solution accuracy on the majority of the benchmark functions.

Feature selection in classification using self-adaptive owl search optimization algorithm with elitism and mutation strategies

The fundamental aim of feature selection is to reduce the dimensionality of data by removing irrelevant and redundant features. As finding out the best subset of features from all possible subsets is computationally expensive, especially for high dimensional data sets, meta-heuristic algorithms are often used as a promising method for addressing the task. In this paper, a variant of recent meta-heuristic approach Owl Search Optimization algorithm (OSA) has been proposed for solving the feature selection problem within a wrapper-based framework. Several strategies are incorporated with an aim to strengthen BOSA (binary version of OSA) in searching the global best solution. The meta-parameter of BOSA is initialized dynamically and then adjusted using a self-adaptive mechanism during the search process. Besides, elitism and mutation operations are combined with BOSA to control the exploitation and exploration better. This improved BOSA is named in this paper as Modified Binary Owl Search Algorithm (MBOSA). Decision Tree (DT) classifier is used for wrapper based fitness function, and the final classification performance of the selected feature subset is evaluated by Support Vector Machine (SVM) classifier. Simulation experiments are conducted on twenty well-known benchmark datasets from UCI for the evaluation of the proposed algorithm, and the results are reported based on classification accuracy, the number of selected features, and execution time. In addition, BOSA along with three common meta-heuristic algorithms Binary Bat Algorithm (BBA), Binary Particle Swarm Optimization (BPSO), and Binary Genetic Algorithm (BGA) are used for comparison. Simulation results show that the proposed approach outperforms similar methods by reducing the number of features significantly while maintaining a comparable level of classification accuracy.

Salp swarm algorithm: a comprehensive survey

This paper completely introduces an exhaustive and a comprehensive review of the so-called salp swarm algorithm (SSA) and discussions its main characteristics. SSA is one of the efficient recent meta-heuristic optimization algorithms, where it has been successfully utilized in a wide range of optimization problems in different fields, such as machine learning, engineering design, wireless networking, image processing, and power energy. This review shows the available literature on SSA, including its variants, like binary, modifications and multi-objective. Followed by its applications, assessment and evaluation, and finally the conclusions, which focus on the current works on SSA, suggest possible future research directions.

Improving multilayer perceptron neural network using chaotic grasshopper optimization algorithm to forecast iron ore price volatility

Developing an accurate forecasting model for the volatility of iron ore price plays a vital role in future investments and decisions for mining projects and related companies. Viewed from this perspective, this paper proposes a novel model for accurately forecasting monthly iron ore price volatilities. This model integrates chaotic behavior into a recent meta-heuristic method grasshopper optimization algorithm (GOA) to form a new GOA algorithm called chaotic grasshopper optimization algorithm (CGOA), which is used as a trainer to learn the multilayer perceptron neural network (NN). The results of the proposed model (CGOA–NN) are compared to other models, including the conventional grasshopper optimization algorithm for NN (GOA–NN), Particle swarm optimization for NN (PSO–NN), Genetic Algorithm for NN (GA–NN), and classic NN. Empirical results demonstrate the superiority of the hybrid CGOA–NN model over other models. Moreover, the proposed CGOA–NN model demonstrates an improvement in the forecasting accuracy obtained from classic NN, GA–NN, PSO–NN, and GOA–NN models by 60.82%, 32.18%, 16.49%, and 38.71% decrease in mean square error, respectively.

Comparison of recent optimization algorithms for design optimization of a cam-follower mechanism

This study presents the application of seven recent meta-heuristic optimization algorithms to automate design of disk cam mechanism with translating roller follower regarding four follower motion laws. The algorithms are: salp swarm algorithm (SSA), moth–flame optimization (MFO), ant lion optimizer (ALO), multi verse optimizer (MVO), grey wolf optimizer (GWO), evaporation rate water cycle algorithm (ER-WCA), and mine blast algorithm (MBA). The optimum cam design problem is formulated with three objectives including the minimum congestion, maximum performance, and maximum strength resistance of the cam. Moreover, the effect of selecting follower motion law on the optimal design of mechanism is investigated. The computational results clearly indicate that the utilized algorithms are very competitive in structural design optimization, especially MBA, ER-WCA, MFO and GWO techniques. Among the four follower motion laws, the polynomial 3-4-5 degree is the best one.

Optimal Network Reconfiguration in Active Distribution Networks with Soft Open Points and Distributed Generation

In this study, we allocated soft open points (SOPs) and distributed generation (DG) units simultaneously with and without network reconfiguration (NR), and investigate the contribution of SOP losses to the total active losses, as well as the effect of increasing the number of SOPs connected to distribution systems under different loading conditions. A recent meta-heuristic optimization algorithm called the discrete-continuous hyper-spherical search algorithm is used to solve the mixed-integer nonlinear problem of SOPs and DGs allocation, along with new NR methodology to obtain radial configurations in an efficient manner without the possibility of getting trapped in local minima. Further, multi-scenario studies are conducted on an IEEE 33-node balanced benchmark distribution system and an 83-node balanced distribution system from a power company in Taiwan. The contributions of SOP losses to the total active losses, as well as the effect of increasing the number of SOPs connected to the system, are investigated to determine the real benefits gained from their allocation. It was clear from the results obtained that simultaneous NR, SOP, and DG allocation into a distribution system creates a hybrid configuration that merges the benefits offered by radial distribution systems and mitigates drawbacks related to losses, power quality, and voltage violations, while offering a far more efficient and optimal network operation. Also, it was found that the contribution of the internal loss of SOPs to the total loss for different numbers of installed SOPs is not dependent on the number of SOPs and that loss minimization is not always guaranteed by installing more SOPs or DGs along with NR. One of the findings of the paper demonstrates that NR with optimizing tie-lines could reduce active losses considerably. The results obtained also validate, with proper justifications, that SOPs installed for the management of constraints in LV feeders could further reduce losses and efficiently address issues related to voltage violations and network losses.

A robust swarm intelligence-based feature selection model for neuro-fuzzy recognition of mild cognitive impairment from resting-state fMRI

Individuals diagnosed with mild cognitive impairment (MCI) are at a high risk of transition to Alzheimer's disease (AD), but a diagnosis of MCI is challenging. Resting-state functional magnetic resonance imaging (rs-fMRI) is a promising tool for identifying patients with MCI, but an accurate and robust analysis method is needed to extract discriminative rs-fMRI features for classification between MCI patients and healthy people. In this paper, a new rs-fMRI data analysis approach based on Chaotic Binary Grey Wolf Optimization (CBGWO) and Adaptive Neuro-Fuzzy Inference System (ANFIS), namely (CBGWO-ANFIS), is presented to distinguish MCI patients based on rs-fMRI. CBGWO is a new feature selection model that attempts to reduce the number of features without loss of significant information for classification, and it uses the naïve Bayes criterion as a part of the objective function. Based on the chaos theory, the important parameters of GWO are estimated and tuned by using ten different chaos sequence maps. Subsequently, ANFIS is used to classify MCI patients and healthy people based on the subset of features retained by CBGWO. Experiments were carried out on 62 MCI patients and 65 normal controls (NC). Fractional amplitude of low frequency fluctuation (f-ALFF) was extracted from rs-fMRI as features. The results indicate that the proposed CBGWO-ANFIS approach with the Chebyshev chaos map shows a higher accuracy (around 86%), higher convergence speed, and shorter execution time than other chaos maps. Further, the proposed approach outperforms the conventional machine learning techniques and the recent meta-heuristic optimization algorithms. This study indicates that the proposed CBGWO-ANFIS approach on rs-fMRI could be a potential tool for early diagnosis of MCI.

Grasshopper optimization algorithm for extracting maximum power from wind turbine installed in Al-Jouf region

This paper proposes a novel methodology based on the recent metaheuristic grasshopper optimization algorithm (GOA) for extracting the maximum power from a wind energy system operated under variable speed conditions. The proposed system comprises a permanent magnet synchronous generator operated via a wind turbine, a dc-dc boost converter, a three-phase diode rectifier, and a maximum power point tracker (MPPT) designed via the proposed GOA. The proposed MPPT tunes the duty cycle of the boost converter to extract the maximum power from the wind energy system. The proposed system is built in Matlab/Simulink, different operating conditions are studied, and the results are compared with other programed metaheuristic approaches like Particle Swarm Optimization, Cuckoo Search, and Crow Search Algorithm. Additionally, the proposed GOA-MPPT is investigated using actual collected data for wind speed in four different locations in the Al-Jouf region in the northern Saudi Arabia, which are Sakaka, Dumat Al-Jandal, Qurayyat, and Tabarjal. The obtained results confirmed the superiority and reliability of the proposed MPPT compared to the others in enhancing the extracted power from the wind energy system.

Parameter identification of fractional-order chaotic systems using different Meta-heuristic Optimization Algorithms

Fractional-order chaotic systems (FOCS) parameter identification is an essential issue in chaos control and synchronization process. In this paper, different recent Meta-heuristic Optimization Algorithms are used to estimate the parameters and orders of three FOCS. The investigated systems are Arneodo, Borah rotational attractor and Chen double- and four-wing systems. The employed algorithms are the Salp Swarm Algorithm, Whale Optimization Algorithm, Moth-Flame Optimizer, Grey Wolf Optimizer and the Flower Pollination Algorithm (FPA). The proposed algorithms are applied on several objective functions to identify the FOCS parameters including Mean Square Error (MSE), Integral of Squared Error (ISE), Integral of Absolute Error and Integral of Time Absolute Error. A comparison between the obtained results from each algorithm over each employed objective function is carried out. The target is to investigate the most adequate optimization technique in this difficult multidimensional problem and the best objective function that helps the algorithms capture more accurate and consistent results. The performance of optimization algorithms in the presence of measurement noise has been tested using two objective functions (MSE and ISE) for the three chaotic systems. The overall outcome shows that FPA with ISE objective function is the most efficient combination for the parameter identification of the three FOCS without/with noise because it achieves higher accuracy and more robust results with faster convergence speeds than all other algorithms.

Improved whale optimization algorithm for feature selection in Arabic sentiment analysis

To help individuals or companies make a systematic and more accurate decisions, sentiment analysis (SA) is used to evaluate the polarity of reviews. In SA, feature selection phase is an important phase for machine learning classifiers specifically when the datasets used in training is huge. Whale Optimization Algorithm (WOA) is one of the recent metaheuristic optimization algorithm that mimics the whale hunting mechanism. However, WOA suffers from the same problem faced by many other optimization algorithms and tend to fall in local optima. To overcome these problems, two improvements for WOA algorithm are proposed in this paper. The first improvement includes using Elite Opposition-Based Learning (EOBL) at initialization phase of WOA. The second improvement involves the incorporation of evolutionary operators from Differential Evolution algorithm at the end of each WOA iteration including mutation, crossover, and selection operators. In addition, we also used Information Gain (IG) as a filter features selection technique with WOA using Support Vector Machine (SVM) classifier to reduce the search space explored by WOA. To verify our proposed approach, four Arabic benchmark datasets for sentiment analysis are used since there are only a few studies in sentiment analysis conducted for Arabic language as compared to English. The proposed algorithm is compared with six well-known optimization algorithms and two deep learning algorithms. The comprehensive experiments results show that the proposed algorithm outperforms all other algorithms in terms of sentiment analysis classification accuracy through finding the best solutions, while its also minimizes the number of selected features.

A Novel Robust Methodology Based Salp Swarm Algorithm for Allocation and Capacity of Renewable Distributed Generators on Distribution Grids

A novel methodology based on the recent metaheuristic optimization algorithm Salp Swarm Algorithm (SSA) for locating and optimal sizing of renewable distributed generators (RDGs) and shunt capacitor banks (SCBs) on radial distribution networks (RDNs) is proposed. A multi-objective function index (MOFI) approach is used for assuring the power quality (PQ) through enhancing the voltage level in addition to minimizing the power losses of the system and the whole operating cost of the grid. The proposed methodology is tested via 33-Bus standard radial distribution networks at different scenarios to prove their validity and performance. The obtained results are compared with the Grasshopper Optimization Algorithm (GOA), and the hybrid Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (PSOGSA). The SSA optimizer proved its superiority with high attitude and accuracy for solving the problems of RDGs’ and SCBs’ locations and capacities simultaneously. An Egyptian practical case study at different load levels via different scenarios including the control operation within 24 h is considered.

Recent meta-heuristic grasshopper optimization algorithm for optimal reconfiguration of partially shaded PV array

When solar radiation striking the PV modules inserted in PV array is non-homogeneous; then the partial shading operation is taken place. This phenomenon has negative effects on the efficiency of the PV array as the generated power is reduced, hot spots are generated; one solution to get rid of these negatives is the reconfiguration of modules such that distributing the solar radiation in regular form as much as possible. Therefore, this paper proposed a new methodology based on recent meta-heuristic optimization algorithm named grasshopper to be applicable in solving the reconfiguration process of the partially shaded PV array optimally. The main purpose of such action is to maximize the power extracted from the array via proposed objective function presented in this work. The PV array arrangement obtained via the proposed approach incorporated recent meta-heuristic grasshopper optimization algorithm (GOA) is compared with those obtained via total cross tied connection (TCT), Su Do Ku connection and genetic algorithm (GA) configuration. Additionally, different shadow patterns during a day are studied and reconfiguration arrangement is obtained at each hour. The obtained results confirm the reliability and the efficiency of the proposed GOA in evaluating the global maximum power point (GMPP) extracted from the partially shaded PV array.

A new chaotic multi-verse optimization algorithm for solving engineering optimization problems

ABSTRACTMulti-verse optimization algorithm (MVO) is one of the recent meta-heuristic optimization algorithms. The main inspiration of this algorithm came from multi-verse theory in physics. However, MVO like most optimization algorithms suffers from low convergence rate and entrapment in local optima. In this paper, a new chaotic multi-verse optimization algorithm (CMVO) is proposed to overcome these problems. The proposed CMVO is applied on 13 benchmark functions and 7 well-known design problems in the engineering and mechanical field; namely, three-bar trust, speed reduce design, pressure vessel problem, spring design, welded beam, rolling element-bearing and multiple disc clutch brake. In the current study, a modified feasible-based mechanism is employed to handle constraints. In this mechanism, four rules were used to handle the specific constraint problem through maintaining a balance between feasible and infeasible solutions. Moreover, 10 well-known chaotic maps are used to improve the performance of MVO. The experimental results showed that CMVO outperforms other meta-heuristic optimization algorithms on most of the optimization problems. Also, the results reveal that sine chaotic map is the most appropriate map to significantly boost MVO’s performance.