EWOA-OPF: Effective Whale Optimization Algorithm to Solve Optimal Power Flow Problem

Economical-environmental-technical optimal power flow solutions using a novel self-adaptive wild geese algorithm with stochastic wind and solar power

This study introduces an enhanced self-adaptive wild goose algorithm (SAWGA) for solving economical-environmental-technical optimal power flow (OPF) problems in traditional and modern energy systems. Leveraging adaptive search strategies and robust diversity capabilities, SAWGA distinguishes itself from classical WGA by incorporating four potent optimizers. The algorithm's application to optimize an OPF model on the different IEEE 30-bus and 118-bus electrical networks, featuring conventional thermal power units alongside solar photovoltaic (PV) and wind power (WT) units, addresses the rising uncertainties in operating conditions, particularly with the integration of renewable energy sources (RESs). The inherent complexity of OPF problems in electrical networks, exacerbated by the inclusion of RESs like PV and WT units, poses significant challenges. Traditional optimization algorithms struggle due to the problem's high complexity, susceptibility to local optima, and numerous continuous and discrete decision parameters. The study's simulation results underscore the efficacy of SAWGA in achieving optimal solutions for OPF, notably reducing overall fuel consumption costs in a faster and more efficient convergence. Noteworthy attributes of SAWGA include its remarkable capabilities in optimizing various objective functions, effective management of OPF challenges, and consistent outperformance compared to traditional WGA and other modern algorithms. The method exhibits a robust ability to achieve global or nearly global optimal settings for decision parameters, emphasizing its superiority in total cost reduction and rapid convergence.

An enhanced turbulent flow of water-based optimization for optimal power flow of power system integrated wind turbine and solar photovoltaic generators

This paper uses enhanced turbulent flow in water-based optimization (TFWO), specifically ETFWO, to achieve optimal power flow (OPF) in electrical networks that use both solar photovoltaic (PV) units and wind turbines (WTs). ETFWO is an enhanced TFWO that alters the TFWO structure through the promotion of communication and collaboration. Individuals in the population now interact with each other more often, which makes it possible to search more accurately in the search area while ignoring local optimal solutions. Probabilistic models and real-time data on wind speed and solar irradiance are used to predict the power output of WT and PV producers. The OPF and solution methods are evaluated using the IEEE 30-bus network. By comparing ETFWO to analogical other optimization techniques applied to the same groups of constraints, control variables, and system data, we can gauge the algorithm's robustness and efficiency in solving OPF. It is shown in this paper that the proposed ETFWO algorithm can provide suitable solutions to OPF problems in electrical networks with integrated PV units and WTs in terms of energy generation costs, improved voltage profiles, emissions, and losses, compared to the traditional TFWO and other proposed algorithms in recent studies.

A leader supply-demand-based optimization for large scale optimal power flow problem considering renewable energy generations

The supply-demand-based optimization (SDO) is among the recent stochastic approaches that have proven its capability in solving challenging engineering tasks. Owing to the non-linearity and complexity of the real-world IEEE optimal power flow (OPF) in modern power system issues and like the existing algorithms, the SDO optimizer necessitates some enhancement to satisfy the required OPF characteristics integrating hybrid wind and solar powers. Thus, a SDO variant namely leader supply-demand-based optimization (LSDO) is proposed in this research. The LSDO is suggested to improve the exploration based on the simultaneous crossover and mutation mechanisms and thereby reduce the probability of trapping in local optima. The LSDO effectiveness has been first tested on 23 benchmark functions and has been assessed through a comparison with well-regarded state-of-the-art competitors. Afterward, Three well-known constrained IEEE 30, 57, and 118-bus test systems incorporating both wind and solar power sources were investigated in order to authenticate the performance of the LSDO considering a constraint handling technique called superiority of feasible solutions (SF). The statistical outcomes reveal that the LSDO offers promising competitive results not only for its first version but also for the other competitors.

Optimal evaluation of re-opening policies for COVID-19 through the use of metaheuristic schemes

A new contagious disease or unidentified COVID-19 variants could provoke a new collapse in the global economy. Under such conditions, companies, factories, and organizations must adopt reopening policies that allow their operations to reduce economic effects. Effective reopening policies should be designed using mathematical models that emulate infection chains through individual interactions. In contrast to other modeling approaches, agent-based schemes represent a computational paradigm used to characterize the person-to-person interactions of individuals inside a system, providing accurate simulation results. To evaluate the optimal conditions for a reopening policy, authorities and decision-makers need to conduct an extensive number of simulations manually, with a high possibility of losing information and important details. For this reason, the integration of optimization and simulation of reopening policies could automatically find the realistic scenario under which the lowest risk of infection was attained. In this paper, the metaheuristic technique of the Whale Optimization Algorithm is used to find the solution with the minimal transmission risk produced by an agent-based model that emulates a hypothetical re-opening context. Our scheme finds the optimal results of different generical activation scenarios. The experimental results indicate that our approach delivers practical knowledge and essential estimations for identifying optimal re-opening strategies with the lowest transmission risk.

A Systematic Review of the Whale Optimization Algorithm: Theoretical Foundation, Improvements, and Hybridizations

Despite the simplicity of the whale optimization algorithm (WOA) and its success in solving some optimization problems, it faces many issues. Thus, WOA has attracted scholars' attention, and researchers frequently prefer to employ and improve it to address real-world application optimization problems. As a result, many WOA variations have been developed, usually using two main approaches improvement and hybridization. However, no comprehensive study critically reviews and analyzes WOA and its variants to find effective techniques and algorithms and develop more successful variants. Therefore, in this paper, first, the WOA is critically analyzed, then the last 5 years' developments of WOA are systematically reviewed. To do this, a new adapted PRISMA methodology is introduced to select eligible papers, including three main stages: identification, evaluation, and reporting. The evaluation stage was improved using three screening steps and strict inclusion criteria to select a reasonable number of eligible papers. Ultimately, 59 improved WOA and 57 hybrid WOA variants published by reputable publishers, including Springer, Elsevier, and IEEE, were selected as eligible papers. Effective techniques for improving and successful algorithms for hybridizing eligible WOA variants are described. The eligible WOA are reviewed in continuous, binary, single-objective, and multi/many-objective categories. The distribution of eligible WOA variants regarding their publisher, journal, application, and authors' country was visualized. It is also concluded that most papers in this area lack a comprehensive comparison with previous WOA variants and are usually compared only with other algorithms. Finally, some future directions are suggested.

Aerodynamic System Machine Learning Modeling with Gray Wolf Optimization Support Vector Regression and Instability Identification Strategy of Wavelet Singular Spectrum

The prediction of a stall precursor in an axial compressor is the basic guarantee to the stable operation of an aeroengine. How to predict and intelligently identify the instability of the system in advance is of great significance to the safety performance and active control of the aeroengine. In this paper, an aerodynamic system modeling method combination with the wavelet transform and gray wolf algorithm optimized support vector regression (WT-GWO-SVR) is proposed, which breaks through the fusion technology based on the feature correlation of chaotic data. Because of the chaotic characteristic represented by the sequence, the correlation-correlation (C-C) algorithm is adopted to reconstruct the phase space of the spatial modal. On the premise of finding out the local law of the dynamic system variety, the machine learning method is applied to model the reconstructed low-frequency components and high-frequency components, respectively. As the key part, the parameters of the SVR model are optimized by the gray wolf optimization algorithm (GWO) from the biological view inspired by the predatory behavior of gray wolves. In the definition of the hunting behaviors of gray wolves by mathematical equations, it is superior to algorithms such as differential evolution and particle swarm optimization. In order to further improve the prediction accuracy of the model, the multi-resolution and equivalent frequency distribution of the wavelet transform (WT) are used to train support vector regression. It is shown that the proposed WT-GWO-SVR hybrid model has a better prediction accuracy and reliability with the wavelet reconstruction coefficients as the inputs. In order to effectively identify the sign of the instability in the modeling system, a wavelet singular information entropy algorithm is proposed to detect the stall inception. By using the three sigma criteria as the identification strategy, the instability early warning can be given about 102r in advance, which is helpful for the active control.

MMKE: Multi-trial vector-based monkey king evolution algorithm and its applications for engineering optimization problems

Monkey king evolution (MKE) is a population-based differential evolutionary algorithm in which the single evolution strategy and the control parameter affect the convergence and the balance between exploration and exploitation. Since evolution strategies have a considerable impact on the performance of algorithms, collaborating multiple strategies can significantly enhance the abilities of algorithms. This is our motivation to propose a multi-trial vector-based monkey king evolution algorithm named MMKE. It introduces novel best-history trial vector producer (BTVP) and random trial vector producer (RTVP) that can effectively collaborate with canonical MKE (MKE-TVP) using a multi-trial vector approach to tackle various real-world optimization problems with diverse challenges. It is expected that the proposed MMKE can improve the global search capability, strike a balance between exploration and exploitation, and prevent the original MKE algorithm from converging prematurely during the optimization process. The performance of the MMKE was assessed using CEC 2018 test functions, and the results were compared with eight metaheuristic algorithms. As a result of the experiments, it is demonstrated that the MMKE algorithm is capable of producing competitive and superior results in terms of accuracy and convergence rate in comparison to comparative algorithms. Additionally, the Friedman test was used to examine the gained experimental results statistically, proving that MMKE is significantly superior to comparative algorithms. Furthermore, four real-world engineering design problems and the optimal power flow (OPF) problem for the IEEE 30-bus system are optimized to demonstrate MMKE's real applicability. The results showed that MMKE can effectively handle the difficulties associated with engineering problems and is able to solve single and multi-objective OPF problems with better solutions than comparative algorithms.

Enhanced whale optimization algorithm for medical feature selection: A COVID-19 case study

The whale optimization algorithm (WOA) is a prominent problem solver which is broadly applied to solve NP-hard problems such as feature selection. However, it and most of its variants suffer from low population diversity and poor search strategy. Introducing efficient strategies is highly demanded to mitigate these core drawbacks of WOA particularly for dealing with the feature selection problem. Therefore, this paper is devoted to proposing an enhanced whale optimization algorithm named E-WOA using a pooling mechanism and three effective search strategies named migrating, preferential selecting, and enriched encircling prey. The performance of E-WOA is evaluated and compared with well-known WOA variants to solve global optimization problems. The obtained results proved that the E-WOA outperforms WOA's variants. After E-WOA showed a sufficient performance, then, it was used to propose a binary E-WOA named BE-WOA to select effective features, particularly from medical datasets. The BE-WOA is validated using medical diseases datasets and compared with the latest high-performing optimization algorithms in terms of fitness, accuracy, sensitivity, precision, and number of features. Moreover, the BE-WOA is applied to detect coronavirus disease 2019 (COVID-19) disease. The experimental and statistical results prove the efficiency of the BE-WOA in searching the problem space and selecting the most effective features compared to comparative optimization algorithms.

Recent Advances in Harris Hawks Optimization: A Comparative Study and Applications

The Harris hawk optimizer is a recent population-based metaheuristics algorithm that simulates the hunting behavior of hawks. This swarm-based optimizer performs the optimization procedure using a novel way of exploration and exploitation and the multiphases of search. In this review research, we focused on the applications and developments of the recent well-established robust optimizer Harris hawk optimizer (HHO) as one of the most popular swarm-based techniques of 2020. Moreover, several experiments were carried out to prove the powerfulness and effectivness of HHO compared with nine other state-of-art algorithms using Congress on Evolutionary Computation (CEC2005) and CEC2017. The literature review paper includes deep insight about possible future directions and possible ideas worth investigations regarding the new variants of the HHO algorithm and its widespread applications.

Binary Aquila Optimizer for Selecting Effective Features from Medical Data: A COVID-19 Case Study

Medical technological advancements have led to the creation of various large datasets with numerous attributes. The presence of redundant and irrelevant features in datasets negatively influences algorithms and leads to decreases in the performance of the algorithms. Using effective features in data mining and analyzing tasks such as classification can increase the accuracy of the results and relevant decisions made by decision-makers using them. This increase can become more acute when dealing with challenging, large-scale problems in medical applications. Nature-inspired metaheuristics show superior performance in finding optimal feature subsets in the literature. As a seminal attempt, a wrapper feature selection approach is presented on the basis of the newly proposed Aquila optimizer (AO) in this work. In this regard, the wrapper approach uses AO as a search algorithm in order to discover the most effective feature subset. S-shaped binary Aquila optimizer (SBAO) and V-shaped binary Aquila optimizer (VBAO) are two binary algorithms suggested for feature selection in medical datasets. Binary position vectors are generated utilizing S- and V-shaped transfer functions while the search space stays continuous. The suggested algorithms are compared to six recent binary optimization algorithms on seven benchmark medical datasets. In comparison to the comparative algorithms, the gained results demonstrate that using both proposed BAO variants can improve the classification accuracy on these medical datasets. The proposed algorithm is also tested on the real-dataset COVID-19. The findings testified that SBAO outperforms comparative algorithms regarding the least number of selected features with the highest accuracy.

Classification of Glaucoma Based on Elephant-Herding Optimization Algorithm and Deep Belief Network

This study proposes a novel glaucoma identification system from fundus images through the deep belief network (DBN) optimized by the elephant-herding optimization (EHO) algorithm. Initially, the input image undergoes the preprocessing steps of noise removal and enhancement processes, followed by optical disc (OD) and optical cup (OC) segmentation and extraction of structural, intensity, and textural features. Most discriminative features are then selected using the ReliefF algorithm and passed to the DBN for classification into glaucomatous or normal. To enhance the classification rate of the DBN, the DBN parameters are fine-tuned by the EHO algorithm. The model has experimented on public and private datasets with 7280 images, which attained a maximum classification rate of 99.4%, 100% specificity, and 99.89% sensitivity. The 10-fold cross validation reduced the misclassification and attained 98.5% accuracy. Investigations proved the efficacy of the proposed method in avoiding bias, dataset variability, and reducing false positives compared to similar works of glaucoma classification. The proposed system can be tested on diverse datasets, aiding in the improved glaucoma diagnosis.

An Efficient and Robust Improved Whale Optimization Algorithm for Large Scale Global Optimization Problems

As an efficient meta-heuristic algorithm, the whale optimization algorithm (WOA) has been extensively applied to practical problems. However, WOA still has the drawbacks of converging slowly, and jumping out from extreme points especially for large scale optimization problems. To overcome these defects, a modified whale optimization algorithm integrated with a crisscross optimization algorithm (MWOA-CS) is proposed. In MWOA-CS, each dimension of the optimization problem updates its position by randomly performing improved WOA or crisscross optimization algorithm during the entire iterative process. The improved WOA adopts the new nonlinear convergence factor and nonlinear inertia weight to tune the ability of exploitation and exploration. To analyze the performance of MWOA-CS, a series of numerical experiments were performed on 30 test benchmark functions with dimension ranging from 300 to 1000. The experimental results revealed that the presented MWOA-CS provided better convergence speed and accuracy, and meanwhile, displayed a significantly more effective and robust performance than the original WOA and other state of the art meta-heuristic algorithms for solving large scale global optimization problems.

An Improved Equilibrium Optimizer for Solving Optimal Power Flow Problem

With the rapid development of the economy, the quality of power systems has assumed an increasingly prominent influence on people’s daily lives. In this paper, an improved equilibrium optimizer (IEO) is proposed to solve the optimal power flow (OPF) problem. The algorithm uses the chaotic equilibrium pool to enhance the information interaction between individuals. In addition, a nonlinear dynamic generation mechanism is introduced to balance the global search and local development capabilities. At the same time, the improved algorithm uses the golden sine strategy to update the individual position and enhance the ability of the algorithm to jump out of local optimums. Sixteen benchmark test functions, Wilcoxon rank sum test and 30 CEC2014 complex test function optimization results show that the improved algorithm has better global searching ability than the basic equilibrium optimizer, as well as faster convergence and a more accurate solution than other improved equilibrium optimizers and metaheuristic algorithms. Finally, the improved algorithm is applied to the standard IEEE 30-bus test systems for different objectives. The obtained results demonstrate that the improved algorithm has better solutions than other algorithms in the literature for solving the optimal power flow problem.

Hybridizing of Whale and Moth-Flame Optimization Algorithms to Solve Diverse Scales of Optimal Power Flow Problem

The optimal power flow (OPF) is a practical problem in a power system with complex characteristics such as a large number of control parameters and also multi-modal and non-convex objective functions with inequality and nonlinear constraints. Thus, tackling the OPF problem is becoming a major priority for power engineers and researchers. Many metaheuristic algorithms with different search strategies have been developed to solve the OPF problem. Although, the majority of them suffer from stagnation, premature convergence, and local optima trapping during the optimization process, which results in producing low solution qualities, especially for real-world problems. This study is devoted to proposing an effective hybridizing of whale optimization algorithm (WOA) and a modified moth-flame optimization algorithm (MFO) named WMFO to solve the OPF problem. In the proposed WMFO, the WOA and the modified MFO cooperate to effectively discover the promising areas and provide high-quality solutions. A randomized boundary handling is used to return the solutions that have violated the permissible boundaries of search space. Moreover, a greedy selection operator is defined to assess the acceptance criteria of new solutions. Ultimately, the performance of the WMFO is scrutinized on single and multi-objective cases of different OPF problems including standard IEEE 14-bus, IEEE 30-bus, IEEE 39-bus, IEEE 57-bus, and IEEE118-bus test systems. The obtained results corroborate that the proposed algorithm outperforms the contender algorithms for solving the OPF problem.

Multi-Population Enhanced Slime Mould Algorithm and with Application to Postgraduate Employment Stability Prediction

In this study, the authors aimed to study an effective intelligent method for employment stability prediction in order to provide a reasonable reference for postgraduate employment decision and for policy formulation in related departments. First, this paper introduces an enhanced slime mould algorithm (MSMA) with a multi-population strategy. Moreover, this paper proposes a prediction model based on the modified algorithm and the support vector machine (SVM) algorithm called MSMA-SVM. Among them, the multi-population strategy balances the exploitation and exploration ability of the algorithm and improves the solution accuracy of the algorithm. Additionally, the proposed model enhances the ability to optimize the support vector machine for parameter tuning and for identifying compact feature subsets to obtain more appropriate parameters and feature subsets. Then, the proposed modified slime mould algorithm is compared against various other famous algorithms in experiments on the 30 IEEE CEC2017 benchmark functions. The experimental results indicate that the established modified slime mould algorithm has an observably better performance compared to the algorithms on most functions. Meanwhile, a comparison between the optimal support vector machine model and other several machine learning methods on their ability to predict employment stability was conducted, and the results showed that the suggested the optimal support vector machine model has better classification ability and more stable performance. Therefore, it is possible to infer that the optimal support vector machine model is likely to be an effective tool that can be used to predict employment stability.