Recent Metaheuristic Algorithm Research Articles

PM2.5 Concentration Estimation Using Bi-LSTM with Osprey Optimization Method

Outdoor air pollution causes a lot of health problems for humans. Particulate Matter 2.5 (PM2.5), due to its small size, can enter the human respiratory system with ease and cause significant health effects on humans. This makes PM2.5 significant among the various air pollutants. Hence, it is important to measure the value of PM2.5 accurately for better management of air quality. Algorithms for deep learning and machine learning can be used to forecast air quality data. A model that minimizes the prediction error of the PM2.5 forecast is needed. In this paper, a PM2.5 concentration estimation model using Bi-LSTM (Bidirectional Long Short-Term Memory) with meteorological data as predictor variables is proposed. For a better estimation of PM2.5 values, the hyperparameters of the Bi-LSTM model used are tuned using the Osprey Optimization Algorithm (OOA), a recent meta-heuristic algorithm. The model that works with the optimal values of hyperparameters identified by OOA performed better than the other models when they are compared based on evaluation metrics like Mean-Squared Error and R2.

Improving effort-aware just-in-time defect prediction with weighted code churn and multi-objective slime mold algorithm

Effort-aware just-in-time software defect prediction (JIT-SDP) aims to effectively utilize the limited resources of software quality assurance (SQA) to detect more software defects. This improves the efficiency of SQA work and the quality of the software. However, there is disagreement regarding the representation of the key feature variable, SQA effort, in the field of effort-aware JIT-SDP. Additionally, the most recent metaheuristic optimization algorithms (MOAs) have not yet been effectively integrated with multi-objective effort-aware JIT-SDP tasks. These deficiencies, in both feature representation and model optimization (MO), result in a significant disparity between the performance of effort-aware JIT-SDP techniques and the expectations of the industry. In this study, we present a novel method called weighted code churn (CC) and improved multi-objective slime mold algorithm (SMA) (WCMS) for effort-aware JIT-SDP. It comprises two stages: feature improvement (FI) and MO. In the FI phase, we normalize the two feature variables: number of modified files (NF) and distribution of modified code across each file (Entropy). We then use an exponential function to quantify the level of difficulty of the change. The equation is as follows: DD = NFEntropy, where DD is an acronym for the degree of difficulty, NF denotes the base number, and Entropy denotes the index. We define change effort as the product of the difficulty degree in implementing the change and CC, with weighted CC representing the change effort. During the MO stage, we improve the SMA by incorporating multi-objective handling capabilities and devising mechanisms for multi-objective synchronization and conflict resolution. We develop a multi-objective optimization algorithm for hyperparameter optimization (HPO) of the JIT-SDP model in WCMS. To evaluate the performance of our method, we conducted experiments using six well-known open-source projects and employed two effort-aware performance evaluation metrics. We evaluated our method based on three scenarios: cross-validation, time-wise cross-validation, and across-project prediction. The experimental results indicate that the proposed method outperforms the benchmark method. Furthermore, the proposed method demonstrates greater scalability and generalization capabilities.

Large-scale IoT attack detection scheme based on LightGBM and feature selection using an improved salp swarm algorithm

Due to the swift advancement of the Internet of Things (IoT), there has been a significant surge in the quantity of interconnected IoT devices that send and exchange vital data across the network. Nevertheless, the frequency of attacks on the Internet of Things is steadily rising, posing a persistent risk to the security and privacy of IoT data. Therefore, it is crucial to develop a highly efficient method for detecting cyber threats on the Internet of Things. Nevertheless, several current network attack detection schemes encounter issues such as insufficient detection accuracy, the curse of dimensionality due to excessively high data dimensions, and the sluggish efficiency of complex models. Employing metaheuristic algorithms for feature selection in network data represents an effective strategy among the myriad of solutions. This study introduces a more comprehensive metaheuristic algorithm called GQBWSSA, which is an enhanced version of the Salp Swarm Algorithm with several strategy improvements. Utilizing this algorithm, a threshold voting-based feature selection framework is designed to obtain an optimized set of features. This procedure efficiently decreases the number of dimensions in the data, hence preventing the negative effects of having a high number of dimensions and effectively extracting the most significant and crucial information. Subsequently, the extracted feature data is combined with the LightGBM algorithm to form a lightweight and efficient ensemble learning scheme for IoT attack detection. The proposed enhanced metaheuristic algorithm has superior performance in feature selection compared to the recent metaheuristic algorithms, as evidenced by the experimental evaluation conducted using the NSLKDD and CICIoT2023 datasets. Compared to current popular ensemble learning solutions, the proposed overall solution exhibits excellent performance on multiple key indicators, including accuracy, precision, as well as training and detection time. Especially on the large-scale dataset CICIoT2023, the proposed scheme achieves an accuracy rate of 99.70% in binary classification and 99.41% in multi classification.

Recent metaheuristic algorithms for medical object localization using MSER detector in computer-aided diagnosis system

Recent metaheuristic algorithms for medical object localization using MSER detector in computer-aided diagnosis system

Comprehensive analysis of optimal power flow using recent metaheuristic algorithms

This paper provides six metaheuristic algorithms, namely Fast Cuckoo Search (FCS), Salp Swarm Algorithm (SSA), Dynamic control Cuckoo search (DCCS), Gradient-Based Optimizer (GBO), Northern Goshawk Optimization (NGO), Opposition Flow Direction Algorithm (OFDA) to efficiently solve the optimal power flow (OPF) issue. Under standard and conservative operating settings, the OPF problem is modeled utilizing a range of objectives, constraints, and formulations. Five case studies have been conducted using IEEE 30-bus and IEEE 118-bus standard test systems to evaluate the effectiveness and robustness of the proposed algorithms. A performance evaluation procedure is suggested to compare the optimization techniques' strength and resilience. A fresh comparison methodology is created to compare the proposed methodologies with other well-known methodologies. Compared to previously reported optimization algorithms in the literature, the obtained results show the potential of GBO to solve various OPF problems efficiently.

Integrated transmission expansion planning incorporating fault current limiting devices and thyristor-controlled series compensation using meta-heuristic optimization techniques

Transmission expansion planning (TEP) is a vital process of ensuring power systems' reliable and efficient operation. The optimization of TEP is a complex challenge, necessitating the application of mathematical programming techniques and meta-heuristics. However, selecting the right optimization algorithm is crucial, as each algorithm has its strengths and limitations. Therefore, testing new optimization algorithms is essential to enhance the toolbox of methods. This paper presents a comprehensive study on the application of ten recent meta-heuristic algorithms for solving the TEP problem across three distinct power networks varying in scale. The ten meta-heuristic algorithms considered in this study include Sinh Cosh Optimizer, Walrus Optimizer, Snow Geese Algorithm, Triangulation Topology Aggregation Optimizer, Electric Eel Foraging Optimization, Kepler Optimization Algorithm (KOA), Dung Beetle Optimizer, Sea-Horse Optimizer, Special Relativity Search, and White Shark Optimizer (WSO). Three TEP models incorporating fault current limiters and thyristor-controlled series compensation devices are utilized to evaluate the performance of the meta-heuristic algorithms, each representing a different scale and complexity level. Factors such as convergence speed, solution quality, and scalability are considered in evaluating the algorithms’ performance. The results demonstrated that KOA achieved the best performance across all tested systems in terms of solution quality. KOA’s average value was 6.8% lower than the second-best algorithm in some case studies. Additionally, the results indicated that WSO required approximately 2–3 times less time than the other algorithms. However, despite WSO’s rapid convergence, its average solution value was comparatively higher than that of some other algorithms. In TEP, prioritizing solution quality is paramount over algorithm speed.

Multi-level optimal energy management strategy for a grid tied microgrid considering uncertainty in weather conditions and load

Microgrids require efficient energy management systems to optimize the operation of microgrid sources and achieve economic efficiency. Bi-level energy management model is proposed in this paper to minimize the operational cost of a grid-tied microgrid under load variations and uncertainties in renewable sources while satisfying the various technical constraints. The first level is day ahead scheduling of generation units based on day ahead forecasting of renewable energy sources and load demand. In this paper, a recent meta-heuristic algorithm called Coronavirus Herd Immunity Optimizer (CHIO) is used to solve the problem of day-ahead scheduling of batteries, which is a complex constrained non-linear optimization problem, while the Lagrange multiplier method is used to determine the set-point of the Diesel Generator (DG). The second level of the proposed EMS is rescheduling and updating the set-points of sources in real-time according to the actual solar irradiance, wind speed, load, and grid tariff. In this paper, a novel real-time strategy is proposed to keep the economic operation during real-time under uncertainties. The obtained results show that the CHIO-based bi-level EMS demonstrates an optimal economic operation for a grid-connected microgrid in real-time when there are uncertainties in weather, utility tariffs, and load forecasts.

Bluefin Trevally Optimizer (BTO): A Metaheuristic Algorithm Using Fuzzy Logic Controller for Feature Selection Problem

Metaheuristic algorithms are increasingly used for feature selection (FS) problems due to their robustness and searchability. This study presents a unique optimization technique called fuzzy bluefin trevally optimizer (BTO). BTO draws its inspiration from bluefin’s cooperative nature and sudden ambush behavior. The trevally will jump out of the water and attack its food in the air or even snag it off the water when it is sufficiently close to it. This study uses mathematical modeling to create an optimization algorithm to replicate dynamic patterns and behaviors. Since parameters play a critical role in optimization, we use the fuzzy concept for dynamic parameter adaptation in fuzzy BTO. By introducing the idea of signature and demonstrating that fuzzy BTO converges to the global optimal point, the mathematical basis for the proposed algorithm is provided. The convergence of fuzzy BTO is proved using the Markov chain property. On benchmark functions (BF), the effectiveness of the suggested strategies is evaluated and compared with other metaheuristic algorithms. Then the proposed algorithms are applied to a fuzzy FS problem. Then they are compared with traditional and recent metaheuristic algorithms on the UCI machine learning repository datasets. Finally, the statistical significance is examined using the Kruskal–Wallis test (KWT).

Multi-strategy enhanced Marine Predators Algorithm with applications in engineering optimization and feature selection problems

This paper introduces a novel meta-heuristic optimization algorithm named Clustering Wavelet Opposition-based Marine Predators Algorithm (CWOMPA) to address some limitations present in the well-established Marine Predators Algorithm (MPA). CWOMPA incorporates three key strategies: a fuzzy clustering approach for escaping local optima, using wavelet basis function-based impact coefficient adjustment for elites to prevent premature convergence, and finally an adaptive opposition-based learning strategy for maintaining population diversity. Compared with some recent meta-heuristic algorithms, extensive evaluations conducted affirm that CWOMPA achieves the best Friedman rank, 4.30 and 1.95 respectively, on 23 benchmark functions and the CEC 2017 benchmark set. Not only does CWOMPA demonstrate significant effectiveness on six constrained problems from the CEC 2020 real-world benchmarks, but also when applied to 14 medical datasets, it gains superior Friedman ranks in terms of selected features, classification accuracy, F-Score, and objective function value, 2.5, 2.25, 2.96, 2.93 respectively, outperforming other common methods. Finally, CWOMPA exhibits the highest classification accuracy across all datasets and the best F-Score performance in nine datasets compared to traditional feature selection algorithms. The obtained results reveal that CWOMPA is a powerful and versatile optimization algorithm with significant potential in various real-world applications, including feature selection for medical datasets.

Investigating the actual performance of recent meta-heuristic algorithms in solving different optimization problems

In this paper, the Egret Swarm Optimization Algorithm (ESOA) and Zebra Optimization Algorithm (ZOA) are executed to solve different optimization problems. The results achieved by the two algorithms are evaluated using different criteria, such as stability, minimum, average, and maximum convergence. The evaluation of the results indicates that ESOA not only maintains a surprising stability through its execution but also provides a faster response time compared to ZOA. ESOA requires at most 20 iterations to reach the best value of the main objective functions of the first two selected optimization problems, while ZOA cannot. In the last two selected optimization problems, ESOA continuously shows its superiority over ZOA with its high stability and low utilization of iterations to reach the best value of the main objective functions. Considering these results, ESOA deserves powerful search methods, and the method is strongly recommended to optimize such optimization problems.

A novel strategy for real-time optimal scheduling of grid-tied microgrid considering load management and uncertainties

This paper proposes an efficient bi-level energy management strategy (EMS) to optimize the operation cost of a grid-connected microgrid, considering the system operational constraints and uncertainties for renewable energy sources and load demand. The first level is optimal day-ahead scheduling based on two stages: the first stage is finding the optimal operating points of sources during the next day while the second one is controllable loads management. The second level of the proposed EMS is rescheduling and updating the set-points of sources in real-time according to the actual solar irradiance, wind speed, load, and grid tariff. In this paper, a novel real-time strategy is proposed to keep the economic operation during real-time under uncertainties. Also, a recent meta-heuristic algorithm called Honey Badger Algorithm (HBA) is used to solve the problem of day-ahead scheduling of batteries, which is a complex constrained non-linear optimization problem. Results obtained demonstrate that the HBA based bi-level EMS provides the real-time optimal economic operation of a grid tied microgrid under uncertainties in weather, utility tariff and load forecasts.

Improved Energy Valley Optimizer with Levy Flight for Optimization Problems

Energy Valley Optimizer (EVO) is one of the recent metaheuristic algorithms. It draws inspiration from advanced principles in physics related to particle stability and decay modes. This paper presents a new Energy Valley Optimizer (EVO) and levy flights that are hybrid to improve the EVO in solving optimization problems. Levy flight is one of the most important randomization techniques. Fifteen mathematical test functions (five unimodal functions, four multimodal functions, and six composite functions) are solved with the proposed algorithm. We also compare our results with previous results of metaheuristic algorithms. The statistical results show that the results of the Levy Energy Valley Optimizer (LEVO) outperform other algorithms in almost all mathematical test functions.

Forecasting solar power generation using evolutionary mating algorithm-deep neural networks

This paper proposes an integration of recent metaheuristic algorithm namely Evolutionary Mating Algorithm (EMA) in optimizing the weights and biases of deep neural networks (DNN) for forecasting the solar power generation. The study employs a Feed Forward Neural Network (FFNN) to forecast AC power output using real solar power plant measurements spanning a 34-day period, recorded at 15-minute intervals. The intricate nonlinear relationship between solar irradiation, ambient temperature, and module temperature is captured for accurate prediction. Additionally, the paper conducts a comprehensive comparison with established algorithms, including Differential Evolution (DE-DNN), Barnacles Mating Optimizer (BMO-DNN), Particle Swarm Optimization (PSO-DNN), Harmony Search Algorithm (HSA-DNN), DNN with Adaptive Moment Estimation optimizer (ADAM) and Nonlinear AutoRegressive with eXogenous inputs (NARX). The experimental results distinctly highlight the exceptional performance of EMA-DNN by attaining the lowest Root Mean Squared Error (RMSE) during testing. This contribution not only advances solar power forecasting methodologies but also underscores the potential of merging metaheuristic algorithms with contemporary neural networks for improved accuracy and reliability.

Efficient economic operation based on load dispatch of power systems using a leader white shark optimization algorithm

This article proposes the use of a leader white shark optimizer (LWSO) with the aim of improving the exploitation of the conventional white shark optimizer (WSO) and solving the economic operation-based load dispatch (ELD) problem. The ELD problem is a crucial aspect of power system operation, involving the allocation of power generation resources to meet the demand while minimizing operational costs. The proposed approach aims to enhance the performance and efficiency of the WSO by introducing a leadership mechanism within the optimization process, which aids in more effectively navigating the complex ELD solution space. The LWSO achieves increased exploitation by utilizing a leader-based mutation selection throughout each generation of white sharks. The efficacy of the proposed algorithm is tested on 13 engineer benchmarks non-convex optimization problems from CEC 2020 and compared with recent metaheuristic algorithms such as dung beetle optimizer (DBO), conventional WSO, fox optimizer (FOX), and moth-flame optimization (MFO) algorithms. The LWSO is also used to address the ELD problem in different case studies (6 units, 10 units, 11 units, and 40 units), with 20 separate runs using the proposed LWSO and other competitive algorithms being statistically assessed to demonstrate its effectiveness. The results show that the LWSO outperforms other metaheuristic algorithms, achieving the best solution for the benchmarks and the minimum fuel cost for the ELD problem. Additionally, statistical tests are conducted to validate the competitiveness of the LWSO algorithm.

Multi-feature fusion and dandelion optimizer based model for automatically diagnosing the gastrointestinal diseases.

It is a known fact that gastrointestinal diseases are extremely common among the public. The most common of these diseases are gastritis, reflux, and dyspepsia. Since the symptoms of these diseases are similar, diagnosis can often be confused. Therefore, it is of great importance to make these diagnoses faster and more accurate by using computer-aided systems. Therefore, in this article, a new artificial intelligence-based hybrid method was developed to classify images with high accuracy of anatomical landmarks that cause gastrointestinal diseases, pathological findings and polyps removed during endoscopy, which usually cause cancer. In the proposed method, firstly trained InceptionV3 and MobileNetV2 architectures are used and feature extraction is performed with these two architectures. Then, the features obtained from InceptionV3 and MobileNetV2 architectures are merged. Thanks to this merging process, different features belonging to the same images were brought together. However, these features contain irrelevant and redundant features that may have a negative impact on classification performance. Therefore, Dandelion Optimizer (DO), one of the most recent metaheuristic optimization algorithms, was used as a feature selector to select the appropriate features to improve the classification performance and support vector machine (SVM) was used as a classifier. In the experimental study, the proposed method was also compared with different convolutional neural network (CNN) models and it was found that the proposed method achieved better results. The accuracy value obtained in the proposed model is 93.88%.

Multi-Objective Optimal Allocation of Hybrid Photovoltaic Distributed Generators and Distribution Static Var Compensators in Radial Distribution Systems Using Various Optimization Algorithms

In recent years, considerable growth was about the integration of renewable energy sources in the Radial Distribution Systems (RDS), as Photovoltaic Distributed Generators (PVDG) due to their importance in achieving plenty desired technical and economic benefits. Implementation of the Distribution Static Var Compensator (DSVC) in addition to the PVDG would be one of the best choices that may provide the maximum of those benefits. Hence, it is crucial to determine the optimal allocation of the devices (PVDG and DSVC) into RDS to get satisfactory results and solutions. This paper is devoted to solve the allocation problem (locate and size) of hybrid PVDG and DSVC units into the standards test systems IEEE 33-bus and 69-bus RDSs. Solving the formulated problem of the optimal integration of hybrid PVDG and DSVC units is based on minimizing the proposed Multi-Objective Function (MOF) which is represented as the sum of the technical-economic parameters of Total Active Power Loss (TAPL), Total Reactive Power Loss (TRPL), Total Voltage Deviation (TVD), Total Operation Time (TOT) of the overcurrent relays (OCRs) installed in the RDS, the Investment Cost of PVDGs (ICPVDG) and the Investment Cost of DSVCs (ICDSVC), by applying various recent metaheuristic optimization algorithms. The simulation results reveal the superiority and the effectiveness of the Slime Mould Algorithm (SMA) in providing the minimum of MOF, including minimization of the power losses until 16.209 kW, and 12.11 kVar for the first RDS, 4.756 kW and 7.003 kVar for the second RDS, enhancing the voltage profiles and the overcurrent protection system. Moreover, the ability to reach the optimal allocation of PVDG and DSVC and maintain the voltage profiles in the allowable limit, whatever the load demand variation.

Multi-strategy enhanced kernel search optimization and its application in economic emission dispatch problems

Abstract The kernel search optimizer (KSO) is a recent metaheuristic optimization algorithm that is based on kernel theory, eliminating the need for hyper-parameter adjustments, and demonstrating excellent global search capabilities. However, the original KSO exhibits insufficient accuracy in local search, and there is a high probability that it may fail to achieve local optimization in complex tasks. Therefore, this paper proposes a multi-strategy enhanced KSO (MSKSO) to enhance the local search ability of the KSO. The MSKSO combines several control strategies, including chaotic initialization, chaotic local search mechanisms, the high-altitude walk strategy (HWS), and the Levy flight (LF), to effectively balance exploration and exploitation. The MSKSO is compared with ten well-known algorithms on 50 benchmark test functions to validate its performance, including single-peak, multi-peak, separable variable, and non-separable variable functions. Additionally, the MSKSO is applied to two real engineering economic emission dispatch (EED) problems in power systems. Experimental results demonstrate that the performance of the MSKSO nearly optimizes that of other well-known algorithms and achieves favorable results on the EED problem. These case studies verify that the MSKSO outperforms other algorithms and can serve as an effective optimization tool.

Autonomous bonobo optimization algorithm for power allocation in wireless networks

Bonobo optimizer (BO) is a recent metaheuristic algorithm inspired by the social behavior of bonobos. BO maintains a robust search strategy based on two distinct phases that involve interesting mechanisms, such as the fission–fusion method for selection and an efficient process for producing new candidate solutions. Despite its remarkable capacity, BO presents a critical flaw that corresponds to an inappropriate balance between exploration and exploitation. Under such conditions, suboptimal or even poor solutions can be obtained. This paper proposes a modified BO algorithm in which the trajectory of each search agent is modified dynamically through the adaptation of the main parameters of the algorithm. With this new mechanism, the algorithm allows for the exploration and exploitation of different regions of the solution space and determines the global optimum in a faster manner. The performance of the proposed algorithm is evaluated by considering two scenarios. One is the optimization of 23 benchmark functions and the second is the problem of power allocation in wireless networks. The results show that the proposed scheme enhances the performance of the basic BO method by increasing the robustness and providing a better solution quality.

Social welfare maximization in deregulated power market incorporating wind power plants using metaheuristic algorithm

The increasing integration of renewable energy sources (RESs), particularly wind power plants (WPP), into deregulated power markets introduces complexities in optimizing social welfare (SW). This article proposes a recent metaheuristic algorithm to address this challenge and maximize SW while accounting for the presence of WPP and the inherent uncertainty associated with wind power forecasting. The proposed algorithm optimizes generation scheduling and demand-side bidding strategies in the deregulated power market to maximize SW while ensuring economic efficiency. To validate the effectiveness and robustness of the proposed algorithm, MATLAB simulations are conducted on IEEE 30 and IEEE 118-bus systems. The results demonstrate that the proposed algorithm provides promising solutions for maximizing SW, especially in the context of incorporating WPP. This research contributes to the advancement of power market optimization methods and promotes the seamless integration of RESs, fostering a more sustainable energy future.

A Novel Hybrid Approach Based on Analytical and Metaheuristic Algorithms for Parameters and Dynamic Resistance Estimation of a PV Array

Accurate parameters of photovoltaic (PV) arrays are essential for the modeling, analysis, and control of PV systems. Due to the lack of complete datasheets from manufacturers, different techniques have been introduced to extract the unknown parameters of PV modules. A novel approach based on one of the most recent metaheuristic (MH) optimization algorithms, the Flow Direction Algorithm, is developed in this paper to estimate the PV-modules parameters accurately. The proposed approach extracts the parameters for single-, double-, and three-diode models under different operating conditions. Comparative studies with state-of-the-art MH algorithms showed that the proposed approach is more accurate and robust and reduces the computational burden. Furthermore, a general formula is derived to obtain the dynamic resistance of different PV models. It is shown in this paper that inaccurate PV model parameters might negatively impact the steady-state and dynamic performance assessment of grid-connected PV systems under different operating conditions. Detailed time-domain simulations are presented to validate the analytical results and show the effectiveness of the proposed approach.