Snake Optimizer: A novel meta-heuristic optimization algorithm

Metaheuristic optimization algorithms continue to be essential for solving complex real-world problems, yet existing methods often struggle with balancing exploration and exploitation across diverse problem landscapes. This paper proposes a novel nature-inspired metaheuristic optimization algorithm named the Painted Wolf Optimization (PWO) algorithm. The main inspiration for the PWO algorithm is the group behavior and hunting strategy of painted wolves, also known as African wild dogs in the wild, particularly their unique consensus-based voting rally mechanism, a behavior fundamentally distinct from the social dynamics of grey wolves. In this innovative process, pack members explore different areas to find prey; then, they hold a pre-hunting voting rally based on the alpha member to determine who will begin the hunt and attack the prey. The efficiency of the proposed PWO algorithm is evaluated by a comparison study with other well-known optimization algorithms on 33 test functions, including the Congress on Evolutionary Computation (CEC) 2017 suite and different real-world engineering design cases. Furthermore, the algorithm’s performance is further tested across a spectrum of optimization problems with extensive unknown search spaces. This includes its application within the field of cybersecurity, specifically in the context of training a machine learning-based intrusion detection system (ML-IDS), achieving an accuracy of 0.90 and an F-measure of 0.9290. Statistical analyses using the Wilcoxon signed-rank test (all p<0.05) indicate that the PWO algorithm outperforms existing state-of-the-art algorithms, providing superior solutions in diverse and unpredictable optimization landscapes. This demonstrates its potential as a robust method for tackling complex optimization problems in various fields. The source code for the PWO algorithm is publicly available at https://github.com/saeidsheikhi/Painted-Wolf-Optimization.

With the increasing demand for hospital services amidst the COVID-19 pandemic, allocation of limited public resources and management of healthcare services are of paramount importance. In the field of patient flow scheduling, previous research primarily focused on classical-based objective functions, while ignoring environmental-based objective functions. This study presents a flexible job shop scheduling problem to optimize patient flow and, thereby, minimize the total carbon footprint, as the sustainability-based objective function. Since flexible job shop scheduling is an NP-hard problem, a metaheuristic optimization algorithm, called Chaotic Salp Swarm Algorithm Enhanced with Opposition-Based Learning and Sine Cosine (CSSAOS), was developed. The proposed algorithm integrates the Salp Swarm Algorithm (SSA) with chaotic maps to update the position of followers, the sine cosine algorithm to update the leader position, and opposition-based learning for a better exploration of the search space. generating more accurate solutions. The proposed method was successfully applied in a real-world case study and demonstrated better performance than other well-known metaheuristic algorithms, including differential evolution, genetic algorithm, grasshopper optimization algorithm, SSA based on opposition-based learning, quantum evolutionary SSA, and whale optimization algorithm. In addition, it was found that the proposed method is scalable to different sizes and complexities.

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

Precise estimation of pan evaporation is necessary to manage available water resources. In this study, the capability of three hybridized models for modeling monthly pan evaporation (Epan) at three stations in the Dongting lake basin, China, were investigated. Each model consisted of an adaptive neuro-fuzzy inference system (ANFIS) integrated with a metaheuristic optimization algorithm; i.e., particle swarm optimization (PSO), whale optimization algorithm (WOA), and Harris hawks optimization (HHO). The modeling data were acquired for the period between 1962 and 2001 (480 months) and were grouped into several combinations and incorporated into the hybridized models. The performance of the models was assessed using the root mean square error (RMSE), mean absolute error (MAE), Nash–Sutcliffe Efficiency (NSE), coefficient of determination (R2), Taylor diagram, and Violin plot. The results showed that maximum temperature was the most influential variable for evaporation estimation compared to the other input variables. The effect of periodicity input was investigated, demonstrating the efficacy of this variable in improving the models’ predictive accuracy. Among the models developed, the ANFIS-HHO and ANFIS-WOA models outperformed the other models, predicting Epan in the study stations with different combinations of input variables. Between these two models, ANFIS-WOA performed better than ANFIS-HHO. The results also proved the capability of the models when they were used for the prediction of Epan when given a study station using the data obtained for another station. Our study can provide insights into the development of predictive hybrid models when the analysis is conducted in data-scare regions.

Cross-correlation stacking-based microseismic source location using three metaheuristic optimization algorithms

The inherent variability and uncertainty of solar radiation, influenced by factors such as seasons, weather, and cloud cover, pose significant challenges in accurately forecasting energy output from solar power systems. Robust forecasting models that can capture the dynamic behavior of solar radiation are crucial for optimizing energy generation, distribution, and storage processes. This study proposes an integrated approach that combines neural networks, metaheuristic optimization algorithms, and principal component analysis (PCA) for precise solar radiation forecasting. The methodology uses one year of solar radiation data from November 2022 to October 2023 from Vellore district, India. PCA is employed for dimensionality reduction, mitigating the curse of dimensionality. The reduced dataset is segregated into winter, summer, and monsoon subsets to capture distinct seasonal patterns. Neural networks are trained on these seasonal datasets, with their weights optimized by metaheuristic algorithms, including particle swarm optimization (PSO), gray wolf optimization (GWO), whale optimization algorithm (WOA), and coati optimization algorithm (COA). A comprehensive comparative study evaluated the performance of ANN-PSO, ANFIS-GWO, ANN-WOA, ANN-COA, and traditional ANN models. The ANN-COA model, integrating ANN with COA for weight optimization and PCA for dimensionality reduction, achieved the most accurate forecasts, outperforming other approaches. It attained optimal RMSE of 12.06 for winter, 6.72 for summer, and 9.49 for monsoon season, MAE of 6.66, 5.50, and 6.25, MAPE of 3.58 %, 1.68 %, and 2.31 %, and R2 of 0.973, 0.982, and 0.985 for winter, summer, and monsoon seasons respectively, demonstrating its robustness and suitability for time-series solar radiation forecasting.

Several optimization problems from various types of applications have been efficiently resolved using available meta-heuristic algorithms such as Particle Swarm Optimization and Genetic Algorithm. Recently, many meta-heuristic optimization techniques have been extensively reported in the literature. Nevertheless, there is still room for new optimization techniques and strategies since, according to the literature, there is no meta-heuristic optimization algorithm that may be considered as the best choice to cope with all modern optimization problems. This paper introduces a novel meta-heuristic optimization algorithm named Dynamic Group-based Optimization Algorithm (DGCO). The proposed algorithm is inspired by the cooperative behavior adopted by swarm individuals to achieve their global goals. DGCO has been validated and tested against twenty-three mathematical optimization problems, and the results have been verified by a comparative study with respect to state-of-the-art optimization algorithms that are already available. The results have shown the high exploration capabilities of DGCO as well as its ability to avoid local optima. Moreover, the performance of DGCO has also been verified against five constrained engineering design problems. The results demonstrate the competitive performance and capabilities of DGCO with respect to well-known state-of-the-art meta-heuristic optimization algorithms. Finally, a sensitivity analysis is performed to study the effect of different parameters on the performance of the DGCO algorithm.

Introduction. The assessment of the reliability and cost of complex systems, such as Complex Bridge Systems (CBS) and Life Support Systems in Space Capsules (LSSSC), is fascinating. To achieve the ideal system design through diverse constraints and increase overall system reliability, researchers have extensively explored system reliability and cost optimization problems. Hence, the significant advancement in metaheuristic methods is the primary source of further system reliability and cost optimization process refinement.&#x0D; Aim and tasks. This research attempts to enhance the reliability and cost of complex systems named CBC and LSSSC has been presented. &#x0D; Results. The structure is based on few recent metaheuristic techniques, such as Moth Flame Optimization (MFO), Whale Optimization Algorithm (WOA), Gazelle Optimization Algorithm (GOA), Dragonfly Algorithm (DA), and Coati Optimization Algorithm (COA). Comparing the acquired findings to those found in other proposed techniques demonstrates the usefulness of a methodology based on COA. The proposed COA algorithm exhibits enhanced efficiency by offering superior solutions to reliability and cost-optimization problems. In addition, a non-parametric Friedman ranking was performed for validation. The results of this research are based on improving the reliability of the parameters and decreasing complex systems’ costs used by the five metaheuristic methods. Observing the convergence graph, Friedman ranking, statistical results test, and tables determined that COA is the most effective algorithm for a complex system’s cost and reliability parameters compared to other existing approaches, and also provided a faster solution.&#x0D; Conclusions. This study proposes unique ways to reduce costs while increasing parameter reliability in complex systems. After analysing the comparative solution, the authors found that when comparing these approaches (GOA, DA, MFO, WOA, and COA), the COA provided the best minimum solution for the cost and reliability of complex systems. Hence, the suggested COA procedure was more successful than that described in this study.

Optimization is a vital constituent of machine learning as it brings about efficiency of the analysis process. An optimizer is used to minimize an objective function, which in turn leverages efficiency of any given model. Many metaheuristic algorithms have been applied on many models for optimization purposes. The mostly favoured metaheuristic algorithms are the population-based ones as they initialize a population of candidate solutions, rather than a single-solution strategy. This paper takes off from the original research made on fuzzy C-means (FCM), where the weighting component (m) was set at 2. Setting the weighting component at 2 may not ideally work well for all applications. Then, the point of this examination is to optimize the weighting component m using bio-inspired algorithms that are population-based in nature. The idea is to compare performance of conventional FCM with improved version of FCM by augmenting it with different optimization techniques. Three comparatively new and popular bio-inspired optimization algorithms such as moth-flame Optimizer (MFO), gray wolf optimizer (GWO) and whale optimization algorithm (WOA) are selected to optimize tunable parameters of conventional FCM. For experimentation purpose, five different data sets namely seeds, iris, glass identification, mall customer segmentation and BuddyMove are selected. The experimental results show considerable improvements in the overall performance of conventional FCM as a consequence of parameter optimization. A new approach for optimizing FCM parameters using bio-inspired algorithms is presented.

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.

One of the most parameters and variables in every economics is the interest rate. Government officials and lawmakers change interest rates for various purposes: controlling liquidity, inflation, and prices, Economic growth and development, lending, etc. So, it is important to set the interest rate correctly. If you can predict the interest rate correctly, you can earn and gain profit by investing in various sectors. Moreover, the interest rate can impact other sectors through parallel markets such as the stock market, automobile, housing, etc. Interest rates are related to parallel markets. Thus, if you can forecast the interest rate, you can predict the parallel markets too. The main goal of this article, as it is clear from the title, is the prediction of interest rate using ANN and improving the network using some novel heuristic algorithms such as Moth Flame Optimization algorithm (MFO), Chimp Optimization Algorithm (CHOA), Time-varying Correlation Particle Swarm Optimization algorithm (TVAC-PSO), etc. we used 17 variables such as oil price, gold coin price, house price, etc. as input variables. We used GA and a new algorithm called Grey Wolf Optimization, Particle Swarm Optimization (GWO-PSO) algorithm as a feature selection and choosing the best variables. We have used eight loss functions such as MSE, RMSE, MAE, etc. too. Finally, we have compared different algorithms due to their estimation errors. The main contribution of this paper is that, first, this is for the first time which these novel metaheuristic algorithms have been used for the prediction of interest rate. Second, it has tried to use different graphs and tables for better understanding and totally a comprehensive research paper. The results show that Whale Optimization Algorithm (WOA) performed better than other methods along with less error.

The dynamic voltage restorer's (DVR) transient, steady-state, and dynamic responses are essential requirements for protecting sensitive loads against upstream voltage disturbances via the DVR's ride-through capabilities. DVRs also look after transient oscillation at the instant of entering, and /or exiting by the DVR. This paper presents an enhanced, optimized, and less complex DVR control system structure, which is capable of improving the transient, steady-state, and dynamic responses as well as eliminating inherent transient oscillations. The control system comprises a closed-loop feedback control signal and feedforward upstream disturbance detection signal. Incorporating the feedforward term helps, dramatically, in improving the system response and eliminating the transient oscillations in the load voltage. The error signals are adapted using a PI controller to make the load voltage faithfully track its predefined reference waveform. The controller is implemented in the dq synchronous rotating reference frame. The parameters of the PI controller are selected using modern population-based optimization called the Harris Hawks Optimization (HHO) technique. The results obtained using the HHO technique are compared with two other optimization algorithms, namely Particle Swarm Optimization (PSO) and Whale Optimization Algorithm (WOA). The results show that the HHO gives the best system response. The system is simulated using MATLAB/Simulink and the validation via Typhoon HIL402 real-time emulator. Both HIL402 validation and simulation results show that the proposed control scheme recovers normal operation against voltage disturbance within approximately 1.2 milliseconds without overshoot with steady-state error near zero and significantly dampens the inherent voltage oscillation that occurs at the instant of DVR entrance and/or exit.

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.

Abstract. The optimization of aerospace propulsion systems plays a pivotal role in enhancing performance and efficiency. This paper presents a comprehensive analysis of a 2D bypass dual throat nozzle, with a primary focus on optimizing two critical design parameters: expansion ratio and bypass width, to maximize the mass flow rate and facilitate thrust vectoring capabilities. An improved Harris Hawks Optimization (HHO) algorithm was leveraged in this research to achieve an optimal configuration for the nozzle. Furthermore, the complex relationship between design parameters and mass flow rate is approximated using a Radial Basis Function (RBF) surrogate model, allowing for effective optimization. Through extensive simulations and analyses, the effectiveness of the proposed methodology is demonstrated, showcasing significant improvements in nozzle performance compared to conventional approaches. Furthermore, the optimized configuration obtained through the HHO algorithm is systematically compared with other metaheuristic optimization algorithms, highlighting its superiority in achieving optimal solutions for thrust vectoring applications. This study advances aerospace propulsion technology by providing valuable insights into the optimization of bypass dual throat nozzles, thereby paving the way for enhanced performance and maneuverability in aerospace systems.

The optimization of dam reservoir operations is of the utmost importance, as operators strive to maximize revenue while minimizing expenses, risks, and deficiencies. Metaheuristics have recently been investigated extensively by researchers in the management of dam reservoirs. But the animal-concept-based metaheuristic algorithm with Lévy flight integration approach has not been used at Karun-4. This paper investigates the optimization of dam reservoir operation using three unexplored metaheuristics: the whale optimization algorithm (WOA), the Levy-flight WOA (LFWOA), and the Harris hawks optimization algorithm (HHO). Utilizing a time series data set on the hydrological and climatic characteristics of the Karun-4 hydroelectric reservoir in Iran, an analysis was conducted. The objective functions and constraints of the Karun-4 hydropower reservoir were examined throughout the optimization procedure. HHO produces the best optimal value, the least-worst optimal value, the best average optimal value, and the best standard deviation (SD) with scores of 0.000026, 0.001735, 0.000520, and 0.000614, respectively, resulting in the best overall ranking mean (RM) with a score of 1.5 at Karun-4. Throughout the duration of the test, the optimized trends of water release and water storage indicate that HHO is superior to the other investigated metaheuristics. WOA has the best correlation of variation (CV) with a score of 0.090195, while LFWOA has the best convergence rate (3.208 s) and best CPU time. Overall, it can be concluded that HHO has the most desirable performance in terms of optimization. Yet, current studies indicate that both WOA and LFWOA generate positive and comparable outcomes.