Hybrid Metaheuristic Research Articles

Design Optimization of a Plate‐Fin Heat Exchanger With Metaheuristic Hybrid Algorithm

ABSTRACTThis paper introduces a novel approach to enhance the heat transfer efficiency of a plate‐fin heat exchanger. The metaheuristic hybrid approach combines particle swarm optimization (PSO) with the genetic algorithm (GA). Seven critical design parameters are used as variables. The research demonstrates the effectiveness of this hybrid method through a case study based on existing literature. The numerical results show the superior performance of the hybrid genetic algorithm particle swarm optimization over conventional GA and PSO techniques. The hybrid method achieves an optimal configuration with increased accuracy in a shorter computational time, offering significant time and cost savings in the design process.

Bus scheduling with heterogeneous Fleets: Formulation and hybrid metaheuristic algorithms

This paper focuses on optimizing mixed-fleet bus scheduling (MFBS) with vehicles of different sizes in public transport systems. We develop a novel mixed-integer nonlinear programming (MINLP) model to address the MFBS problem by optimizing vehicle assignment and dispatching programs. The model considers user costs, operator costs, and the crowding inconvenience of standing and sitting passengers. To tackle the complexity of the MFBS problem, we employ Genetic Algorithm (GA) and Grey Wolf Optimizer (GWO). Besides, we develop two hybrid metaheuristics, including GA-SA [a combination of GA and Simulated Annealing (SA)] and GWO-SA (a combination of GWO and SA), to improve optimization capabilities for the MFBS problem. We also employ a Taguchi approach to fine-tune the metaheuristics’ parameters. We widely examine and compare the metaheuristics’ performance across various-sized samples (small, medium, and large), considering solution quality, computational time, and the result stability of each algorithm. We also compare the metaheuristics’ solutions with the optimal solutions acquired by GAMS software in small and medium-scale samples. Our findings show that the GWO-SA outperforms the other metaheuristics. Applying our model to a real bus corridor in Santiago, Chile, we find that precise dispatching plans generated by more sophisticated/advanced algorithms (GA-SA and GWO-SA) lead to larger cost savings and improved performance compared to simpler algorithms (GA and GWO). Interestingly, utilizing more advanced algorithms makes a difference in terms of fleet planning in crowded scenarios, whereas for low and medium-demand cases, simpler dispatching algorithms could be used without a drop in accuracy.

Stability analysis and voltage improvement in DG-integrated distribution networks using VCPI-based critical buses and lines detection considering uncertain power factor

Voltage stability ensures reliable and efficient power transmission to end-users, making it essential for power system efficiency. Dynamic load changes and rising power requirements might cause voltage instability and strain performance. This paper investigates the voltage stability Indicators of distribution systems to address the voltage stability issue in power systems. The uncertain power factor and other numerous technical parameters that affect the voltage drop or voltage collapse are involved in predicting the system’s voltage stability. First, the analysis uses the voltage collapse proximity index (VCPI) technique, which considers bus voltage, impedance, uncertain power factor of load buses, and transmission line impedance at both ends. The applied technique correctly predicts and detects weak buses and transmission lines using different voltage stability indices since technical parameters are correlated with the VCP index. The index and line impedance correspond; consequently, the recommended indication compensates for voltage loss owing to impedance. Therefore, the VCPI is a better index for anticipating voltage collapse and finding network weak busses and lines. The implemented approach is simple, precise, and economical; the index technique detects weak buses and forecasts system collapse. Second, this work introduces EMFO-FPSO, a hybrid metaheuristic that improves voltage stability by combining Fractional-order particle swarm optimization (PSO) and Entropy design mouth flame optimization (MFO). The proposed approach is validated on the IEEE 33 bus system by identifying weak buses and optimizing distributed generation placement in the distribution system. The implemented solution approach based on VCPI and EMFO-FPSO optimization achieves a significant power loss reduction of 71.5% compared to the base case, whereas the reductions for ABC, ACO-ABC, and PSO are 57.3%, 60.7%, and 64.4%, respectively. Moreover, the proposed method attains a stable VCPI Index of 0.90, indicating a 10% enhancement in voltage stability relative to the base case. The study’s results, key findings, and comparisons show the relevance of the proposed method for voltage stability enhancement.

Data-Driven Optimised XGBoost for Predicting the Performance of Axial Load Bearing Capacity of Fully Cementitious Grouted Rock Bolting Systems

This article investigates the application of eXtreme gradient boosting (XGBoost) and hybrid metaheuristics optimisation techniques to predict the axial load bearing capacity of fully grouted rock bolting systems. For this purpose, a comprehensive dataset of 72 pull-out tests was built, considering various influential parameters such as three water-to-grout (W/G) ratios, five ranges of curing time (CT), three different grout admixtures with two different fly ash (FA) contents, and two different diameter confinements (DCs). Additionally, to find out the effect of the mechanical behaviour of grouts on the performance of fully grouted rock bolting systems, seventy-two uniaxial compression strength (UCS) samples were cast and tested simultaneously with pull-out samples. The UCS samples were prepared with the same details as the pull-out samples to avoid any inconsistency. The results highlight that peak load values generally increase with longer curing times, lower W/G, and higher UCS and DC values. The main novelty of this paper lies in its data-driven approach, using various XGBoost models. This method offers a time-, cost-, and labour-efficient alternative to traditional experimental methods for predicting rock bolt performance. For this purpose, after building the dataset and dividing it randomly into two training and testing datasets, five different XGBoost models were developed: a standalone XGBoost model and four hybrid models incorporating Harris hawk optimisation (HHO), the jellyfish search optimiser (JSO), the dragonfly algorithm (DA), and the firefly algorithm (FA). These models were subsequently evaluated for their ability to predict peak load values. The results demonstrate that all models effectively predicted peak load values, but the XGBoost-JSO hybrid model demonstrated superior performance, achieving the highest R-squared coefficients of 0.987 and 0.988 for the training and testing datasets, respectively. Sensitivity analysis revealed that UCS values were the most influential parameter, while FA content had the least impact on the maximum peak load values of fully cementitious grouted rock bolts.

Improving Automatic Coronary Stenosis Classification Using a Hybrid Metaheuristic with Diversity Control.

This study proposes a novel Hybrid Metaheuristic with explicit diversity control, aimed at finding an optimal feature subset by thoroughly exploring the search space to prevent premature convergence. Background/Objectives: Unlike traditional evolutionary computing techniques, which only consider the best individuals in a population, the proposed strategy also considers the worst individuals under certain conditions. In consequence, feature selection frequencies tend to be more uniform, decreasing the probability of premature convergent results and local-optima solutions. Methods: An image database containing 608 images, evenly balanced between positive and negative coronary stenosis cases, was used for experiments. A total of 473 features, including intensity, texture, and morphological types, were extracted from the image bank. A Support Vector Machine was employed to classify positive and negative stenosis cases, with Accuracy and the Jaccard Coefficient used as performance metrics. Results: The proposed strategy achieved a classification rate of 0.92 for Accuracy and 0.85 for the Jaccard Coefficient, obtaining a subset of 16 features, which represents a discrimination rate of 0.97 from the 473 initial features. Conclusions: The Hybrid Metaheuristic with explicit diversity control improved the classification performance of coronary stenosis cases compared to previous literature. Based on the achieved results, the identified feature subset demonstrates potential for use in clinical practice, particularly in decision-support information systems.

Contemporary approaches in matheuristics an updated survey

AbstractMatheuristics are problem independent frameworks that use mathematical programming tools to obtain high quality heuristic solutions. They are structurally general enough to be applied to different problems with little adaptation to their abstract structure, so they can be considered as new or hybrid metaheuristics based on components derived from the mathematical model of the problems of interest. In this survey, we emphasize the mathematical tools and describe how they can be used to design heuristics. We focus on mixed-integer linear programming and report representative examples from the literature of how it has been used for effective heuristic optimization. References to contributions to matheuristics deriving from neighboring research areas such as Artificial Intelligence or Quantum Computing are also included. We conclude with some ideas for possible future developments. This paper extends an original version published in 4OR with new sections on CMSA, Incremental Core, AI hybrids and Quantum Heuristics, and includes references to several recent publications.

Application of meta-heuristic hybrid models in estimating the average air temperature of Caspian sea coast of Iran

The rise of industrial societies leads to higher greenhouse gas emissions, profoundly affecting the climate in coastal regions. Consequently, air temperature readings from standard meteorological stations are key indicators of the Earth's environmental condition. Therefore, accurate estimation of daily temperature in each region is one of the important prerequisites for agricultural planning as well as water resources management and drought prevention, which can be done in different ways such as experimental, semi-experimental and intelligent models. In this research, WSVR, AIG-SVR, GWO-SVR and BAT-SVR hybrid models were investigated and evaluated in order to estimate the average daily air temperature on the shores of the Caspian Sea located in the north of Iran. For modeling, weather station data from Babolsar meteorological station located in Mazandaran province were used. During the water year from 2012 to 2022, daily parameters including relative humidity, maximum temperature, minimum temperature, wind speed, and evaporation were selected as network inputs, with the average daily air temperature as the network output. To assess and compare model performances, several criteria were employed including correlation coefficient, root mean square error (RMSE), mean absolute error (MAE), Nash-Sutcliffe efficiency (NSE), and percentage bias. Comparative analysis revealed that the WSVR model surpassed other models, demonstrating the highest correlation coefficient (0.992), lowest RMSE (0.096), and lowest MAE (0.042). The highest Nash Sutcliffe criterion (0.996) and bias percentage (0.001) were prioritized in the validation stage.

CMSA based on set covering models for packing and routing problems

AbstractMany packing, routing, and knapsack problems can be expressed in terms of integer linear programming models based on set covering. These models have been exploited in a range of successful heuristics and exact techniques for tackling such problems. In this paper, we show that integer linear programming models based on set covering can be very useful for their use within an algorithm called “Construct, Merge, Solve & Adapt”(CMSA), which is a recent hybrid metaheuristic for solving combinatorial optimization problems. This is because most existing applications of CMSA are characterized by the use of an integer programming solver for solving reduced problem instances at each iteration. We present applications of CMSA to the variable-sized bin packing problem and to the electric vehicle routing problem with time windows and simultaneous pickups and deliveries. In both applications, CMSA based on a set covering model strongly outperforms CMSA when using an assignment-type model. Moreover, state-of-the-art results are obtained for both considered optimization problems.

Meta-Heuristic Scheduling: A Review on Swarm Intelligence and Hybrid Meta-Heuristics Algorithms for Cloud Computing

Meta-Heuristic Scheduling: A Review on Swarm Intelligence and Hybrid Meta-Heuristics Algorithms for Cloud Computing

Hybrid simulated annealing–slime mould algorithm for the non-permutation flow-shop scheduling problem

The non-permutation flow-shop scheduling problem (NPFSP) is a more general type of flow-shop scheduling problem than the permutation flow-shop scheduling problem (PFSP). It features a large solution space and is highly complex. In this study, a novel metaheuristic algorithm is proposed for the NPFSP, where makespan is the scheduling objective. The specific implementation process is as follows. First, a mathematical model of the NPFSP is constructed. Secondly, the encoding and decoding rules are designed to establish the association between the solutions for PFSP and NPFSP. Subsequently, a metaheuristic hybrid simulated annealing–slime mould algorithm is proposed. Finally, a series of control experiments is conducted based on the Demirkol benchmark. The statistical results show the effectiveness of the proposed algorithm in solving the NPFSP.

A Hybrid Metaheuristic based Feature Selection Framework for In-silico Mutagenicity Prediction

Mutagenicity is both a toxic risk to humans and an indicator of carcinogenicity. Hence, estimating mutagenicity in the early stages of drug design is crucial to minimize last-stage failures and withdrawals in drug discovery. Recently, in-silico methods have started to play critical and essential roles in the drug development process because they are low cost and low effort procedures. This study aims to predict mutagenicity of chemicals using in-silico methods. To achieve this goal, a two-phased flexible framework was proposed: 1) searching the effective and representative descriptors subset with Butterfly Optimization Algorithm (BOA) and Particle Swarm Optimization and 2) predicting mutagenicity of chemicals by the selected descriptor using gradient boosted tree-based ensemble methods. The study used two datasets: one including 8167 compounds for descriptor selection and modelling, and another containing 716 external compounds to validate the efficacy of our models. The datasets comprise 162 descriptors calculated using PaDEL. The results of both the cross-validation and the external data showed that descriptors reduced by nearly one-third by BOA (51 descriptors) yielded similar or slightly better predictive results than results obtained with the entire data set. The accuracy range attained by the proposed approach using BOA is approximately 91.9% to 97.91% for the external set and 83.35% to 86.47% for the test set. This research contributes that using optimization techniques for improving early drug design and minimizing risks in drug discovery can be considered as a valuable insights and advances in the field of drug toxicity prediction, based on the findings.

Performance analysis of hybrid metaheuristic assisted collateral FO controller for HRES system integrated UPQC

Due to the fluctuating characteristics of the environment, it is advisable to connect a minimum of two renewable energy sources (RES) to the grid to mitigate unreliable power supply. The integration of Hybrid Renewable Energy Storage (HRES) with nonlinear loads introduces harmonics and other power quality issues, which are significant concerns for both utility providers and customers. Power quality challenges, such as real power fluctuations, voltage interruptions, and reactive power imbalances, can be mitigated through the use of a Unified Power Quality Conditioner (UPQC). For optimal performance, the series and shunt compensators within the UPQC rely on precise PWM signals to control the converters' outputs. These signals are correctly tuned by controllers using updated algorithms. The paper develops an optimized Hybrid Metaheuristic assisted Collateral Controller for the improvement of the 3-phase HRES system-based Distribution Grid incorporated with UPQC. It consists of a Fractional order Proportional Integral derivative (FOPID) controller combined with a proportional integral (PI) controller. As a consequence, the DC link voltage regulation and controller optimization are achieved with the optimal tuning of gain parameters by using a hybrid Metaheuristic Algorithm named Amplified Slime Mould with WildeBeest Herd Optimization (ASM-WHO) Algorithm. The optimization of the weights (W1 and W2) and parameters of the Collateral Fractional Order Controller is achieved using an innovative hybrid metaheuristic method that combines the Wildebeest Herd Optimization (WHO) and Slime Mould Algorithm (SMA). The proposed system is implemented in MATLAB Simulink to analyze the compensation efficiency during voltage sag/swell. The proposed controller showcases robust performance, emphasizing its potential for enhancing power quality and distribution stability in Three-Phase Hybrid Renewable Energy Storage systems integrated with UPQC, outperforming or matching the performance metrics of conventional methods such as PI, FOPID controller, FOPID-PI with various optimization algorithms (CSO,SOA, WHO, SMA, AQO, HBA). The settling minimum and maximum of 170.5668 and 189.4736 for the proposed FOPID-PI controller with ASM – WHO is notably superior to that of the controller without optimization, the proposed collateral controller method (using SOA or CSO), WHO optimization, and the PI controller, improving by 1.7 %, 13.78 %, 1.338 %, and 13.66 %, respectively. The ASM-WHO algorithm shows robust performance and improved convergence, as validated by benchmark function tests, and surpasses competing algorithms.

A multi‐objective optimization metaheuristic hybrid technique for forecasting the electricity consumption of the UAE: A grey wolf approach

A multi‐objective optimization metaheuristic hybrid technique for forecasting the electricity consumption of the UAE: A grey wolf approach

A fuzzy-PID controller for load frequency control of a two-area power system using a hybrid algorithm

This paper presents the use of a new hybrid optimization approach known as particle swarm optimization and grey wolf optimizer (PSO-GWO) for improving frequency stability load frequency control (LFC) in tow-area power systems. The approach consists in optimizing the fuzzy proportional-integral-derivative (fuzzy-PID) controller parameters with meta-heuristic hybrid algorithm: PSO-GWO. This technique allows to have dynamic responses with the least possible frequency deviation in very short response times. The approach proposes to controls the tie-line power and the frequency deviation in the considered two-area power systems under variable perturbation in load and changing of system parameters in order to evaluate its effectiveness. The suggested hybrid algorithm-based fuzzy-PID controller is compared with various widely used control methods in the literature such as PID controller and algorithms such as PSO and GWO in order to evaluate its effectiveness and its robustness. Through the simulations carried out on MATLAB/Simulink, the proposed PSO-GWO fuzzy-PID and the objective function exhibit improved performance, achieving minimal objective values. The proposed technique proved to be quite powerful tool in the resolution of problems related to electrical power systems, particularly in load frequency control.

Metaheuristic optimization algorithms-based prediction modeling for titanium dioxide-Assisted photocatalytic degradation of air contaminants

Airborne contaminants pose significant environmental and health challenges. Titanium dioxide (TiO2) has emerged as a leading photocatalyst in the degradation of air contaminants compared to other photocatalysts due to its inherent inertness, cost-effectiveness, and photostability. To assess its effectiveness, laboratory examinations are frequently employed to measure the photocatalytic degradation rate of TiO2. However, this approach involves time-consuming requirements, labor-intensive tasks, and high costs. In literature, ensemble or standalone models are commonly used for assessing the performance of TiO2 photocatalytic degradation of water and air contaminants. Nonetheless, the application of metaheuristic hybrid models has the potential to be more effective in predictive accuracy and efficiency. Accordingly, this research utilized hybrid machine learning (ML) algorithms to estimate the photo-degradation rate constants of organic air pollutants using TiO2 nanoparticles and exposure to ultraviolet light. Six metaheuristics optimization algorithms, namely, nuclear reaction optimization (NRO), differential evolution algorithm (DEA), human felicity algorithm (HFA), lightning search algorithm (LSA), Harris hawks algorithm (HHA), and tunicate swarm algorithm (TSA) were combined with random forest (RF) technique to establish the hybrid models. A database of 200 data points was acquired from experimental studies for model training and testing. Furthermore, multiple statistical indicators and 10-fold cross-validation were employed to examine the established hybrid model's accuracy and robustness. The TSA-RF model demonstrated superior prediction accuracy among the six suggested models, achieving an impressive correlation (R) of 0.90 and a lower root mean square error (RMSE) of 0.25. In contrast, the HFA-RF, HHA-RF, and NRO-RF models exhibited a slightly lower R-value of 0.88, with RMSE scores of 0.32. The DEA-RF and LSA-RF models, while effective, showed a marginally lower R-value of 0.85, with RMSE values of 0.45 and 0.44, respectively. Moreover, the SHapley Additive exPlanation (SHAP) results indicated that the degradation rates of air contaminants through photocatalysis were most notably influenced by factors such as the reactor sizes, photocatalyst dosage, humidity, and intensity.

Autonomous Landing of Unmanned Aerial Vehicles: Hybrid Metaheuristic Aided Detection and Extended Kalman Filter for Localization

Autonomous Landing of Unmanned Aerial Vehicles: Hybrid Metaheuristic Aided Detection and Extended Kalman Filter for Localization

Leveraging Hybrid Adaptive Sine Cosine Algorithm with Deep Learning for Arabic Poem Meter Detection

Poetry is a significant aspect of any language. Many cultures and the history of nations are recognized in poems. Compared to prose, each poem has a rhythmic structure that is quite different. The language has its set of lyrical structures for poems, known as meters. Detecting the meters of Arabic poems is a complicated and lengthy procedure. The text must be encrypted using the Arudi method to classify the poem's meter, which requires complex rule-based transformation before another set of rules classifies the meters. Applying deep learning (DL) to meter classification in Arabic poems includes constructing a neural network to discern rhythmic patterns inherent in various meters. The model can extract essential features, like word lengths or syllable patterns, by tokenizing and preprocessing text datasets. Architectures such as Long Short-Term Memory Networks (LSTM) or Recurrent Neural Networks (RNNs) are fitting solutions to capture temporal relations in poetic verses. This research introduces a Hybrid Meta-heuristics with Deep Learning for the Arabic Poem Meter Detection and Classification (HMDL-APMDC) model. The main intention of the HMDL-APMDC system is to recognize various kinds of meters in Arabic poems. The HMDL-APMDC technique primarily preprocesses the input dataset to make it compatible with the classification process. Besides, the HMDL-APMDC technique applies Convolution and Attention with a Bi-directional Gated Recurrent Unit (CAT-BiGRU) for the automated recognition of meter classes. Furthermore, the adaptive sine-s-cosine particle swarm optimization (ASCA-PSO) algorithm is applied to optimize the hyperparameter tuning of the CAT-BiGRU model, enhancing the meter detection results. A detailed simulation analysis is made to highlight the improved performance of the HMDL-APMDC technique. The empirical outcomes stated that the HMDL-APMDC technique had a superior outcome of 98.53% over recent models under the MetRec dataset.

Seismic Response Prediction of Porcelain Transformer Bushing Using Hybrid Metaheuristic and Machine Learning Techniques: A Comparative Study

Although seismic response predictions are widely used for engineering structures, their applications in electrical equipment are rare. Overstressing at the bottom of the porcelain insulators during seismic events has made power transformer bushings in substations prone to failure. Thus, this paper proposed and compared six integrated machine learning (ML) models for seismic stress response predictions for porcelain transformer bushings using easily monitored acceleration responses. Metaheuristic algorithms such as particle swarm optimization were employed for architecture tuning. Prediction accuracies for stress response values and classifications were evaluated. Finally, shaking table tests and simulation analyses for a 1100 kV bushing were implemented to validate the accuracy of the six ML models. The results indicated that the proposed ML models can quickly forecast the maximum stress experienced by a porcelain bushing during earthquakes. Swarm intelligence evolutionary technologies could quickly and automatically aid in the retrofitting of architecture for the ML models. The K-nearest neighbor regression model had the best level of prediction accuracy among the six selected ML models for experimental and simulation validations. ML prediction models have clear benefits over frequently used seismic analytical techniques in terms of speed and accuracy for post-earthquake emergency relief in substations.

Hybrid metaheuristic for the dial-a-ride problem with private fleet and common carrier integrated with public transportation

Hybrid metaheuristic for the dial-a-ride problem with private fleet and common carrier integrated with public transportation

Performance and Comparison of Voltage Compensation Based on Hybrid Metaheuristic Technique Tuned Z-DVR

Performance and Comparison of Voltage Compensation Based on Hybrid Metaheuristic Technique Tuned Z-DVR