Metaheuristic Optimization Algorithm Research Articles

Election Optimizer Algorithm: A New Meta-Heuristic Optimization Algorithm for Solving Industrial Engineering Design Problems

This paper introduces the election optimization algorithm (EOA), a meta-heuristic approach for engineering optimization problems. Inspired by the democratic electoral system, focusing on the presidential election, EOA emulates the complete election process to optimize solutions. By simulating the presidential election, EOA introduces a novel position-tracking strategy that expands the scope of effectively solvable problems, surpassing conventional human-based algorithms, specifically, the political optimizer. EOA incorporates explicit behaviors observed during elections, including the party nomination and presidential election. During the party nomination, the search space is broadened to avoid local optima by integrating diverse strategies and suggestions from within the party. In the presidential election, adequate population diversity is maintained in later stages through further campaigning between elite candidates elected within the party. To establish a benchmark for comparison, EOA is rigorously assessed against several renowned and widely recognized algorithms in the field of optimization. EOA demonstrates superior performance in terms of average values and standard deviations across the twenty-three standard test functions and CEC2019. Through rigorous statistical analysis using the Wilcoxon rank-sum test at a significance level of 0.05, experimental results indicate that EOA consistently delivers high-quality solutions compared to the other benchmark algorithms. Moreover, the practical applicability of EOA is assessed by solving six complex engineering design problems, demonstrating its effectiveness in real-world scenarios.

MCN portfolio: An efficient portfolio prediction and selection model using multiserial cascaded network with hybrid meta-heuristic optimization algorithm

ABSTRACT Generally, financial investments are necessary for portfolio management. However, the prediction of a portfolio becomes complicated in several processing techniques which may cause certain issues while predicting the portfolio. Moreover, the error analysis needs to be validated with efficient performance measures. To solve the problems of portfolio optimization, a new portfolio prediction framework is developed. Initially, a dataset is collected from the standard database which is accumulated with various companies’ portfolios. For forecasting the benefits of companies, a Multi-serial Cascaded Network (MCNet) is employed which constitutes of Autoencoder, 1D Convolutional Neural Network (1DCNN), and Recurrent Neural Network (RNN) is utilized. The prediction output for the different companies is stored using the developed MCNet model for further use. After predicting the benefits, the best company with the highest profit is selected by Integration of Artificial Rabbit and Hummingbird Algorithm (IARHA). The major contribution of our work is to increase the accuracy of prediction and to choose the optimal portfolio. The implementation is conducted in Python platform. The result analysis shows that the developed model achieves 0.89% and 0.56% regarding RMSE and MAE measures. Throughout the analysis, the experimentation of the developed model shows enriched performance.

Proactive dam operation based on inflow prediction by modified long short-term memory for improving resilience

The operation of dams can have a significant downstream impact, causing major flood damage. For efficient dam operation, it is essential to accurately predict the inflow using deep learning such as a long short-term memory (LSTM). The modified LSTM (MLSTM) with new optimizers combined with a metaheuristic optimization algorithm was suggested. The MLSTM were trained using rainfall, water level, and discharge of the Yongdam Dam from 2010 to 2019 as input data and the inflow of the Daecheong Dam as output data. Normalization and time lagged cross correlation (TLCC) were applied as data preprocessing. The inflow of the Daecheong Dam in 2020 was predicted using rainfall, water level, and discharge of the Yongdam Dam. Compared to LSTM, MLSTM showed an accuracy improvement of about 63.85–83.34% based on the mean square error (MSE), which is a loss function. The results of proactive dam operation were calculated based on the predicted inflow considering TLCC. Compared to the existing operation, the peak water level of the reservoir was reduced from 77.54 m to 77.17 m, and the resilience was improved from 0.9225 to 0.9623. New dam operation can maintain low water levels of reservoir and improve resilience to prevent flood damage.

Convex Combination Search Algorithm: A Novel Metaheuristic Optimization Algorithm for Solving Global Optimization and Engineering Design Problems

In this paper, a novel metaheuristic optimization algorithm (MHOA) called convex combination search (CCS) is proposed as a solution to global optimization problems and engineering design problems. CCS is based on a combination of rules that depend upon the concept of the linear convex combination. These rules are mathematically modeled to guarantee the variety of solutions at the initialization stage and achieve equilibrium between exploitation, exploration capabilities at the generation stage, the algorithm’s convergence, and robustness. A detailed mathematical model of the algorithm is offered. As an advantage for the CCS algorithm, it requires just two parameters which are the population size and the number of generations for determining the global optimal solution of any optimization problem. The effectiveness of the suggested algorithm is investigated on 17 unconstrained multimodal test functions, and 7 constrained benchmark problems having different characteristics. In addition, five engineering design challenges are resolved to confirm the robustness and dependability of CCS in resolving engineering applications. The efficiency and competitiveness of the proposed algorithm were illustrated in comparison with other methods. A statistical analysis of the results has been carried out to illustrate the competitiveness and power effectiveness of the proposed algorithm. Finally, the sensitivity of the CCS parameters is presented to show the sensitivities of these parameters to the performance of the proposed algorithm.

Estimation of virus transport parameters in both unsaturated-saturated zone using numerical simulation and flower pollination algorithm

To develop a virus transport model considering both the unsaturated and saturated zone, it is essential to consider separate parameters for both the zone. This is because the virus fate and transport process depends on the degree of saturation. As such, this paper introduces a parameter estimation model that simultaneously identifies virus transport parameters in an unconfined aquifer for both the unsaturated and saturated zones. The proposed model utilizes a linked simulation-optimization technique to minimize errors between observed and simulated virus concentrations. The simulation model used in this study is termed as “variable detachment model,” this is because the simulation component of this approach incorporates varying adsorption rates, which are influenced by the degree of saturation. This study also highlights the complexities involved in parameter estimation for virus transport models, such as non-uniqueness, instability, and non-identifiability. These challenges arise when multiple parameter combinations can produce similar model predictions, leading to difficulty in determining the actual parameter values. To address these challenges, a meta-heuristic optimization algorithm, Flower Pollination Algorithm (FPA), is used, effectively solving such non-convex problems. The model could estimate all the parameters accurately except the inactivation rates of viruses. Later when the global sensitivity analysis was carried out to understand the relative importance of each parameter, inactivation rates were found to be less influential to the model output. Thus, FPA based parameter estimation model can be used as an alternative for estimating the virus transport parameters in groundwater aquifers.

Gravity profiles interpretation applying a metaheuristic particle optimization algorithm of mineralized bodies resembled by finite elements

The interpretation of gravity anomalies is crucial for identifying subsurface mineralized targets and understanding the density variations between the targets and the surrounding structures. To confirm the presence of ore and mineral targets, simple geometric bodies are often used. One of the commonly used global metaheuristic algorithms for gravity data analysis is the particle optimization algorithm. In this study, we employed this method to determine the parameters of buried bodies that resemble finite vertical cylinders by inferring gravity anomalies profiles (amplitude coefficient, depth to top, depth to bottom, origin, and length of the target representing the difference between two depths). The algorithm utilizes particle movement to identify the best way to reach the global or optimum solution. The algorithm's performance was evaluated on synthetic-examples with and without noise (5% and 10% levels) and also verified on a real dataset for mineral exploration from Canada. The results showed that the algorithm's stability and accuracy were not affected by the presence of noise and multi-models. Moreover, the field case results were consistent with the existing geological information, borehole data, and previously published outcomes.

Improvement of Commercial Vehicle Seat Suspension Employing a Mechatronic Inerter Element

To further improve the ride comfort of commercial vehicles, a seat ISD (Inerter–Spring–Damper) suspension utilizing a mechatronic inerter is proposed in this paper. Firstly, a five-DOF (degree-of-freedom) commercial vehicle seat ISD model was built. Then, the positive real network constraint conditions of a biquadratic impedance transfer function were determined, and the meta-heuristic intelligent optimization algorithm was used to solve the parameters. According to the solution, the impedance transfer function was obtained and the specific network structure was realized by network synthesis. Lastly, this study compares the vibration isolation performance of the mechatronic ISD suspension of the vehicle seat with that of a passive suspension. In comparison to passive seat suspension, the seat mechatronic ISD suspension reduces seat vibration transmissibility by 16.33% and vertical acceleration by 16.78%. Results indicate that the new suspension system can be an effective improvement in ride comfort.

Novel Initialization Functions for Metaheuristic-Based Online Virtual Network Embedding

Virtual network embedding (VNE) is the process of allocating resources in a substrate (i.e. physical) network to support virtual networks optimally. The VNE problem is an NP-hard problem that has been studied for more than a decade in the continuous seek to maximize the revenue of physical infrastructures with more efficient VNE solutions. Metaheuristics have been widely used in online VNE as they incorporate mechanisms to avoid local optimum solutions, explore larger search spaces, and keep acceptable execution times. All metaheuristic optimization algorithms require initialization for which the vast majority of online VNE solutions implement random initialization. This paper proposes three novel initialization functions namely, Initialization Based on Node Selection (IFNS), Initialization Function Based on Community Detection (IFCD), and Initialization Function Based on Previous Solutions (IFPS), intending to enhance the performance of the online VNE process. Through simulation, our initialization functions have been proven to enhance the acceptance rate, revenue, and revenue-to-cost metrics of the VNE process. The enhancements achieved by our initialization functions are statistically significant and their implementation does not add computational overhead to the classic VNE approaches.

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.

Metaheuristic optimization algorithms comparison adopted for the profit maximization of electricity market participants

The electricity market faces numerous challenges due to the growing demand for energy, increasing penetration of renewable energy sources, and the need for grid reliability and efficiency. To address these challenges, optimization algorithms have emerged as essential tools for optimizing various aspects of the electricity market, including generation, transmission, distribution, and demand-side management. The review can be done by providing an overview of the key components and challenges of the electricity market, including generation dispatch, unit commitment, economic dispatch, transmission network optimization, and demand response management. It then systematically examines a wide range of optimization techniques employed in addressing these challenges, including linear programming, mixed-integer linear programming, nonlinear programming, dynamic programming, genetic algorithms, particle swarm optimization, simulated annealing, and machine learning-based approaches. This paper presents a comparison of optimization algorithms, RCEDUMDA (Ring-Cellular Encode-Decode Univariate Marginal Distribution Algorithm) and CL_HC2RCEDUMDA (Hill Climbing to Ring Cellular Encode-Decode Univariate Marginal Distribution Algorithm) for the profit maximization of Electricity Market consumers & prosumers.

Cleaner fish optimization algorithm: a new bio-inspired meta-heuristic optimization algorithm

Cleaner fish optimization algorithm: a new bio-inspired meta-heuristic optimization algorithm

A Chlorophyll-a Concentration Inversion Model Based on Backpropagation Neural Network Optimized by an Improved Metaheuristic Algorithm

Chlorophyll-a (Chl-a) concentration monitoring is very important for managing water resources and ensuring the stability of marine ecosystems. Due to their high operating efficiency and high prediction accuracy, backpropagation (BP) neural networks are widely used in Chl-a concentration inversion. However, BP neural networks tend to become stuck in local optima, and their prediction accuracy fluctuates significantly, thus posing restrictions to their accuracy and stability in the inversion process. Studies have found that metaheuristic optimization algorithms can significantly improve these shortcomings by optimizing the initial parameters (weights and biases) of BP neural networks. In this paper, the adaptive nonlinear weight coefficient, the path search strategy “Levy flight” and the dynamic crossover mechanism are introduced to optimize the three main steps of the Artificial Ecosystem Optimization (AEO) algorithm to overcome the algorithm’s limitation in solving complex problems, improve its global search capability, and thereby improve its performance in optimizing BP neural networks. Relying on Google Earth Engine and Google Colaboratory (Colab), a model for the inversion of Chl-a concentration in the coastal waters of Hong Kong was built to verify the performance of the improved AEO algorithm in optimizing BP neural networks, and the improved AEO algorithm proposed herein was compared with 17 different metaheuristic optimization algorithms. The results show that the Chl-a concentration inversion model based on a BP neural network optimized using the improved AEO algorithm is significantly superior to other models in terms of prediction accuracy and stability, and the results obtained via the model through inversion with respect to Chl-a concentration in the coastal waters of Hong Kong during heavy precipitation events and red tides are highly consistent with the measured values of Chl-a concentration in both time and space domains. These conclusions can provide a new method for Chl-a concentration monitoring and water quality management for coastal waters.

Predicting the Compressive Strength of Ultra-High-Performance Concrete Based on Machine Learning Optimized by Meta-Heuristic Algorithm

Ultra-high-performance concrete (UHPC) is a recently developed material which has attracted considerable attention in the field of civil engineering because of its outstanding characteristics. One of the key factors in concrete design is the compressive strength (CS) of UHPC. As one of the most potent tools in artificial intelligence (AI), machine learning (ML) can accurately predict concrete’s mechanical properties. Hyperparameter tuning is crucial in ensuring the prediction model’s reliability. However, it is a complex work. The purpose of this study is to optimize the CS prediction method for UHPC. Three ML methods, random forest (RF), support vector machine (SVM), and k-nearest neighbor (KNN), are selected to predict the CS of UHPC. Among them, the RF model demonstrates superior predictive accuracy, with the testing dataset R2 of 0.8506. In addition, three meta-heuristic optimization algorithms, particle swarm optimization (PSO), beetle antenna search (BAS), and snake optimization (SO), are utilized to optimize the prediction model hyperparameters. The R2 values for the testing dataset of SO-RF, PSO-RF, and BAS-RF are 0.9147, 0.8529, and 0.8607, respectively. The results indicate that SO-RF exhibits the highest predictive performance. Furthermore, the importance of input parameters is evaluated, and the findings prove the feasibility of the SO-RF model. This research enriches the prediction method of the CS of UHPC.

Plant Leaf Disease Detection Using Metaheuristic Optimization Algorithms and Deep Learning

Plant Leaf Disease Detection Using Metaheuristic Optimization Algorithms and Deep Learning

Metaheuristic optimization based- ensemble learners for the carbonation assessment of recycled aggregate concrete

This study addresses the enhanced prevalence of carbonation, a process accelerating steel reinforcement corrosion in recycled aggregate concrete (RAC) compared to natural aggregate concrete. Traditional carbonation depth assessment methods in RAC are noted for being labor-intensive, costly, and requiring specialized expertise. There is a noted deficiency in the application of machine learning techniques for accurately predicting carbonation depth in RAC, a gap this study aims to fill. Utilizing the extreme gradient boosting (XGBoost) technique, recognized for its efficacy in ensemble machine learning, this study innovates in modeling carbonation depth in RAC. It emphasizes the criticality of hyperparameter optimization of the XGBoost algorithm for maximizing model accuracy. To achieve this, three novel metaheuristic optimization algorithms, including reptile search algorithm (RSA), Aquila optimizer (AO), and arithmetic optimization algorithm (AOA), were introduced as global optimizers for tunning the XGBoost hyperparameters. The study was underpinned by a comprehensive database compiled from extensive literature, facilitating the development of an accurate RAC carbonation depth model. Through rigorous evaluations, including sensitivity analyses, the Wilcoxon signed-rank test, and runtime comparisons, the synthesized models demonstrated exceptional accuracy, with coefficients of determination exceeding 0.95. The XGBoost-AO algorithm, in particular, showcased superior performance, with the XGBoost-RSA algorithm providing efficient predictions considering runtime. SHapley Additive exPlanations (SHAP) interpretation highlighted environmental conditions as significant carbonation depth influencers. A user-friendly graphical user interface was developed, enhancing the practical utility of the findings for predicting carbonation depth progression in RAC over time. This research significantly advances the predictive accuracy for carbonation depth in RAC, contributing to the sustainable management of concrete infrastructures and emphasizing the integration of advanced machine learning techniques with metaheuristic optimization for environmental and structural engineering advancements.

Simulating long-term energy consumption prediction in campus buildings through enhanced data augmentation and metaheuristic-optimized artificial intelligence

Forecasting long-term energy consumption is essential to enhance resource utilization and promote sustainability in campus buildings. This study employs a comprehensive approach, integrating artificial intelligence models, data augmentation techniques, and metaheuristic optimization algorithms to predict energy consumption one year in advance. The predictive models undergo training and validation using historical energy consumption data and weather patterns. However, predicting energy consumption a year ahead presents challenges due to instability and limited training data. To tackle this, the dataset is enriched using diverse data augmentation techniques. A metaheuristic algorithm, Jellyfish Search Optimizer (JSO), is also applied to refine the best predictive models and augmentation techniques. Results demonstrate a notable enhancement in prediction accuracy by integrating data augmentation techniques, convolutional neural network models, and metaheuristic optimization algorithms. The top-performing model, JSO-MobileNet with random noise injection, achieves a Mean Absolute Percentage Error (MAPE) value of 11.95% in forecasting energy consumption for campus buildings one year ahead. This study contributes to affirming the effectiveness of the metaheuristic optimization algorithm in fine-tuning artificial intelligence models integrated with data augmentation techniques, offering a practical approach to forecasting long-term energy consumption in campus buildings. The research outlines a systematic process for identifying the optimal predictive model, the most suitable data augmentation method, and the best set of hyperparameters.

A Review On Early Detection Of Chronic Kidney Disease

Early detection of Chronic Kidney Disease (CKD) is critical for timely intervention and effective treatment. Deep learning algorithms have demonstrated promise in medical applications, including disease detection. In this study, we propose a deep learning-based system for early CKD detection using the Chronic Kidney Disease dataset from Kaggle. Additionally, we incorporate the Grasshopper Optimization Algorithm (GOA) for feature selection to enhance the system's performance and interpretability. Our system employs a convolutional neural network (CNN) architecture to analyze clinical and laboratory attributes from the CKD dataset, obtained from Kaggle, consisting of 4,000 instances with 25 attributes. These attributes encompass patient demographics, blood tests, and medical history, providing a comprehensive representation of CKD-related factors. To improve the system's performance, we integrate the GOA for feature selection. The GOA is a nature-inspired metaheuristic optimization algorithm that mimics the foraging behavior of grasshoppers. It aims to identify the most relevant attributes associated with CKD from the dataset. By selecting a subset of informative features, we enhance the model's predictive accuracy and reduce overfitting. During the training phase, the CNN learns to automatically extract relevant features and patterns associated with CKD from the selected attributes. Additionally, data preprocessing techniques such as normalization and feature scaling are applied to further improve the model's performance and generalizability. To evaluate the system's performance, we conduct experiments using a separate test dataset comprising 1,000 instances from the CKD dataset. The incorporation of the GOA for feature selection in our deep learning system not only improves its performance but also enhances interpretability. By identifying the most relevant attributes associated with CKD, we focus on key biomarkers and risk factors, enhancing the system's accuracy and providing valuable insights into the disease. Our research showcases the potential of deep learning algorithms, coupled with GOA-based feature selection, for early CKD detection. By leveraging the Kaggle CKD dataset and incorporating the GOA, we contribute to improving the accuracy and applicability of the system in real-world clinical settings. To handle Big data we are proposing to implement this problem on Pyspark one of the Big data computational environments for effective learning. In this platform, we can dynamically scale the infrastructure as per the demand of the data. Ultimately, our work aims to advance the early detection and management of CKD, leading to improved patient outcomes and more effective healthcare interventions.

An innovative intelligent design method of alkali-activated foamed geopolymer: Mixture optimization and performance prediction

The complex design parameters and lack of standard mix design methods generate a difficulty in the high efficiently preparation of alkali-activated foamed geopolymer (AAFG). Therefore, this paper proposes an innovative intelligent design method of AAFG based on machine learning (ML) and Metaheuristic Optimization Algorithms (MOA). The effects of twelve factors on drying density (DD) and compressive strength (CS) of AAFG are revealed. The DD and CS predictive models of AAFG with high accuracy (R2 = 0.96 and 0.84) and strong generalization are trained through Random Forest (RF). The enlargements of proportions of granulated blast furnace slag (GBFS) and metakaolin (MK) in precursors can improve the CS but are not conducive to reduction of DD. Higher proportion of fly ash (FA) in precursors can efficiently reduce DD but can weaken the CS. Higher or lower Na2O content, silicate modulus (Ms) and water to binder (W/B) can reduce DD and CS with the optimal foaming conditions of 20 %, 1.0 and 0.75. More additions of foaming agents and foam stabilizers can reduce DD, while the CS has a remarkable degradation. Higher or lower foaming temperature (FT) is both not beneficial for the reduction of DD and enhancement of CS with the optimal FT of 60 °C. The intelligent design system of AAFG developed by RF and Slime Mould Algorithm (SMA) has easy operability, high efficiency, and good stability. The optimal preparation parameters can be efficiently acquired and the corresponding performances can be quickly and accurately predicted.

An improved constant current step-based grey wolf optimization algorithm for photovoltaic systems

In this paper, an improved constant current step based on the grey wolf optimization (CCS-GWO) algorithm for photovoltaic systems is investigated. The development of grey wolf optimization has been widely spread over photovoltaic applications. This method is one of the metaheuristic swarm optimization algorithm groups inspired by an optimum means of chasing prey by grey wolves. The proposed technique applies constant current steps to the pack of wolves (alpha, beta, and omega) by monitoring the average of the internal current step and external current step in order to target the leader alpha wolf position. Moreover, the proposed technique solves the convergence process issues, low convergence speed, and premature local optima problems of the traditional GWO algorithm. This CCS-GWO algorithm accurately tracks the maximum power point from the photovoltaic systems for load charging in different partial shading conditions (PSCs). A number of standard benchmark functions are presented with low average cost functions and their corresponding standard deviation values. The simulation results revealed that the proposed CCS-GWO approach outperforms the existing GWO and GA algorithms in terms of efficiency (98.55%) and tracking time (0.3 s).

Multi-swarm surrogate model assisted PSO algorithm to minimize distribution network energy losses

The computational analysis of real-world engineering problems often relies on time-consuming simulation calculations. This presents challenges in balancing computational burden and precision when applying traditional metaheuristic algorithms to large-scale complex problems. While there have been numerous surrogate-assisted metaheuristic optimization algorithms proposed, most of them are designed for solving expensive problems within 30–50 dimensions. In this paper, a novel approach called Parallel-multi-swarm Gaussian Process Regression surrogate-based particle swarm optimization (Parallel-MS-GPRS-PSO) is proposed for high-dimensional problems. This approach combines a standard particle swarm optimization (PSO) algorithm, multi-swarm cooperative PSO (MSCPSO), and a GPR surrogate-based PSO (PSO-GPR). By integrating PSO-GPR and MSCPSO, the proposed method aims to effectively explore and exploit the search space while enhancing the global and local performance of the surrogate model. To validate the effectiveness of the proposed algorithm, it is compared with several existing PSO algorithms on seven benchmark functions and an engineering problem. The results illustrate that the Parallel-MS-GPRS-PSO approach outperforms the existing algorithms. Moreover, the method shows promising performance in computationally expensive engineering optimization problems with 288 variables.