Optimization Algorithm Research Articles

Deep feature fusion with computer vision driven fall detection approach for enhanced assisted living safety

Assisted living facilities cater to the demands of the elderly population, providing assistance and support with day-to-day activities. Fall detection is fundamental to ensuring their well-being and safety. Falls are frequent among older persons and might cause severe injuries and complications. Incorporating computer vision techniques into assisted living environments is revolutionary for these issues. By leveraging cameras and complicated approaches, a computer vision (CV) system can monitor residents’ movements continuously and identify any potential fall events in real time. CV, driven by deep learning (DL) techniques, allows continuous surveillance of people through cameras, investigating complicated visual information to detect potential fall risks or any instances of falls quickly. This system can learn from many visual data by leveraging DL, improving its capability to identify falls while minimalizing false alarms precisely. Incorporating CV and DL enhances the efficiency and reliability of fall detection and allows proactive intervention, considerably decreasing response times in emergencies. This study introduces a new Deep Feature Fusion with Computer Vision for Fall Detection and Classification (DFFCV-FDC) technique. The primary purpose of the DFFCV-FDC approach is to employ the CV concept for detecting fall events. Accordingly, the DFFCV-FDC approach uses the Gaussian filtering (GF) approach for noise eradication. Besides, a deep feature fusion process comprising MobileNet, DenseNet, and ResNet models is involved. To improve the performance of the DFFCV-FDC technique, improved pelican optimization algorithm (IPOA) based hyperparameter selection is performed. Finally, the detection of falls is identified using the denoising autoencoder (DAE) model. The performance analysis of the DFFCV-FDC methodology was examined on the benchmark fall database. A widespread comparative study reported the supremacy of the DFFCV-FDC approach with existing techniques.

Revolutionizing PV grid integration: Metaheuristic optimization of fractional PI controllers in T-type neutral point piloted inverters for enhanced performance

This study investigates the efficacy of FOPI regulators as a substitute for conventional proportional integral (PI) controllers in grid-connected PV inverters. The research's objective is to improve the dynamic efficiency of these systems by integrating intelligent optimization techniques that utilize both PI and FOPI controllers and employing different metaheuristic optimization procedures. The study introduces four new optimization algorithms: the Tyrannosaurus Optimization Algorithm (TROA), the Nutcracker Optimization Algorithm (NOA), the Golden Eagle Optimizer (GEO), and the Jellyfish Search Optimizer (JSO). These are made to meet the needs of multi-objective optimization. The proposal suggests using two PI/FOPI regulators to regulate both voltage and amperage provided by the multilayer inverter. The proposal employs a T-type three-level inverter, known for its superior conversion efficiency over conventional inverters. Matlab-Simulink simulations demonstrate that the Nutcracker optimization algorithm (NOA) outperforms other metaheuristic techniques for optimizing dynamic behavior, such as overshoot, settling time, execution, and rising time. Comparisons with existing methods, such as the Manta Rays Foraging Optimization (MRFO) and Grey Wolf Optimizer (GWO), show that all four new algorithms consistently outperform these existing algorithms. The data suggest that using NOA improves stability, efficiency, and power factor while reducing the inverter's THD.

Prediction and optimization of surface waviness of WAAM components using a hybrid Rank-Gaussian PSO algorithm and ANN

Surface waviness is crucial for the quality of components produced via the wire and arc additive manufacturing (WAAM) process. This study presents a novel method for predicting surface waviness, employing an advanced model to optimize process parameter configurations using a combination of Rank-Gaussian particle swarm optimization (RGPSO) and an Artificial Neural Network (ANN). The novelty of this process is that the RGPSO not only optimizes the hyperparameters of the ANN model to enhance prediction performance, but also addresses surface waviness optimization. The RGPSO algorithm's optimization performance is evaluated using 23 benchmark functions, demonstrating competitiveness against nine comparative meta-heuristic algorithms. Experimental data on surface waviness from the literature are utilized to train, test and validate three different prediction models, including a standalone ANN model, a PSO optimized ANN (PSO-ANN) model, and an RGPSO optimized ANN (RGPSO-ANN) model. The results indicate that the developed RGPSO-ANN model achieves the highest accuracy in terms of the metrics RMSE (0.019), R (0.996), R20.990,MAE (0.013), RMSLE0.013,and MAPE (3.46 %). It performs better than the PSO-ANN model (RMSE 0.026, R 0.975, R20.982,MAE 0.019, RMSLE0.019,and MAPE 5.73 %), and better than the ANN (RMSE 0.046, R0.991, R20.944,MAE 0.034, RMSLE0.032 and MAPE 9.31 %). The RGPSO, PSO and other optimization algorithms are then applied to minimize the surface waviness of a WAAM component. RGPSO achieved the optimal value (0.1631 mm), which corresponds to a 12.6 % reduction compared to the best value obtained using PSO.

Optimizing Additive Manufacturable Structures with Computer Vision to Enhance Material Efficiency and Structural Stability

This study introduces an innovative technique that merges computer vision with topology optimization to advance additive manufacturing. Employing advanced photogrammetry software, we obtain high-resolution images of the design domain, which are then used to develop accurate 3D models through meticulous scanning procedures. These models are transformed into an STL file format and remeshed using an adaptive algorithm within COMSOL 5.3 Multiphysics, facilitated by a custom MATLAB 2023 application. This integration achieves the optimal mesh resolution and precision in analytical assessments. We applied this technique to the design of a concrete pillar for 3D printing, targeting a 75% reduction in volume to improve the material efficiency and structural stability—critical factors for extraterrestrial applications. The design, captured with a 360-degree camera array, guided the MATLAB-based topology optimization process. By combining MATLAB’s optimization algorithms with COMSOL’s meshing and finite element analysis tools, we investigated various material-efficient configurations. The findings reveal a substantial volume reduction, especially in the central region of the design, effectively optimizing material utilization while preserving structural integrity. The optimization algorithm exhibited a swift and stable convergence, reaching near-optimal solutions within approximately 20 iterations.

Research on Fault Prediction and Diagnosis of Mechanical and Electrical Equipment in Construction Machinery based on Big Data Technology

This article aims to explore how to use big data technology for fault prediction and diagnosis of mechanical and electrical equipment in construction machinery. Through steps such as data collection and storage, data feature extraction and selection, and data analysis and processing, a fault prediction and diagnosis model is constructed to achieve real-time monitoring and early warning. The accuracy and efficiency of fault prediction are improved through decision feedback and algorithm optimization.

An Exponential Map-based Whale Optimization Algorithm (Exp-WOA) for Optimization

This research work offers three variations of the Whale Optimization Algorithm (WOA) based on exponential chaotic maps, namely Logistic-Exponential-Logistic WOA (LEL-WOA), Logistic-Exponential-Sinusoidal WOA (LES-WOA), and Logistic-Exponential-Tent WOA (LET-WOA). The WOA with an exponential chaos-based mechanism is developed in this study to overcome the poor rate of convergence of the WOA and to prevent getting caught in local optimal solutions while dealing with the challenges. An exponential chaotic mechanism was employed in this research to initialize the agents and control the parameters of the exploration and exploitation phases of WOA. The proposed methodologies (Exp-WOA) are evaluated using twenty-three widely recognized test functions. The results demonstrate that the given solutions can enhance the performance of WOA by achieving optimal (minimum) values. The findings also indicate that LEL-WOA and LES-WOA exhibit faster convergence than WOA.

SR4KVQA: Video quality assessment database and metric for 4K super-resolution

The quality assessment for 4K super-resolution (SR) videos can be conducive to the optimization of video SR algorithms. To improve the subjective and objective consistency of the SR quality assessment, a 4K video database and a blind metric are proposed in this paper. In the database SR4KVQA, there are 30 4K pristine videos, from which 600 SR 4K distorted videos with mean opinion score (MOS) labels are generated by three classic interpolation methods, six SR algorithms based on the deep neural network (DNN), and two SR algorithms based on the generative adversarial network (GAN). The benchmark experiment of the proposed database indicates that video quality assessment (VQA) of the 4K SR videos is challenging for the existing metrics. Among those metrics, the Video-Swin-Transformer backbone demonstrates tremendous potential in the VQA task. Accordingly, a blind VQA metric based on the Video-Swin-Transformer backbone is established, where the normalized loss function and optimized spatio-temporal sampling strategy are applied. The experiment result manifests that the Pearson linear correlation coefficient (PLCC) and Spearman rank-order correlation coefficient (SROCC) of the proposed metric reach 0.8011 and 0.8275 respectively on the SR4KVQA database, which outperforms or competes with the state-of-the-art VQA metrics. The database and the code proposed in this paper are available in the GitHub repository, https://github.com/AlexReadyNico/SR4KVQA.

A surrogate-assisted expensive constrained multi-objective global optimization algorithm and application

Expensive multi-objective optimization problems (MOPs) have seen the successful applications of surrogate-assisted evolutionary algorithms (SAEAs). Nevertheless, the majority of SAEAs are developed for costly unconstrained optimization, and costly constrained MOPs (CMOPs) have received less attention. Therefore, this article proposes a surrogate-assisted global optimization algorithm (named CTEA) for solving CMOPs within a very limited number of fitness evaluations. The proposed algorithm combines two selection frameworks, a bi-level selection framework, and an adaptive sampling framework, to enhance optimization performance. Leveraging on a constraint-improving strategy and a Pareto-based three-indicator criterion (convergence, constraint, and diversity indicators) at the different levels, the proposed bi-level selection framework can select more promising solutions. Moreover, an adaptive sampling framework is developed to prioritize objective and constraint functions and select the candidate solutions for real function evaluations according to the priority. Experimental results demonstrate that the proposed CTEA exhibits superior performance when compared with five state-of-the-art algorithms, achieving the best results in 61.9 % out of the 64 test instances. Finally, CTEA is applied to the multidisciplinary design optimization of blended-wing-body underwater gliders, and an impressive solution set is obtained.

Improved whale optimization algorithm towards precise state-of-charge estimation of lithium-ion batteries via optimizing LSTM

Accurate state-of-charge (SOC) estimation is a crucial part of the battery management system (BMS). However, conventional estimation methods are unable to capture the extremely complex dynamic characteristics of lithium-ion batteries. Besides, manually setting the optimal hyperparameters of models has many drawbacks. To address the problems, an (improved whale optimization algorithm) IWOA- (long short-term memory) LSTM model is proposed in this work. Utilizing the whale optimization algorithm (WOA) improved with four enhancement strategies (Gaussian chaotic mapping initialization, Nonlinear weight update, Lévy flight mechanism, and Elite opposition-based learning) to optimize the number of hidden layer nodes, the learning rate, and the number of iterations of LSTM model. It not only overcomes the shortcomings of artificially setting LSTM hyperparameters but also further boosts the learning ability of the IWOA-LSTM model, making the model more suitable for SOC estimation under different scenarios. The evaluation results show that the MAE of the proposed model for SOC estimation results is lower than 0.8 % under different temperatures and dynamic conditions. Compared with SOTA models, all MAE, RMSE, and MAPE of the proposed model substantially decline. Furthermore, the R2 of the estimation results using the LG dataset in Experiment Ⅲ is 98.65 %, suggesting the applicability of the proposed model to Li-ion batteries from various manufacturers. The experimental results demonstrate the proposed IWOA-LSTM model is suitable for accurate SOC estimation.

Enhancing connectivity and coverage in wireless sensor networks: a hybrid comprehensive learning-Fick’s algorithm with particle swarm optimization for router node placement

AbstractWireless Sensor Networks (WSNs) are essential for collecting and transmitting data in modern applications that rely on data, where effective network connectivity and coverage are crucial. The optimal placement of router nodes within WSNs is a fundamental challenge that significantly impacts network performance and reliability. Researchers have explored various approaches using metaheuristic algorithms to address these challenges and optimize WSN performance. This paper introduces a new hybrid algorithm, CFL-PSO, based on combining an enhanced Fick’s Law algorithm with comprehensive learning and Particle Swarm Optimization (PSO). CFL-PSO exploits the strengths of these techniques to strike a balance between network connectivity and coverage, ultimately enhancing the overall performance of WSNs. We evaluate the performance of CFL-PSO by benchmarking it against nine established algorithms, including the conventional Fick’s law algorithm (FLA), Sine Cosine Algorithm (SCA), Multi-Verse Optimizer (MVO), Salp Swarm Optimization (SSO), War Strategy Optimization (WSO), Harris Hawk Optimization (HHO), African Vultures Optimization Algorithm (AVOA), Capuchin Search Algorithm (CapSA), Tunicate Swarm Algorithm (TSA), and PSO. The algorithm’s performance is extensively evaluated using 23 benchmark functions to assess its effectiveness in handling various optimization scenarios. Additionally, its performance on WSN router node placement is compared against the other methods, demonstrating its competitiveness in achieving optimal solutions. These analyses reveal that CFL-PSO outperforms the other algorithms in terms of network connectivity, client coverage, and convergence speed. To further validate CFL-PSO’s effectiveness, experimental studies were conducted using different numbers of clients, routers, deployment areas, and transmission ranges. The findings affirm the effectiveness of CFL-PSO as it consistently delivers favorable optimization results when compared to existing methods, highlighting its potential for enhancing WMN performance. Specifically, CFL-PSO achieves up to a 66.5% improvement in network connectivity, a 16.56% improvement in coverage, and a 21.4% improvement in the objective function value when compared to the standard FLA.

Future energy landscapes: Analyzing the cost-effectiveness of nuclear-renewable integrated energy Systems in Retrofitting of coal power plants

Coal Power Plants (CPPs), despite their substantial contribution to global energy needs, pose significant environmental concerns due to Greenhouse Gas (GHG) emissions. Thus, the world has started thinking of alternative generation sources to replace CPPs. To replace CPPs, some energy generation resources must come into the scenario that can outshine the advantages of CPPs, such as easy availability of fuel, operational safety, and cost effectiveness. Concerning this matter, nuclear-renewable integrated systems can play a vital role as a potential replacement for CPPs. In this study, we systematically explore the transitional approach from CPPs to advanced energy systems and conduct an exhaustive comparative analysis focusing on three proposed energy system models: Greenfield, Coal-to-Nuclear (C2N), and Coal-to-Integrated Energy Systems (C2IES). Before conducting the comparative analysis, we determine the most feasible coal sites from Alaska, our surrogate location for this study, using a GIS-based nuclear reactor siting tool named “Siting Tool for Advanced Nuclear Development (STAND).” To carry out the comparative analysis among the proposed energy models for the selected coal site, we find out the optimal configuration of each system using a robust and recent nature-based metaheuristic optimization algorithm, Mountain Gazelle Optimization (MGO), complemented by another nature-based metaheuristic optimization algorithm, Particle Swarm Optimization (PSO), for validation. The key data used in this study include solar irradiance, temperature, wind speed, load profiles, and comprehensive cost data for each system component. Since the proposed energy models are highly complex and consider several assumptions, the key research findings are strengthened by performing a comprehensive sensitivity analysis. The base case results show that C2IES can reduce the Cost of Energy (COE) by roughly 65 % compared to Greenfield and C2N and ensure the utmost reliability of the energy system. Although this cost-saving margin contrasts in the sensitivity analysis across a range of scenarios, C2IES consistently offers the most cost-effective solution, highlighting its potential for sustainable energy transition.

Density-guided and Adaptive Update Strategy for Multi-objective Particle Swarm Optimization

Abstract As a powerful optimization technique, multi-objective particle swarm optimization (MOPSO) has been paid more and more attention by scientists. However, in more complex problems, MOPSO faces the challenges of weak global search ability and easy to fall into local optimality. To address these challenges and obtain better solutions, people have proposed many variants. In this study, a density-guided and adaptive update strategy for multi-objective particle swarm optimization (DAMOPSO) is proposed. First, an adaptive grid is used to determine the mutation particles and guides. Then the Cauchy mutation operator is performed for the poorly distributed particles to expand the search space of the population. Additionally, the strategy of non-dominated sorting and hyper-region density are devised for maintaining external archives, which contribute to the uniform distribution of optimal solutions. Finally, an adaptive detection strategy based on the adjustment coefficient and conversion efficiency is designed to update the flight parameters. These approaches not only speed up the convergence of algorithms, but also balance exploitation and exploration more effectively. The proposed algorithm is compared with several representative multi-objective optimization algorithms on 22 benchmark functions; meanwhile, statistical tests, ablation experiments, analysis of stability and complexity are also performed. The experimental results demonstrate DAMOPSO is more competitive than other comparison algorithms.

Overview of Startups Developing Artificial Intelligence for the Energy Sector

The energy industry is experiencing a major change due to fast progress in artificial intelligence (AI). Startup companies in this revolution use AI technologies like Machine Learning (ML), predictive analytics, and optimization algorithms to improve energy efficiency, optimize grid management, and incorporate renewable energy sources. AI-powered solutions allow for a more accurate prediction of demand, immediate monitoring, and automated decision-making processes, significantly enhancing operational efficiency and sustainability. Through promoting a more effective energy system, these advancements play a vital role in the worldwide battle against climate change and carbon dioxide emissions. Adding to the progress of AI, quantum computing (QC) shows great potential despite being a nascent area. The collaboration of AI and QC is poised to transform the energy industry by offering unmatched computational capabilities. This blend of technologies can tackle intricate energy obstacles like enhancing power grids and enhancing battery storage, which traditional computers cannot currently handle. Combining QC with AI speeds up innovation, providing advanced solutions that improve the resilience and efficiency of energy networks. This paper discusses the latest advancements, possible effects, and upcoming paths of new companies leading in AI and QC innovations within the energy industry. Their joint responsibility is highlighted in advancing a sustainable and intelligent energy future, as well as tackling crucial environmental issues and lessening the impact of climate change.

Advanced Queueing and Location-Allocation Strategies for Sustainable Food Supply Chain

Background: This study presents an integrated multi-product, multi-period queuing location-allocation model for a sustainable, three-level food supply chain involving farmlands, facilities, and markets. The model employs M/M/C/K queuing systems to optimize the transportation of goods, enhancing efficiency and sustainability. A mixed-integer nonlinear programming (MINLP) approach is used to identify optimal facility locations while maximizing profitability, minimizing driver waiting times, and reducing environmental impact. Methods: The grasshopper optimization algorithm (GOA), a meta-heuristic algorithm inspired by the behavior of grasshopper swarms, is utilized to solve the model on a large scale. Numerical experiments demonstrate the effectiveness of the proposed model, particularly in solving large-scale problems where traditional methods like GAMS fall short. Results: The results indicate that the proposed model, utilizing the grasshopper optimization algorithm (GOA), effectively addresses complex and large-scale food supply chain problems. Compared to GAMS, GOA achieved similar outcomes with minimal differences in key metrics such as profitability (with a gap ranging from 0.097% to 1.11%), environmental impact (0.172% to 1.83%), and waiting time (less than 0.027%). In large-scale scenarios, GOA significantly reduced processing times, ranging from 20.45 to 64.78 s. The optimization of processing facility locations within the supply chain, based on this model, led to improved balance between cost (up to $74.2 million), environmental impact (122,112 hazardous units), and waiting time (down to 11.75 h). Sensitivity analysis further demonstrated that increases in truck arrival rates and product value had a significant impact on improving supply chain performance.

Adaptive Tasmanian Devil Optimization algorithm based efficient task scheduling for big data application in a cloud computing environment

Adaptive Tasmanian Devil Optimization algorithm based efficient task scheduling for big data application in a cloud computing environment

A Hybrid Method for Designing Sustainable River Monitoring Networks Using Fuzzy Logic Site Selection and Genetic Algorithm Optimization

A Hybrid Method for Designing Sustainable River Monitoring Networks Using Fuzzy Logic Site Selection and Genetic Algorithm Optimization

Enhancing Active Disturbance Rejection Control for a Vehicle Active Stabiliser Bar with an Improved Chicken Flock Optimisation Algorithm

An active stabiliser bar significantly enhances the anti-roll capabilities of vehicles. The control strategy is a crucial factor in enabling the active stabiliser bar to function effectively. This paper investigates an active disturbance rejection control (ADRC) strategy. Given the numerous parameters of the ADRC and their significant mutual influence, optimising these parameters is challenging. To address this, an improved chicken flock optimisation algorithm is proposed to optimise the ADRC parameters and enhance its performance. First, a three-degree-of-freedom dynamic model of the vehicle is established, and an active disturbance rejection control-based optimisation model utilising a chicken flock optimisation algorithm is constructed. To tackle the issues of getting stuck in local optima and low precision when dealing with complex problems in the traditional chicken flock optimisation (CFO) algorithm, several strategies, including improved Lévy flight, have been adopted. Subsequently, the twelve parameters of the ADRC are optimised using the improved chicken flock optimisation algorithm. Comprehensive testing on multiple benchmark functions demonstrates that the improved chicken flock optimisation (ICFO) algorithm is distinctly superior to other advanced algorithms in terms of solution quality and robustness. Simulation results show that the ICFO-ADRC controller is significantly superior. In four different complex road condition tests, the ICFO-ADRC controller shows an average performance improvement of 8% compared to the fuzzy PI-PD controller, an average improvement of 82% compared to the non-optimised ADRC controller, and an average improvement of 18% compared to the CFO-ADRC controller. Our findings confirm that this paper was able to provide new solutions for vehicle stability control whilst opening up new possibilities for the application of metaheuristic algorithms.

Design of hybrid optimized PI controller for power conditioning circuit of piezoelectric energy harvester

Abstract Piezoelectric Energy Harvesters (PEH) are vital in the present day to encourage advancing technology through producing sustainable energy and dealing with economic and ecological issues associated with batteries. The required stable energy production from the PEH is challenging due to its erratic behaviour with input. As such, a power conditioning circuit with an optimized controller is proposed in this paper. The Proportional Integral (PI) controller is utilized to control the boost converter of the PEH system. The conventional approach of selecting the controller parameters, such as Proportional gain (KP) and Integral gain (Ki), fails to produce optimal outcomes and is time-consuming. Metaheuristic optimization techniques such as Aquila Optimization (AO), African Vulture’s Optimization Algorithm (AVOA) and Hybrid Aquila Optimizer-based African Vulture’s Optimization Algorithm (HAOAVOA) are used to optimize the controller parameter. The different stages of PEH system (Rectifier, boost converter without controller, boost converter with un optimized controller and boost converter with optimized controller) are simulated and analysed using MATLAB-SIMULINK® software. An experimental study is executed to validate the performance of the PEH system with optimized controller. The experimentally obtained response is compared with simulation results. In which the HAOAVOA optimized PI controller gives the better response. Furthermore, the power conditioning circuit with a hybrid optimized controller produces an output of 12 V for the input of 6 V.

Optimal 4E design and innovative R-curve approach for a gas-solar- biological waste polygeneration system for power, freshwater, and methanol production

BackgroundCogeneration power plants traditionally rely on fossil fuels to produce stable power and heat. However, increasing energy demand and population growth have intensified the emission of biological pollutants due to fossil fuel use. The Global Alliance on Health and Pollution advocates for integrating renewable energy sources to mitigate these issues. ObjectivesThis study aims to evaluate the integration of a solar-biomass polygeneration system with a hybrid solar-waste-fossil fuel cogeneration system. The goal is to analyze the system from technical, economic, and environmental perspectives, focusing on optimizing energy demand and minimizing environmental impact. MethodsTo assess energy demand and supply, the R-curve methodology was applied to the hybrid cogeneration system, with a specific focus on solar and biomass renewable energies. Various scenarios were analyzed, including total annual costs, pollutant emissions, water footprint, and overall environmental impact based on life cycle assessment. The study examined and compared the performance of three types of biomass waste (Municipal solid waste, mixed paper waste, and date palm waste). Multi-objective optimization was performed using artificial intelligence and machine learning techniques, employing four meta-heuristic algorithms. The conditions generated by each algorithm were analyzed and compared. ResultsMunicipal solid waste, being the most readily available fuel, provided the most favorable economic conditions for the system. Environmentally, municipal solid waste ranked in the middle compared to other fuels. Among the optimization algorithms, the Salps swarm algorithm proved to be the most efficient in terms of calculation time and system efficiency improvements. The optimization improved net power generation by 5.25 %, overall energy efficiency by 16.27 %, total cost rate by 10.19 %, and total environmental impact rate by 14.02 %. ConclusionThe integrated system's performance was analyzed across different climatic change throughout the year. The multi-objective Salps swarm algorithm optimization demonstrated significant benefits in enhancing system efficiency and reducing costs and environmental impacts.

Hybrid XGB model for predicting unconfined compressive strength of solid waste-cement-stabilized cohesive soil

The utilization of cement has been found to have negative environmental impacts. In order to reduce the quantity of cement used and improve the mechanical properties of solid waste-cement-stabilized cohesive soil, the incorporation of solid waste as additives has been investigated. Unconfined compressive strength is a crucial parameter in geotechnical engineering. However, existing empirical formulas have limited accuracy and applicability when it comes to the unconfined compressive strength of solid waste-cement-stabilized cohesive soil. The machine learning model can be used to provide accurate and comprehensive predictions by considering the nonlinear relationships between independent and dependent variables. This study aims to propose a machine learning model tuned by optimization algorithms with high generalization performance in accurately predicting the unconfined compressive strength. Firstly, a database containing 474 specimens was developed. Secondly, eight machine learning models were established, composed five single models and three hybrid models, to train and test the database. Six performance indicators were employed to evaluate the generalization ability of these models. Finally, the optimal model was selected for analysis of the importance of the feature variables using shapley additive explanations, which were compared with those of the existing empirical model. The research findings indicated that, the extreme gradient boosting model tuned with tree-structured parzen estimators exhibited the highest predictive accuracy and generalization ability. The curing age, cement content, plastic limit, and water content were identified as the most critical factors influencing the unconfined compressive strength. Among the chemical components in solid waste, the aluminum oxide content and silicon dioxide content were found to significantly influence the unconfined compressive strength, while the impact of calcium oxide content was relatively minor. Furthermore, the optimal solid waste content was found to be around 10 %. This study made a significant contribution to the effective utilization of waste resources in the context of sustainable construction practices.