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Deep Resources Engineering 2024年第2期1卷 101-108页

作者： Junjun Chen Zhenxue Dai National and Local Joint Engineering Laboratory of Internet Application Technology on Mine China University of Mining and TechnologyXuzhou 221116China College of Construction Engineering Jilin UniversityChangchun 130026China

Groundwater inverse modeling is a vital technique for estimating unmeasurable model parameters and enhancing numerical simulation *** paper comprehensively reviews the current advances and future prospects of metaheuristic algorithm-based groundwater model parameter ***,the simulation-optimization parameter estimation framework is introduced,which involves the integration of simulation models with metaheuristic *** subsequent sections explore the fundamental principles of four widely employed metaheuristic algorithms-genetic algorithm(GA),particle swarm optimization(PSO),simulated annealing(SA),and differential evolution(DE)-highlighting their recent applications in water resources research and related ***,a solute transport model is designed to illustrate how to apply and evaluate these four optimization algorithms in addressing challenges related to model parameter ***,three noteworthy directions are presented to address the common challenges among current studies,including balancing the diverse exploration and centralized exploitation within metaheuristic algorithms,local approxi-mate error of the surrogate model,and the curse of dimensionality in spatial variational heterogeneous *** summary,this review paper provides theoretical insights and practical guidance for further advancements in groundwater inverse modeling studies.

关键词： Groundwater Inverse modeling metaheuristic algorithms Genetic algorithm Particle swarm optimization Simulated annealing Differential evolution

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Comparison of metaheuristic optimization algorithms with a new modified deb feasibility constraint handling technique

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2021年第7期29卷 3270-3289页

作者： Cimen, Murat Erhan Garip, Zeynep Boz, Ali Fuat Sakarya Univ Appl Sci Fac Technol Dept Elect & Elect Engn Sakarya Turkey Sakarya Univ Appl Sci Fac Technol Dept Comp Engn Sakarya Turkey

In this study, the modification of the Deb feasibility method is considered to solve the constrained optimization problems. In the developed modified Deb feasibility constraint method, the third rule in its procedure was revised in order to increase the performance of the Deb feasibility constraint handling method. The innovation in the method is based on generating a new individual by using both possible solutions that violate the constraints in the method used for solving the problem. In detail, discussions were given about the application and usefulness of six constrained handling techniques. Furthermore, genetic algorithm, particle swarm optimization, Harris hawks optimization, whale optimization algorithm, grey wolf optimization and sine cosine algorithms were applied to both various benchmark functions and also different engineering application problems such as pressure vessel design, welded beam design, speed reducer design and active filter design. Overall the experimental results show that modified Deb feasibility constraint handling technique is more robust and efficient than Deb feasibility technique and most of the other constraint handling techniques.

关键词： Constraint handling methods metaheuristic algorithms Deb feasibility

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Application of metaheuristic optimization algorithms in predicting the compressive strength of 3D-printed fiber-reinforced concrete

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DEVELOPMENTS IN THE BUILT ENVIRONMENT 2024年 17卷

作者： Alyami, Mana Khan, Majid Javed, Muhammad Faisal Ali, Mujahid Alabduljabbar, Hisham Najeh, Taoufik Gamil, Yaser Najran Univ Coll Engn Dept Civil Engn Najran Saudi Arabia COMSATS Univ Islamabad Civil Engn Dept Abbottabad Campus Islamabad 22060 Pakistan Silesian Tech Univ Fac Transport & Aviat Engn Dept Transport Syst Traff Engn & Logist Krasinskiego 8 St PL-40019 Katowice Poland Prince Sattam Bin Abdulaziz Univ Coll Engn Al Kharj Dept Civil Engn Al Kharj 11942 Saudi Arabia Lulea Univ Technol Dept Civil Environm & Nat Resources Engn Operat Maintenance & Acoust Lulea Sweden Monash Univ Malaysia Sch Engn Dept Civil Engn Jalan Lagoon Selatan Bandar Sunway 47500 Selangor Malaysia

In recent years, the construction industry has been striving to make production faster and handle more complex architectural designs. Waste reduction, geometric freedom, lower construction costs, and speedy construction make the 3D-printed fiber-reinforced concrete (3DPFRC) alternative for future construction. However, achieving the optimum mixture composition for 3DPFRC remains a daunting task, entailing the consideration of multiple variables and necessitating an extensive trial-and-error experimental process. Therefore, this study investigated the application of different metaheuristic optimization algorithms to predict the compressive strength (CS) of 3DPFRC. A database of 299 data samples with 16 different input features was compiled from the experimental studies in the literature. Six metaheuristic algorithms, such as human felicity algorithm (HFA), differential evolution algorithm (DEA), nuclear reaction optimization (NRO), Harris hawks optimization (HHO), lightning search algorithm (LSA), and tunicate swarm algorithm (TSA) were applied to identify the optimal hyper parameter combination for the random forest (RF) model in predicting the CS of 3DPFRC. Different statistical metrics and 10-fold cross-validation were used to evaluate the accuracy of the models. The TSA-RF model exhibited superior performance compared to other models, achieving correlation (R), mean absolute error (MAE), and root mean square error (RMSE) values of 0.99, 2.10 MPa, and 3.59 MPa, respectively. The LSA-RF model also performed well, with R, MAE, and RMSE values of 0.99, 2.93 MPa, and 6.23 MPa, respectively. SHapley Additive exPlanation (SHAP) interpretability elucidates the intricate relationships between features and their effects on the CS, thereby offering invaluable insights for the performance-based mix proportion design of 3DPFRC.

关键词： Additive manufacturing 3D-printed concrete Compressive strength Fiber-reinforced concrete metaheuristic algorithms Random forest

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Comparison of metaheuristic optimization algorithms for numerical solutions of optimal control problems

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2023年第10期35卷

作者： Cimen, Murat Erhan Garip, Zeynep Boz, Ali Fuat Sakarya Univ Appl Sci Fac Technol Elect Elect Engn Sakarya Turkiye Sakarya Univ Appl Sci Fac Technol Comp Engn Sakarya Turkiye

The objective in optimal control problems is to determine the control rule to be applied according to the objective function determined for a given system. Theoretically, different solution methods have been developed starting from calculus of variation such as Pontriagin, Hamilton, or Riccati. In this study, apart from analytical methods, it is proposed to apply algorithms inspired by nature to the same problem. Among these algorithms, genetic algorithm, firefly algorithm, Harris Hawk's optimization algorithm, and differential evolution optimization algorithm have been applied to given optimal control problems and their successes were compared in terms of statistical analysis such as minimum, maximum, average, Wilcoxon, and Friedman analysis. The originality of this study is to control the system with discrete control rule by partitioning the control signal, to be applied to the system which is wanted to be optimally controlled. This will allow the control of different techniques, such as MPC or NMPC, which will contribute to the determination of the global optimum control rule, especially in the solution of nonlinear systems. In addition, three different applications have been performed to demonstrate. As a result of these experiments, it has been shown that these algorithms can be applied successfully to such problems.

关键词： benchmark testing metaheuristic algorithms optimal control

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metaheuristic learning algorithms for accurate prediction of hydraulic performance of porous embankment weirs

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APPLIED SOFT COMPUTING 2024年 151卷

作者： Rahmanshahi, Mostafa Jafari-Asl, Jafar Fathi-Moghadam, Manoochehr Ohadi, Sima Mirjalili, Seyedali Hong Kong Polytech Univ Dept Civil & Environm Engn Hong Kong Peoples R China Univ Porto Fac Engn INEGI P-4200465 Porto Portugal Univ Porto Fac Engn CONSTRUCT P-4200465 Porto Portugal Shahid Chamran Univ Ahvaz Fac Water & Environm Engn Ahvaz Iran Univ Sistan & Baluchestan Fac Engn Dept Civil Engn Zahedan Iran Torrens Univ Ctr Artificial Intelligence Res & Optimisat Brisbane Australia Obuda Univ Univ Res & Innovat Ctr H-1034 Budapest Hungary

A porous weir is an environmentally friendly structure with minimal negative environmental impact. Due to the complex flow mechanism around porous weirs, it is difficult to provide a general empirical relationship to estimate the free and submerged discharge coefficients and threshold submergence or modular limit. Hence, the current study attempts to introduce an efficient alternative approach leveraging on multilayer perceptron (MLP) coupled with generalized normal distribution optimization (GNDO), Runge-Kutta optimization algorithm (RUN), slime mould algorithm (SMA), whale optimization algorithm (WOA), and grey wolf optimizer (GWO). These models' robustness and accuracy are investigated to solve the free discharge coefficient (FDC), modular limit index (MLI), and submerge discharge reduction factor (SDRF) of the porous embankment weirs (PEWs) modeling problem where 6514 datasets are studied experimentally for free, modular limit, and submerged flow conditions. The performance of developed metaheuristic-MLP models is compared with a gradient algorithm-based MLP, namely Levenberg-Marquardt (LM) model. The obtained results from analysis of different performance indicators demonstrate that the GNDO-MLP outperforms other developed MLP models. The results demonstrate that the metaheuristic-based MLP models are more reliable in forecasting FDC, MLI, and SDRF of PEWs than the ML-MLP model. To measure how much the MLP model improved when combined with meta-heuristic algorithms, an aggregated statistical index called objective function was applied. The results of this index showed that the MLP model with GNDO performed better than the MLP models with WOA, SMA, GWO, RUN, and LM by 82%, 66%, 69%, 69%, and 96% for FDC, by 41%, 13%, 9%, 8%, and 48% for MLI, and by 64%, 20%, 8%, 4%, and 69% for SDRF condition, respectively. Furthermore, the results of the uncertainty analysis, using the P-factor and R-factor, demonstrated that GNDO-MLP had lower uncertainty compared to other mod

关键词： Discharge coefficient Porous embankment weir metaheuristic algorithms Multilayer perceptron

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Comparison of metaheuristic optimization algorithms for solving constrained mechanical design optimization problems

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 183卷

作者： Gupta, Shubham Abderazek, Hammoudi Yildiz, Betul Sultan Yildiz, Ali Riza Mirjalili, Seyedali Sait, Sadiq M. Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore Mech Res Ctr GRAD BP N73B Ain El Bey 25021 Constantine Algeria Bursa Uludag Univ Dept Elect & Energy Bursa Turkey Bursa Uludag Univ Dept Automot Engn Bursa Turkey Ton Ens Univ Ctr Artificial Intelligence Res & Optimizat 90 Bowen Terrace Fortitude Valley Qld 4006 Australia Yonsei Univ Yonsei Frontier Lab Seoul South Korea King Fahd Univ Petr & Minerals Dept Comp Engn Dhahran Saudi Arabia

Determining the solution for real mechanical design problems is a challenging task when using the newly developed and efficient swarm intelligence algorithms. There are so many difficulties to be addressed, including but not limited to mixed decision variables, diverse constraints, inherent errors, conflicting objectives, and numerous locally optimal solutions. This work analyzes the behavior of nine metaheuristic algorithms, namely, salp swarm algorithm (SSA), multi-verse optimizer (MVO), moth-flame optimizer (MFO), atom search optimi-zation (ASO), ecogeography-based optimization (EBO), queuing search algorithm (QSA), equilibrium optimizer (EO), evolutionary strategy (ES) and hybrid self-adaptive orthogonal genetic algorithm (HSOGA). The efficiency of these algorithms is evaluated on eight mechanical design problems using the solution quality and convergence analysis, which verifies the wide applicability of these algorithms to real-world application problems.

关键词： Optimization metaheuristic algorithms Mechanical design problems Exploration Exploitation

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Knapsack Cipher-based metaheuristic optimization algorithms for cryptanalysis in blockchain-enabled internet of things systems

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AD HOC NETWORKS 2022年 128卷

作者： Abdel-Basset, Mohamed Mohamed, Reda ELkomy, Osama M. Zagazig Univ Fac Comp & Informat Zagazig 44519 Egypt

The integration of the Internet of Things (IoT) and blockchain demand the use of public-key cryptography systems to secure network communications. In this study, one of those public-key algorithms, known as Merkle-Hellman Knapsack Cryptosystem (MHKC), is employed to cryptographically analyze its utilization for blockchain technology using a metaheuristic algorithm. To do so, eight well-known metaheuristic algorithms are employed to determine the trustworthiness of MHKC against cryptoanalysis attacks using various knapsack lengths, ranging from 8 to 32 bits. The experimental findings showed that pathfinder algorithm (PFA) and slime mold optimizer (SMA) could exploit MHKC under 8-bit ASCII code, and their performance gradually deteriorates with higher bit representations, while the performance of manta ray foraging optimization (MRFO) could be superior for the knapsack lengths higher than 8-bit. Additionally, MRFO would not attack MHKC under 32-bit;thus, some genetic operators have been integrated to manipulate the binary solutions obtained by this algorithm to promote its exploration capability in a variant, namely HMRFO. The experimental findings revealed that HMRFO is a better alternative to the existing ones for attacking the MHKC with knapsack lengths higher than 8 bit to appear their fragility points, while both SMA and PFA are competitive for 8-bit ASCII code and superior to the other algorithms.

关键词： metaheuristic algorithms Knapsack cipher Blockchain IoT Merkle-Hellman cryptosystem Cryptanalysis Internet of Things (IoT)

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Comparative performance analysis of metaheuristic search algorithms in parameter extraction for various solar cell models

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Environmental Challenges 2023年 11卷

作者： Bakır, Hüseyin Department of Electronics and Automation Vocational School Dogus University Istanbul 34775 Turkey

In today's technology, solar energy continues to be one of the leading renewable energy sources in electricity production methods due to its abundance on the Earth's surface and its non-polluting nature. In solar photovoltaic (PV) systems, it is relatively easy to produce electricity from the sun, and their efficiency can be enhanced by accurately estimating the electrical parameters of solar cells. From this point of view, this paper has focused on solar cell design to improve the performance of solar PV systems. To achieve this target, fitness-distance balance-based stochastic fractal search (FDB-SFS), particle swarm optimization (PSO), student psychology-based optimization (SPBO), and adaptive guided differential evolution (AGDE) algorithms are employed. The experimental analysis is performed on single-diode solar cell, double-diode solar cell, and three PV modules. Considering the experimental results, it is observed that the best estimation accuracy is reached by the FDB-SFS algorithm. Accordingly, root mean square error (RMSE) values between measured and estimated data were calculated to be 9.86E-04 for the single-diode solar cell model, 9.84E-04 for the double-diode solar cell model, 2.42E-03 for the Photowatt-PWP201 module, 1.72E-03 for STM6–40/36 module, and 1.67E-02 for STP6–120/36 module. Consequently, the present paper reports that FDB-SFS is an efficient and powerful method for parameter extraction of solar photovoltaic models. © 2023

关键词： Double-diode model metaheuristic algorithms Parameter extraction Single-diode model Solar photovoltaic cells

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Enhanced MPP Tracking in Partial Shading Conditions for Solar PV Systems: A metaheuristic Approach Utilizing Projectile Search Algorithm

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IEEE ACCESS 2025年 13卷 50895-50917页

作者： Hussain, Md Tahmid Shahabuddin, Mohammed Huang, Liang-Yin Sarwar, Adil Asim, Mohammed Ahmad, Shafiq Liu, Hwa-Dong Tariq, Mohd Aligarh Muslim Univ Dept Elect Engn ZHCET Aligarh 202002 India Integral Univ Elect Engn Dept Lucknow 226021 India Asia Eastern Univ Sci & Technol Dept Mech Engn New Taipei City 220 Taiwan King Saud Univ Coll Engn Ind Engn Dept Riyadh 11421 Saudi Arabia Natl Taiwan Normal Univ Undergrad Program Vehicle & Energy Engn Taipei 106 Taiwan Florida Int Univ Dept Elect & Comp Engn Miami FL 33174 USA

For solar Photovoltaic (PV) systems to function effectively amid dynamic conditions, it is imperative to achieve efficient power harvesting. Given the escalating demand for energy, the application of solar PV technology for electricity generation must be optimized to ensure optimal performance and cost-effectiveness. Maintaining an ample supply of power at the lowest cost is crucial in this regard. Partial Shading Conditions (PSCs) significantly reduce power transfer efficiency in solar PV systems, potentially leading to the formation of hotspots within the solar array. While the insertion of bypass diodes can address this issue, it often results in multiple power peaks on the Power vs Voltage (P-V) curve characteristics, complicating the process of maximum power tracking. To overcome this challenge and alleviate the computational load on the microcontroller, the application of metaheuristic techniques for Maximum Power Point Tracking (MPPT) proves beneficial. However, due to the distinctive operational characteristics of metaheuristic algorithms, continuous research in this domain is essential. Addressing the need for effective Maximum Power Point (MPP) capture in diverse partial shading scenarios, this study introduces a novel MPPT technique based on the Projectile Search Algorithm (PSA). The PSA, inspired by the projectile motion of physical objects, is a metaheuristic optimization algorithm tailored for solving optimization problems by simulating projectile motion within a search space to identify optimal solutions. To assess the performance of the proposed PSA-based approach, comparisons are made with Jaya, Cuckoo Search, Particle Swarm Optimization (PSO) and Perturb and Observe (P&O) algorithms. Real-time validation using the Typhoon Hardware in the Loop (HIL)-402 emulator is employed to verify the suggested approach, and MATLAB/Simulink software is utilized for evaluation. A comprehensive analysis of the results, considering tracking time, power tracking effi

关键词： metaheuristics Photovoltaic systems Computational modeling Solar power generation Real-time systems Projectiles Maximum power point trackers Photovoltaic cells Prediction algorithms Integrated circuit modeling Projectile search algorithm (PSA) maximum power point tracking partial shading condition metaheuristic algorithms photovoltaic (PV) optimization

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Nature-based metaheuristic optimisation techniques for band selection in drone-based hyperspectral images for plant-level crop classification

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STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT 2025年第2期39卷 599-611页

作者： Sarma, Anagha S. Nidamanuri, Rama Rao Indian Inst Space Sci & Technol Dept Earth & Space Sci Thiruvananthapuram 695547 Kerala India

Hyperspectral imagery-based remote sensing has emerged as a major contributing factor for precision agriculture. Drone-based hyperspectral imagery provides high resolution data for plant-level analysis of agriculture fields, making it possible to discriminate crops with subtle differences in their spectral signatures. However, the high dimensionality and correlated bands in the hyperspectral imagery make data processing challenging in an operational environment. Feature selection plays a vital role in reducing the dimensionality of hyperspectral data without losing spectral integrity. Though evolutionary computing-based optimal feature selection has been applied for hyperspectral data at a broader level, their applications are not explored for crop classification in drone-based hyperspectral imagery, especially at plant-level. This work has evaluated several nature-inspired metaheuristic optimisation algorithms to classify drone hyperspectral imagery at different flying altitudes for crop classification at plant level. We acquired ultra-high spatial resolution hyperspectral imagery from a drone over research farms of the University of Agricultural Sciences, GKVK, Bengaluru, India. From the perspective of studying drone-based hyperspectral imaging for plant-level mapping and the interference of background introduced by varying flying height, this work is the first of its kind to study the impact of the optimisation techniques for hyperspectral band reduction on drone data acquired at different heights. The models are trained on a dataset captured at the height of 30 m and further tested on images acquired over 25 m and 40 m. The outcomes suggest the requirement of only selected bands from certain wavelengths for achieving classification accuracy as compared with the original set of bands. The results also emphasise the effective application of evolutionary computing-based optimisation techniques for hyperspectral band reduction, irrespective of the height of the data

关键词： Optimisation Band selection metaheuristic algorithms Evolutionary computing Hyperspectral imaging

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