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Swarm Intelligent metaheuristic optimization algorithms-Based Artificial Neural Network Models for Breast Cancer Diagnosis: Emerging Trends, Challenges and Future Research Directions

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ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING 2025年第1期32卷 381-398页

作者： Veeranjaneyulu, K. Lakshmi, M. Janakiraman, Sengathir SRM Inst Sci & Technol Sch Comp Dept Data Sci & Business Syst Kattankulathur 603203 Tamil Nadu India CVR Coll Engn Dept Informat Technol Mangalpally 501510 Telangana India

Breast Cancer Disease is identified as one of the prime causes of death in women around the globe standing next to lung cancer. Breast cancer represents the development of malignant neoplasm from the breast cells. This breast cancer can be treated when it is identified at an early stage. Several researchers have contributed different machine learning approaches for maximizing the accuracy during the process of predicting breast cancer. optimization of selected features is another important step essential for attaining maximized accuracy during the process of detection during the use of Artificial Neural Network. The utilization of optimization algorithm also helps in fine-tuning the hyperparameters of ANN such that the process of classification can be achieved with better precision and less computational time. In this paper, a Review on Swarm Intelligent metaheuristic optimization algorithms-based Artificial Neural Network-based Breast Cancer Diagnosis Schemes is presented for comparing different approaches depending on their efficacy in achieving the classification process. It presents the potentiality of wrapper and filter methods generally used for classifying cancer cells from normal cells. This review specifically concentrates on highlighting the significance of the swarm intelligent algorithms-based optimized ANN models which are contributed with its limitations. This review also demonstrates the future scope of research which could be concentrated from the identified extract of the literature. This review also highlighted the different kinds of evaluation metrics considered for assessing the potentiality of the existing ANN-based Breast Cancer Diagnosis Schemes with its need in utilization during evaluation.

关键词： Breast cancer Swarm intelligence metaheuristic optimization algorithms Exploration Exploitation Deep learning model Neural network

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Optimal Performance of PI Controller for AC Microgrid Based on metaheuristic optimization algorithms

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IEEE ACCESS 2025年 13卷 45733-45752页

作者： Majeed Kareem, Ruqaya Al-Nussairi, Mohammed Kh. Bayindir, Ramazan Univ Misan Coll Engn Dept Elect Engn Misan 62001 Iraq Gazi Univ Fac Engn Dept Elect & Elect Engn TR-06560 Ankara Turkiye

Microgrids are small power systems with one or most distributed generating units (DGs). Frequency and voltage control are crucial for grid-independent operating. It is a complex problem and essential to provide reliability and stability. Thus, the operating control issue is one main problem for the microgrid that must be addressed during operation, which calls for developing an optimal control method. Among these techniques, droop control is a common method due to the lack of critical communication links between parallel inverters to regulate the distributed generating units inside a microgrid, which is used for optimizing power sharing among DGs sources. Unfortunately, this strategy isn't accurate in keeping the system frequency and voltage near their nominal values. As a result, the parameter values must be carefully selected utilizing the optimization techniques. This work aims to improve the droop controller based on optimized PI controller parameters to control the frequency and voltage of microgrids under various conditions by using three metaheuristic optimization algorithms, Slime Mould Algorithm (SMA), Sine Cosine Algorithm (SCA), and Sparrow Search Algorithm (SSA), and compare with the conventional method. Simulation findings using Simulink in MATLAB demonstrate the performance of the suggested microgrid stability techniques. Finally, to investigate the effectiveness of the suggested control strategies, the study's results are compared with those of the conventional control method. The simulation results demonstrated the significant superiority of the proposed methods, including the SMA, in terms of performance analysis standards, power deviations, frequency stability, voltage response, load-sharing balance, overshoot reduction, and improvement in settling time and rise time.

关键词： Microgrids Voltage control Frequency control Power system stability Inverters PI control Reliability Renewable energy sources Switches metaheuristics Microgrid droop control metaheuristic optimization algorithms distributed generating (DGs)

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A Comparative Study of metaheuristic optimization algorithms for Solving Real-World Engineering Design Problems

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Computer Modeling in Engineering & Sciences 2024年第4期139卷 1039-1094页

作者： Elif Varol Altay Osman Altay Yusuf Ovik Department of Software Engineering Manisa Celal Bayar UniversityManisa45400Turkey

Real-world engineering design problems with complex objective functions under some constraints are relatively difficult problems to *** design problems are widely experienced in many engineering fields,such as industry,automotive,construction,machinery,and interdisciplinary ***,there are established optimization techniques that have shown effectiveness in addressing these types of *** research paper gives a comparative study of the implementation of seventeen new metaheuristic methods in order to optimize twelve distinct engineering design *** algorithms used in the study are listed as:transient search optimization(TSO),equilibrium optimizer(EO),grey wolf optimizer(GWO),moth-flame optimization(MFO),whale optimization algorithm(WOA),slimemould algorithm(SMA),harris hawks optimization(HHO),chimp optimization algorithm(COA),coot optimization algorithm(COOT),multi-verse optimization(MVO),arithmetic optimization algorithm(AOA),aquila optimizer(AO),sine cosine algorithm(SCA),smell agent optimization(SAO),and seagull optimization algorithm(SOA),pelican optimization algorithm(POA),and coati optimization algorithm(CA).As far as we know,there is no comparative analysis of recent and popular methods against the concrete conditions of real-world engineering ***,a remarkable research guideline is presented in the study for researchersworking in the fields of engineering and artificial intelligence,especiallywhen applying the optimization methods that have emerged *** research can rely on this work for a literature search on comparisons of metaheuristic optimization methods in real-world problems under similar conditions.

关键词： metaheuristic optimization algorithms real-world engineering design problems multidisciplinary design optimization problems

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Comparison of numerical methods and metaheuristic optimization algorithms for estimating parameters for wind energy potential assessment in low wind regions

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RENEWABLE & SUSTAINABLE ENERGY REVIEWS 2017年第Mar.期69卷 1199-1217页

作者： Jiang, Haiyan Wang, Jianzhou Wu, Jie Geng, Wei Nankai Univ Inst Stat Tianjin 300071 Peoples R China Dongbei Univ Finance & Econ Sch Stat Dalian 116025 Peoples R China Northwest Univ Nationalities Math & Comp Inst Lanzhou 730030 Peoples R China

Recently, with energy crises and environmental problems becoming increasingly obvious, the utilization of wind power has become a big concern. Meanwhile, the inconsistent relationship between China's economy and wind energy potential distribution has caused inevitable difficulties in transportation of wind power and even in grid integration. Therefore, the establishment of electrical power system integrated with local-used low-speed wind power has got considerable attention. Weibull, Rayleigh, Gamma and Lognormal probability distributions are evaluated. Then three numerical methods (NMs) - method of moment (MM), maximum likelihood estimation (MLE), and least squares method (LSM), are applied to get parameter estimation in the these distributions. Additionally, another three comparison metaheuristic optimization algorithms (MOAs), including bat algorithm (BA), cuckoo search algorithm (CS) and particle swarm optimization (PSO) are employed as comparison methods to tune the optimal parameters. Experimental results conclude that in this case MOAs perform better than NMs. Moreover, BA-Weibull, CS-Weibull, and PSO-Weibull with only a slight difference outperform all of the other distributions. Specifically, BA-Weibull and PSO-Weibull are only slightly superior to CS-Weibull. The average wind power density, the effective wind power density, the available factor and the capacity factor of wind turbine are considered as key determinant factors in assessing the low-speed wind energy potential, which are directly influenced by the parameters in Weibull model. Moreover, the wind potential assessment in the low-speed wind areas can provide an essential technique support for further investment and development, even for further wind farm construction and economy evaluation. Consequently, accurate parameter estimation is of great importance in low speed wind energy resource assessment.

关键词： metaheuristic optimization algorithms BA CS PSO Low-speed wind assessment

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Optimizing chemotherapy treatment outcomes using metaheuristic optimization algorithms: A case study

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BIO-MEDICAL MATERIALS AND ENGINEERING 2024年第2期35卷 191-204页

作者： Samy, Prakas Gopal Kanesan, Jeevan Badruddin, Irfan Anjum Kamangar, Sarfaraz Ahammad, N. Ameer Univ Malaya Dept Elect Engn Fac Engn Kuala Lumpur Malaysia SEGi Univ & Coll Dept Elect & Elect Engn Fac Engn Built Environm & Informat Technol Petaling Jaya Selangor Malaysia King Khalid Univ Mech Engn Dept Coll Engn Abha Saudi Arabia Univ Tabuk Dept Math Fac Sci Tabuk Saudi Arabia

BACKGROUND: This study explores the dynamics of a mathematical model, utilizing ordinary differential equations (ODE), to depict the interplay between cancer cells and effector cells under chemotherapy. The stability of the equilibrium points in the model is analysed using the Jacobian matrix and eigenvalues. Additionally, bifurcation analysis is conducted to determine the optimal values for the control parameters. OBJECTIVE: To evaluate the performance of the model and control strategies, benchmarking simulations are performed using the PlatEMO platform. METHODS: The Pure Multi-objective Optimal Control Problem (PMOCP) and the Hybrid Multi-objective Optimal Control Problem (HMOCP) are two different forms of optimal control problems that are solved using revolutionary metaheuristic optimisation algorithms. The utilization of the Hypervolume (HV) performance indicator allows for the comparison of various metaheuristic optimization algorithms in their efficacy for solving the PMOCP and HMOCP. RESULTS: Results indicate that the MOPSO algorithm excels in solving the HMOCP, with M-MOPSO outperforming for PMOCP in HV analysis. CONCLUSION: Despite not directly addressing immediate clinical concerns, these findings indicates that the stability shifts at critical thresholds may impact treatment efficacy.

关键词： Multi-objective optimal control problem metaheuristic optimization algorithms bifurcation analysis stability analysis

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Efficient influence of cross section shape on the mechanical and economic properties of concrete canvas and CFRP reinforced columns management using metaheuristic optimization algorithms

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COMPUTERS AND CONCRETE 2022年第6期29卷 375-391页

作者： Ge, Genwang Liu, Yingzi AI-Tamimi, Haneen M. Pourrostam, Towhid Zhang, Xian Ali, H. Elhosiny Jan, Amin Salameh, Anas A. Maanshan Univ Dept Civil Engn Maanshan 243100 Peoples R China Anhui Univ Technol Dept Civil Engn Maanshan 243032 Peoples R China Al Mustaqbal Univ Coll Air Conditioning & Refrigerat Tech Engn Dept Babylon 51001 Iraq Islamic Azad Univ Dept Civil Engn Cent Tehran Branch Tehran Iran Anhui Power Corp Econ & Tech Res Inst Hefei 230022 Peoples R China King Khalid Univ Dept Phys Fac Sci Adv Funct Mat & Optoelect Lab AFMOL POB 9004 Abha Saudi Arabia King Khalid Univ Res Ctr Adv Mat Sci RCAMS POB 9004 Abha 61413 Saudi Arabia Zagazig Univ Fac Sci Phys Dept Zagazig 44519 Egypt Univ Malaysia Kelantan Fac Hospitality Tourism & Wellness City Campus Kota Baharu 16100 Kelantan Malaysia Prince Sattam Bin Abdulaziz Univ Coll Business Adm Dept Management Informat Syst Al Kharj 11942 65 Saudi Arabia

This paper examined the impact of the cross-sectional structure on the structural results under different loading conditions of reinforced concrete (RC) members??? management limited in Carbon Fiber Reinforced Polymers (CFRP). The mechanical properties of CFRC was investigated, then, totally 32 samples were examined. Test parameters included the crosssectional shape as square, rectangular and circular with two various aspect rates and loading statues. The loading involved concentrated loading, eccentric loading with a ratio of 0.46 to 0.6 and pure bending. The results of the test revealed that the CFRP increased ductility and load during concentrated processing. A cross sectional shape from 23 to 44 percent was increased in load capacity and from 250 to 350 percent increase in axial deformation in rectangular and circular sections respectively, affecting greatly the accomplishment of load capacity and ductility of the concentrated members. Two Artificial Intelligence Models as Extreme Learning Machine (ELM) and Particle Swarm optimization (PSO) were used to estimating the tensile and flexural strength of specimen. On the basis of the performance from RMSE and RSQR, C-Shape CFRC was greater tensile and flexural strength than any other FRP composite design. Because of the mechanical anchorage into the matrix, C-shaped CFRCC was noted to have greater fiber-matrix interfacial adhesive strength. However, with the increase of the aspect ratio and fiber volume fraction, the compressive strength of CFRCC was reduced. This possibly was due to the fact that during the blending of each fiber, the volume of air input was increased. In addition, by adding silica fumed to composites, the tensile and flexural strength of CFRCC is greatly improved.

关键词： concrete canvas and CFRP cross section shape efficiency management metaheuristic optimization algorithms reinforced columns

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Performance analysis of metaheuristic optimization algorithms in estimating the parameters of several wind speed distributions

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APPLIED ENERGY 2020年 268卷 114952-114952页

作者： Guedes, Kevin S. de Andrade, Carla F. Rocha, Paulo A. C. Mangueira, Rivanilso dos S. de Moura, Elineudo P. Univ Fed Ceara Technol Ctr Mech Engn Dept BR-60020181 Fortaleza Ceara Brazil Univ Fed Ceara Technol Ctr Dept Met & Mat Engn BR-60020181 Fortaleza Ceara Brazil

For a better use of wind energy, the accurate selection of the wind speed distributions that best represents the regarding wind regime's characteristics is essential. The Weibull distribution is the most common, but this model is not always the most suitable. Therefore, in order to obtain more reliable information, the evaluation of different distributions becomes necessary. Another crucial step is the estimation of the parameters that govern these distributions because the accuracy of these estimates directly affects the energy generation calculations. In the last few years, different optimization methods have been used for this purpose. However, the applications of these methods are focused on conventional two-parameter distributions, such as Weibull and Lognormal. Futhermore, different authors report that there is a lack of studies that use optimization methods for this purpose. In this paper, four metaheuristic optimization algorithms (MOA)-namely, Migrating Birds optimization (MBO), Imperialist Competitive Algorithm (ICA), Harmony Search (HS) and Cuckoo Search (CS)-are used to fit 11 distributions in two Brazillian regions. Thus, this work expands the application of the MOA to beyond the conventional distributions and applies, for the first time, the MBO and ICA in estimating the parameters of wind speed distributions, thereby introducing new ways to optimize the use of wind resources. The fits obtained by the MOA were compared with those obtained by the method Maximum Likelihood Estimation (MLE). Gamma Generalized and Extended Generalized Lindley distributions presented the best fits, and the MOA outperformed the MLE because the global score values obtained were smaller.

关键词： Wind energy Wind speed modeling Optimal parameters estimation Multi-criteria statistical analysis metaheuristic optimization algorithms Non-conventional wind speed distributions

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The design approaches of two-dimensional digital filters based on metaheuristic optimization algorithms: a review of the literature

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ARTIFICIAL INTELLIGENCE REVIEW 2015年第2期44卷 265-287页

作者： Kockanat, Serdar Karaboga, Nurhan Cumhuriyet Univ Sivas Vocat Sch Elect Commun Technol TR-58140 Sivas Turkey Erciyes Univ Dept Elect Engn Fac Engn TR-38039 Kayseri Turkey

Recently, the design of the two-dimensional digital filter has become a subject of interest in the field of two-dimensional signal processing. The two-dimensional digital filter has been applied in many important areas such as image processing, television systems and seismic signal processing. In digital filter design, there are several indispensable aims such as stability, reduced computational complexity and computational time. Thus, researchers and practitioners have investigated various advanced methods based on metaheuristic optimization algorithms for the design of the two-dimensional digital filter. metaheuristic optimization algorithms have been applied to solve different complicated problems in various fields and they have also been successfully used in digital filter design. This paper presents a review of the design approaches of two-dimensional digital filters based on metaheuristic optimization algorithms such as the genetic algorithm, differential evolution and particle swarm optimization. By comparing the proposed design approaches based on metaheuristic optimization algorithms, it is observed that the genetic algorithm is the most preferred algorithm and emerging novel algorithms using metaheuristic optimization algorithms have better performance in terms of computational complexity and computational time. It is hoped that this review will be helpful for researchers and practitioners studying the design of two-dimensional digital filters.

关键词： Two-dimensional digital filter metaheuristic optimization algorithms Filter design optimization Swarm intelligence Evolutionary algorithm

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PI control based on parameter space approach supported metaheuristic optimization algorithms and ANFIS in a natural gas combined cycle power plant

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OPTIMAL CONTROL APPLICATIONS & METHODS 2023年第2期44卷 846-865页

作者： Bayhan, Nevra Arikusu, Yilmaz Seryar Tiryaki, Hasan Istanbul Univ Cerrahpasa Dept Elect & Elect Engn Istanbul Turkey

In this article, proportional-integral (PI) control to ensure stable operation of a steam turbine in a natural gas combined cycle power plant is investigated, since active power control is very important due to the constantly changing power flow differences between supply and demand in power systems. For this purpose, an approach combining stability and optimization in PI control of a steam turbine in a natural gas combined cycle power plant is proposed. First, the regions of the PI controller, which will stabilize this power plant system in closed loop, are obtained by parameter space approach method. In the next step of this article, it is aimed to find the best parameter values of the PI controller, which stabilizes the system in the parameter space, with artificial intelligence-based control and metaheuristic optimization. Through parameter space approach, the proposed optimization algorithms limit the search space to a stable region. The controller parameters are examined with Particle Swarm optimization based PI, artificial bee colony based PI, genetic algorithm based PI, gray wolf optimization based PI, equilibrium optimization based PI, atom search optimization based PI, coronavirus herd immunity optimization based PI, and adaptive neuro-fuzzy inference system based PI (ANFIS-PI) algorithms. The optimized PI controller parameters are applied to the system model, and the transient responses performances of the system output signals are compared. Comparison results of all these methods based on parameter space approach that guarantee stability for this power plant system are presented. According to the results, ANFIS- PI controller is better than other methods.

关键词： ANFIS controller atomic search optimization based PI coronavirus herd immunity optimization based PI metaheuristic optimization algorithms natural gas combined cycle power plant power system

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metaheuristic optimization algorithms for real-world electrical and civil engineering application: A review

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RESULTS IN ENGINEERING 2024年 23卷

作者： Rezk, Hegazy Olabi, Abdul Ghani Wilberforce, Tabbi Sayed, Enas Taha Prince Sattam Bin Abdulaziz Univ Coll Engn Wadi Alddawasir Dept Elect Engn Al Kharj Saudi Arabia Univ Sharjah Sustainable Energy & Power Syst Res Ctr RISE POB 27272 Sharjah U Arab Emirates Kings Coll London Fac Nat Math & Engn Sci London WC2R 2LS England Minia Univ Fac Engn Chem Engn Dept Elminia Egypt

metaheuristic optimization algorithms (MOAs) are gaining increasing interest because of their exceptional effectiveness in addressing many optimization issues. Nevertheless, these algorithms often face difficulties when used to solve real-world problems, necessitating further evaluation of these algorithms that are used to solve realworld problems. In addition, there is currently no established standard for evaluating algorithmic performance. MOAs successfully address engineering challenges due to their inherent complexity and multidimensional character. Several studies have explored the use of these algorithms to tackle electrical and civil engineering problems, highlighting their practical importance. This study thoroughly examined the most recent papers on electrical and civil engineering topics, specifically focusing on the extraction of parameters and optimization of models.

关键词： metaheuristic optimization algorithms Civil engineering Electrical engineering

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