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A penalty-based algorithm proposal for engineering optimization problems

NEURAL COMPUTING & APPLICATIONS 2023年第10期35卷 7635-7658页

作者： Oztas, Gulin Zeynep Erdem, Sabri Pamukkale Univ Dept Business Adm TR-20160 Denizli Turkey Dokuz Eylul Univ Dept Business TR-35390 Izmir Turkey

This paper presents a population-based evolutionary computation model for solving continuous constrained nonlinear optimization problems. The primary goal is achieving better solutions in a specific problem type, regardless of metaphors and similarities. The proposed algorithm assumes that candidate solutions interact with each other to have better fitness values. The interaction between candidate solutions is limited with the closest neighbors by considering the Euclidean distance. Furthermore, Tabu Search Algorithm and Elitism selection approach inspire the memory usage of the proposed algorithm. Besides, this algorithm is structured on the principle of the multiplicative penalty approach that considers satisfaction rates, the total deviations of constraints, and the objective function value to handle continuous constrained problems very well. The performance of the algorithm is evaluated with real-world engineering design optimization benchmark problems that belong to the most used cases by evolutionary optimization researchers. Experimental results show that the proposed algorithm produces satisfactory results compared to the other algorithms published in the literature. The primary purpose of this study is to provide an algorithm that reaches the best-known solution values rather than duplicating existing algorithms through a new metaphor. We constructed the proposed algorithm with the best combination of features to achieve better solutions. Different from similar algorithms, constrained engineering problems are handled in this study. Thus, it aims to prove that the proposed algorithm gives better results than similar algorithms and other algorithms developed in the literature.

关键词： Natural facts Evolutionary computation Constrained nonlinear optimization Engineering benchmark problems Metaheuristics nature-inspired optimization algorithms

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The Genetic Algorithm-Artificial Neural Networks Integration in the optimization: An Application for Transportation Systems 1

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International Conference on Intelligent and Fuzzy Systems (INFUS)

作者： Caparoglu, Omer Faruk Ok, Yesim Ozaydin, Nadide Caglayan Erzurum Tech Univ Dept Ind Engn Erzurum Turkiye Ataturk Univ Dept Ind Engn Erzurum Turkiye

ISBN: (数字)9783031700187

ISBN: (纸本)9783031700170;9783031700187

The transportation sector stands as one of the broadest and most significant industries worldwide. Today, one of the key environmental consequences of the transportation sector is the increase in carbon dioxide (CO2) emissions. This research focuses on CO2 emissions specifically from road transportation. The primary objectives of the study are to investigate how yearly fluctuations on highways impact CO2 emissions. The study utilizes genetic algorithm-based artificial neural networks (GA-ANN) to design a transportation optimization model. Artificial neural networks are employed to estimate the proportion of CO2 emissions from roadways in the total transportation-related CO2 emissions. The article proposes an optimization method using genetic algorithms (GA) to optimize the parameters of the artificial neural networks. The development of the ANN model was carried out in the MATLAB program. The constructed GA-ANN model was compared to a variety of other machine learning (ML) models linear regression (LR), support vector machine (SVM), random forest (RF), artificial neural network (ANN).

关键词： Artificial Neural Network Genetic Algorithm Transportation nature-inspired optimization algorithms optimization Machine Learning Greenhouse Gas Emission

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Integrating nature-inspired refined red-tailed hawk algorithm with enhanced time-series dense encoder for seismic event prediction

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EARTH SCIENCE INFORMATICS 2025年第4期18卷 1-21页

作者： Kumari, Priyanka Kumar, Sunil Giri, Ram Kumar Sheshma, Sanju Kumari Singh, Bhupesh Kumar Amity Univ Rajasthan Amity Sch Engn & Technol Jaipur India MoES India Meteorol Dept Lodi Rd New Delhi 110003 India Rajasthan Tech Univ Kota India

Earthquake and tsunami forecasting are critical components of disaster preparedness and mitigation efforts. This paper presents a novel HawkTide ProForecast model that integrates the Refined Red-Tailed Hawk (RR-TH) algorithm for feature optimization and the Enhanced Time-series Dense Encoder (ETiDE) model for forecasting seismic events and tsunamis. The RR-TH algorithm mimics the hunting behavior of hawks to efficiently select the most relevant features from seismic data, enhancing the model's capacity to seize essential patterns. The ETiDE model, known for its accuracy in time-series forecasting, utilizes dense encoding techniques to capture intricate temporal patterns and dependencies in sequential data streams. To evaluate the proposed model, we use standard metrics including precision, accuracy, recall, and F1 score. These metrics provide the model's performance in predicting earthquake alert levels and tsunami possibilities. The model is trained and tested on historical seismic data to demonstrate its effectiveness in real-world scenarios. Our experimental results show that the integrated model outperforms traditional methods in terms of prediction accuracy and reliability. The precision, accuracy, recall, and F1 score metrics demonstrate the model's ability to accurately forecast seismic events and tsunamis, highlighting its potential for improving early warning systems and disaster response strategies.

关键词： Earthquake and Tsunami forecasting nature-inspired optimization algorithms

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Archimedes Optimizer: Theory, Analysis, Improvements, and Applications

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ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING 2023年第4期30卷 2543-2578页

作者： Dhal, Krishna Gopal Ray, Swarnajit Rai, Rebika Das, Arunita Midnapore Coll Autonomous Dept Comp Sci & Applicat Midnapore W Bengal India Maulana Abul Kalam Azad Univ Technol Dept Comp Sci & Engn Kolkata W Bengal India Sikkim Univ Dept Comp Applicat Gangtok Sikkim India

The intricacy of the real-world numerical optimization tribulations has full-fledged and diversely amplified necessitating proficient yet ingenious optimization algorithms. In the domain wherein the classical approaches fall short, the predicament resolving nature-inspired optimization algorithms (NIOA) tend to hit upon an excellent solution to unbendable optimization problems consuming sensible computation time. Nevertheless, in the last few years approaches anchored in nonlinear physics have been anticipated, announced, and flourished. The process based on non-linear physics modeled in the form of optimization algorithms and as a subset of NIOA, in countless cases, has successfully surpassed the existing optimization methods with their effectual exploration knack thus formulating utterly fresh search practices. Archimedes optimization Algorithm (AOA) is one of the recent and most promising physics optimization algorithms that use meta-heuristics phenomenon to solve real-world problems by either maximizing or minimizing a variety of measurable variables such as performance, profit, and quality. In this paper, Archimedes optimization Algorithm (AOA) has been discussed in great detail, and also its performance was examined for Multi-Level Thresholding (MLT) based image segmentation domain by considering t-entropy and Tsallis entropy as objective functions. The experimental results showed that among recent Physics inspired optimization algorithms (PIOA), the Archimedes optimization Algorithm (AOA) produces very promising outcomes with Tsallis entropy rather than with t-entropy in both color standard images and medical pathology images.

关键词： nature-inspired optimization algorithms Image segmentation optimization Tsallis t-entropy Archimedes optimization algorithm Physics inspired optimization algorithms

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Archimedes optimizer-based fast and robust fuzzy clustering for noisy image segmentation

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JOURNAL OF SUPERCOMPUTING 2023年第4期79卷 3691-3730页

作者： Dhal, Krishna Gopal Das, Arunita Ray, Swarnajit Rai, Rebika Ghosh, Tarun Kumar Midnapore Coll Autonomous Dept Comp Sci & Applicat Paschimmedinipur W Bengal India Maulana Abul Kalam Azad Univ Technol Dept Comp Sci & Engn Kolkata W Bengal India Sikkim Univ Dept Comp Applicat Sikkim India Haldia Inst Technol Dept Comp Sci & Engn Kolkata W Bengal India

Fuzzy C-means (FCM) is one of the prominent and effective cluster-based image segmentation techniques exceedingly susceptible to noise and initial cluster centers, thereby effortlessly converging toward local optima. However, FCM consumes exceptionally high computation time due to the repetitive computation of the distance amid cluster centers and pixels. To resolve this apprehension, this paper aims to consider a histogram-based fast fuzzy image clustering (HBFFIC) procedure that primarily tends to carry out morphological reconstruction (MR) operation over the image to assure noise immunity and safeguard details of the imagery. Further, as a replacement for pixels of a summed image, clustering is carried out based on gray-level histogram. This with no qualm radically trims down the computational time as the number of gray levels in an image is normally to a great extent lesser than that of the number of its pixels. Though HBFFIC is a proficient local optimizer however, owing to arbitrary initialization that is carried out in FCM, HBFFIC has the utmost possibility to get effortlessly wedge into local optima. Consequently, this is where the role of nature-inspired optimization algorithms (NIOA) comes into picture. For that reason, this paper successfully makes use of NIOA to prevail over the dilemma using Archimedes optimizer (AO) to discover the most favorable cluster centers. The real-world images particularly synthetic, grayscale, and color pathology images are exercised to perform the experimental study. The experimental study clearly highlights that the proposed hybrid algorithm (HBFFIC-AO) for noisy image segmentation outperforms the other state-of-art algorithms in terms of segmentation accuracy (SA), comparison score (CS), MSE, and PSNR. The visual along with numerical outcomes projected in the experimental study point toward the pre-eminence of the proposed algorithm as compared with the prevailing leading-edge image segmentation algorithms.

关键词： Fuzzy C-means Archimedes optimizer Noisy image segmentation nature-inspired optimization algorithms Swarm intelligence Histogram-based fast fuzzy image clustering Fuzzy membership Membership filtering Gray-level histogram Morphological reconstruction Image segmentation algorithms

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Bio-inspired algorithms for feature engineering: analysis, applications and future research directions

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INFORMATION DISCOVERY AND DELIVERY 2025年第1期53卷 56-71页

作者： Rajput, Vaishali Mulay, Preeti Mahajan, Chandrashekhar Madhavrao Symbiosis Int Univ Dept Engn Pune India Vishwakarma Inst Technol Dept Engn Sci & Humanities Pune India

Purpose - nature's evolution has shaped intelligent behaviors in creatures like insects and birds, inspiring the field of Swarm Intelligence. Researchers have developed bio-inspired algorithms to address complex optimization problems efficiently. These algorithms strike a balance between computational efficiency and solution optimality, attracting significant attention across domains. Design/methodology/approach - Bio-inspired optimization techniques for feature engineering and its applications are systematically reviewed with chief objective of assessing statistical influence and significance of "Bio-inspired optimization"-based computational models by referring to vast research literature published between year 2015 and 2022. Findings - The Scopus and Web of Science databases were explored for review with focus on parameters such as country-wise publications, keyword occurrences and citations per year. Springer and IEEE emerge as the most creative publishers, with indicative prominent and superior journals, namely, PLoS ONE, Neural Computing and Applications, Lecture Notes in Computer Science and IEEE Transactions. The "National Natural Science Foundation" of China and the "Ministry of Electronics and Information Technology" of India lead in funding projects in this area. China, India and Germany stand out as leaders in publications related to bio-inspired algorithms for feature engineering research. Originality/value - The review findings integrate various bio-inspired algorithm selection techniques over a diverse spectrum of optimization techniques. Anti colony optimization contributes to decentralized and cooperative search strategies, bee colony optimization (BCO) improves collaborative decision-making, particle swarm optimization leads to exploration-exploitation balance and bio-inspired algorithms offer a range of nature-inspired heuristics.

关键词： Bio-inspired algorithms Evolutionary algorithms Speech emotion recognition Sentiment analysis nature-inspired optimization algorithms Feature engineering Bio-inspired computing Bio-inspired optimization Bio-inspired application Feature selection

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Hybrid of Ensemble Machine Learning and nature-inspired algorithms for Divorce Prediction 2nd

Hybrid of Ensemble Machine Learning and Nature-Inspired Algo...

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2nd Pan-African Conference on Artificial Intelligence (PanAfriCon AI)

作者： Sahle, Kalkidan A. Yibre, Abdulkerim M. Bahir Dar Univ Dept Informat Technol Fac Comp Bahir Dar Inst Technol Bahir Dar Ethiopia Hawassa Univ Hawassa Inst Technol Dept Informat Technol Fac Informat Hawassa Ethiopia

ISBN: (纸本)9783031576386;9783031576393

Divorce is a global issue with profound emotional, psychological, and socio-economic consequences. In 2022, Addis Ababa witnessed 14,000 registered marriages but also recorded 1,623 divorces, while 2018 saw 1,923 divorces. Understanding the factors contributing to divorce is vital for prevention and support. Machine learning and AI play a critical role in predicting divorce, early marital distress detection, and personalized interventions. Their scalability aids in effective prevention strategies and targeted support. This research explores a Hybrid Approach AdaBoost, Gradient Boosting, Bagging, Stacking, XGBoost, and Random Forest with Jaya and Whale optimization. The Hybrid Approach is chosen to synergize the strengths of ensemble learning and nature-inspired optimization algorithms. The goal is to enhance divorce prediction accuracy by leveraging ensemble models' robustness and optimization inspired by natural processes. To assess model performance, train-test splits, and k-fold Cross-Validation techniques are used, with metrics like accuracy, precision, recall, F1 score, and AUC-ROC(Area Under the Receiver Operating Characteristic Curve). AdaBoost stands out, achieving 97%, and 96% accuracy in Jaya andWOA hyperparameter optimizations, respectively. This research aligns with Sustainable Development Goals (SDGs) by promoting gender equality (SDG 5), identifying inequalities and offering targeted support (SDG 10), and fostering stable families and social cohesion (SDG 16). By leveraging AI for divorce prediction, this work contributes to a more sustainable and inclusive world, advancing gender equality, reducing inequalities, and peaceful societies.

关键词： Divorce Ensemble Machine Learning nature-inspired optimization algorithms Sustainable Development Goals XGBoost

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Topics in Artificial Intelligence Applied to Industry 4.0 1st

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2024年

作者： Swetta Kukreja

ISBN: (数字)9781394216130;9781394216123

ISBN: (纸本)9781394216116

Topics in Artificial Intelligence Applied to Industry 4.0 Forward thinking resource discussing emerging AI and IoT technologies and how they are applied to Industry 4.0 Topics in Artificial Intelligence Applied to Industry 4.0 discusses the design principles, technologies, and applications of emerging AI and IoT solutions on Industry 4.0, explaining how to make improvements in infrastructure through emerging technologies. Providing a clear connection with different technologies such as IoT, Big Data, AR and VR, and Blockchain, this book presents security, privacy, trust, and other issues whilst delving into real-world problems and case studies. The text takes a highly practical approach, with a clear insight on how readers can increase productivity by drastically shortening the time period between the development of a new product and its delivery to customers in the market by 50%. This book also discusses how to save energy across systems to ensure competitiveness in a global market, and become more responsive in how they produce products and services for their consumers, such as by investing in flexible production lines. Written by highly qualified authors, Topics in Artificial Intelligence Applied to Industry 4.0 explores sample topics such as:

Quantum machine learning, neural network implementation, and cloud and data analytics for effective analysis of industrial data
Computer vision, emerging networking technologies, industrial data spaces, and an industry vision for 2030 in both developing and developed nations
Novel or improved nature-inspired optimization algorithms in enhancing Industry 5.0 and the connectivity of any components for smart environment
Future professions in agriculture, medicine, education, fitness, R&D, and transport and communication as a result of new technologies

Aimed at researchers and students in the interdisciplinary fields of Smart Manufacturing and Smart Applications, Topics in Artific

关键词： Quantum machine learning neural network implementation cloud analytics data analytics industrial data AI Blockchain Edge computing Green Computing Society 5.0 Computer vision nature-inspired optimization algorithms

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optimization of β-Cyclodextrin-Assisted Extraction of Apigenin and Luteolin from Chrysanthemum indicum L. Using Response Surface Methodology Combined with Different optimization algorithms and Evaluation of Its Antioxidant Capacity

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CHEMISTRY & BIODIVERSITY 2023年第8期20卷 e202300873-e202300873页

作者： Thu, Hang Nguyen Thu, Hang Nguyen Nguyen-Van, Phuong Hanoi Univ Pharm Fac Pharmacognosy & Tradit Med Dept Pharmacognosy Hanoi 11000 Vietnam Hanoi Univ Pharm K72 Hanoi 11000 Vietnam

Cyclodextrins and their derivatives have shown successful applications in extracting active compounds from medicinal plants. However, the use of & beta;-cyclodextrin derivatives for extracting apigenin and luteolin from Chrysanthemum indicum L. remains unexplored. Additionally, the application of nature-inspired optimization algorithms in optimizing extraction conditions has been limited. Therefore, this study was performed with the aims of optimizing the extraction of apigenin and luteolin from C. indicum with the assistance of 2-hydroxypropyl-& beta;-cyclodextrin (HP-& beta;-CD) using response surface methodology combined with various optimization algorithms, including desirability function approach, genetic algorithm, particle swarm optimization, and firefly algorithm. The results showed that the optimal conditions obtained by the four algorithms were consistent, with an extraction time of 60 min, HP-& beta;-CD concentration of 30 mg/mL, and a solvent-to-solid ratio of 24 mg/mL. At these conditions, the apigenin and luteolin contents were 1.362 & PLUSMN;0.008 and 8.724 & PLUSMN;0.117 mg/g, respectively. The results also showed that HP-& beta;-CD-assisted extraction exhibited significantly higher apigenin and luteolin contents compared to conventional solvent. Comparable results were also yielded from the antioxidant assay. Our study suggested that the nature-inspired optimization algorithms might be potential options in enhancing the effectiveness of the traditional response surface methodology for the optimization of extraction of natural products.

关键词： antioxidants cyclodextrins flavonoid nature-inspired optimization algorithms response surface methodology

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LGBM-based modeling scenarios to compressive strength of recycled aggregate concrete with SHAP analysis

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MECHANICS OF ADVANCED MATERIALS AND STRUCTURES 2024年第23期31卷 5999-6014页

作者： Xi, Bin Li, Enming Fissha, Yewuhalashet Zhou, Jian Segarra, Pablo Politecn Milan Dept Civil & Environm Engn Milan Italy Univ Politecn Madrid ETSI Minas & Energia Rios Rosas 21 Madrid 28003 Spain Akita Univ Grad Sch Int Resource Sci Dept Geosci Geotechnol & Mat Engn Resources Akita Japan Aksum Univ Dept Min Engn Aksum Tigray Ethiopia Cent South Univ Sch Resources & Safety Engn Changsha Peoples R China

Concrete production contributes significantly to global greenhouse gas emissions, and its manufacture requires substantial natural resources. These concerns can be partly mitigated by recycling construction demolition waste as aggregates to produce Recycled Aggregate Concrete (RAC). RAC has gained momentum due to its lower environmental impact, production costs, and increased sustainability. The aim of this study was to advance the reasonable use of recycled aggregate in concrete and achieve optimal mixture ratio design. Four advanced machine learning algorithms, Support Vector Machine (SVR), Light Gradient Boosting Machine (LGBM), Random Forest (RF), and Multi-Layer Perceptron (MLP), were employed, and the novel optimization algorithms, biogeography-based optimization (BBO), Multi-Verse Optimizer (MVO) and Gravitational Search Algorithm (GSA), were integrated to to predict the compressive strength of RAC. Six potential influential factors for RAC strength were considered in the models. The study employed four evaluation metrics, Taylor diagrams and Regression Error Characteristic plots to compare model performance. The result shows LGBM-based hybrid model outperformed other methods, demonstrating high accuracy in predicting compressive strength. The Shapley Additive Explanation (SHAP) results emphasize the importance of understanding the interactions between the various factors and their effects on the mechanical properties of the RAC. The findings can inform the development of more sustainable and environmentally friendly building materials.

关键词： Waste utilization Recycled aggregate concrete nature-inspired optimization algorithms Light Gradient Boosting Machine Shapley Additive Explanation

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