Nima Khodadadi

About Nima Khodadadi

Nima Khodadadi, With an exceptional h-index of 29 and a recent h-index of 29 (since 2020), a distinguished researcher at Iran University of Science and Technology, specializes in the field of Structural Optimization, Artificial Intelligence, Evolutionary algorithms, Engineering Optimization.

His recent articles reflect a diverse array of research interests and contributions to the field:

Hippopotamus optimization algorithm: a novel nature-inspired optimization algorithm

Predictive Analytics and Machine Learning in Direct Marketing for Anticipating Bank Term Deposit Subscriptions

Puma optimizer (PO): A novel metaheuristic optimization algorithm and its application in machine learning

Modelling the compressive strength of geopolymer recycled aggregate concrete using ensemble machine learning

Multi-objective archived-based whale optimization algorithm

Electric eel foraging optimization: A new bio-inspired optimizer for engineering applications

Whale optimization algorithm for optimization of truss structures with multiple frequency constraints

Greylag goose optimization: Nature-inspired optimization algorithm

Nima Khodadadi Information

University

Iran University of Science and Technology

Position

Research Assistant

Citations(all)

2005

Citations(since 2020)

2005

Cited By

14

hIndex(all)

29

hIndex(since 2020)

29

i10Index(all)

43

i10Index(since 2020)

43

Email

University Profile Page

Iran University of Science and Technology

Nima Khodadadi Skills & Research Interests

Structural Optimization

Artificial Intelligence

Evolutionary algorithms

Engineering Optimization

Top articles of Nima Khodadadi

Hippopotamus optimization algorithm: a novel nature-inspired optimization algorithm

Authors

Mohammad Hussein Amiri,Nastaran Mehrabi Hashjin,Mohsen Montazeri,Seyedali Mirjalili,Nima Khodadadi

Journal

Scientific Reports

Published Date

2024/2/29

The novelty of this article lies in introducing a novel stochastic technique named the Hippopotamus Optimization (HO) algorithm. The HO is conceived by drawing inspiration from the inherent behaviors observed in hippopotamuses, showcasing an innovative approach in metaheuristic methodology. The HO is conceptually defined using a trinary-phase model that incorporates their position updating in rivers or ponds, defensive strategies against predators, and evasion methods, which are mathematically formulated. It attained the top rank in 115 out of 161 benchmark functions in finding optimal value, encompassing unimodal and high-dimensional multimodal functions, fixed-dimensional multimodal functions, as well as the CEC 2019 test suite and CEC 2014 test suite dimensions of 10, 30, 50, and 100 and Zigzag Pattern benchmark functions, this suggests that the HO demonstrates a noteworthy proficiency in both …

Predictive Analytics and Machine Learning in Direct Marketing for Anticipating Bank Term Deposit Subscriptions

Authors

Ahmed Mohamed Zaki,Nima Khodadadi,Wei Hong Lim,SK Towfek

Journal

American Journal of Business and Operations Research

Published Date

2024

Direct marketing strategies in the banking sector have undergone evolution with the integration of predictive analytics and machine learning techniques. The focus of this study is on the utilization of these technologies to foresee bank term deposit subscriptions. The methodology encompasses data exploration, visualization, and the implementation of machine learning models. Datasets from Kaggle are employed, relationships within the data are explored through crosstabulations and heat maps, and feature engineering and preprocessing techniques are applied. The study individually implements models such as SGD Classifier, k-nearest neighbor Classifier, and Random Forest Classifier. The results indicate that the best performance among the evaluated models was exhibited by the Random Forest Classifier, achieving an accuracy of 87.5%, a negative predictive value (NPV) of 92.9972%, and a positive predictive value (PPV) of 87.8307%. These findings provide valuable insights for banks seeking to optimize their marketing strategies within the dynamic landscape of the financial industry.

Puma optimizer (PO): A novel metaheuristic optimization algorithm and its application in machine learning

Authors

Benyamin Abdollahzadeh,Nima Khodadadi,Saeid Barshandeh,Pavel Trojovský,Farhad Soleimanian Gharehchopogh,El-Sayed M El-kenawy,Laith Abualigah,Seyedali Mirjalili

Journal

Cluster Computing

Published Date

2024/1/19

Optimization techniques, particularly meta-heuristic algorithms, are highly effective in optimizing and enhancing efficiency across diverse models and systems, renowned for their ability to attain optimal or near-optimal solutions within a reasonable timeframe. In this work, the Puma Optimizer (PO) is proposed as a new optimization algorithm inspired from the intelligence and life of Pumas in. In this algorithm, unique and powerful mechanisms have been proposed in each phase of exploration and exploitation, which has increased the algorithm’s performance against all kinds of optimization problems. In addition, a new type of intelligent mechanism, which is a type of hyper-heuristic for phase change, is presented. Using this mechanism, the PO algorithm can perform a phase change operation during the optimization operation and balance both phases. Each phase is automatically adjusted to the nature of the …

Modelling the compressive strength of geopolymer recycled aggregate concrete using ensemble machine learning

Authors

Emad Golafshani,Nima Khodadadi,Tuan Ngo,Antonio Nanni,Ali Behnood

Journal

Advances in Engineering Software

Published Date

2024/5/1

In the quest to reduce the environmental impact of the construction sector, the adoption of sustainable and eco-friendly materials is imperative. Geopolymer recycled aggregate concrete (GRAC) emerges as a promising solution by substituting supplementary cementitious materials, including fly ash and slag cement, for ordinary Portland cement and utilizing recycled aggregates from construction and demolition waste, thus significantly lowering carbon emissions and resource consumption. Despite its potential, the widespread implementation of GRAC has been hindered by the lack of an effective mix design methodology. This study seeks to bridge this gap through a novel machine learning (ML)-based approach to accurately model the compressive strength (CS) of GRAC, a critical parameter for ensuring structural integrity and safety. By compiling a comprehensive database from existing literature and enhancing it …

Multi-objective archived-based whale optimization algorithm

Authors

Nima Khodadadi,Seyedeh Zahra Mirjalili,Seyed Mohammad Mirjalili,Mohammad H Nadim-Shahraki,Seyedali Mirjalili

Published Date

2024/1/1

In this chapter, the Multi-Objective Archived-based Whale Optimization Algorithm (MAWOA) is a multi-objective version of the proposed WOA. It mimics the social behavior of humpback whales, and the algorithm is based on the bubble-net hunting strategy. The WOA algorithm incorporates three mechanisms, namely archive, grid, and leader selection, to facilitate multi-objective optimization. As a result of this research, the MAWOA has been developed to address multi-objective optimization issues that arise in various engineering problems. Eight engineering multi-objective optimization design problems are used to evaluate MAWOA. The algorithm's effectiveness is measured concerning multiple criteria, including coverage, generational distance, spacing, and others. The optimization results show that the MAWOA algorithm can compete favorably with the most advanced meta-heuristic algorithms.

Electric eel foraging optimization: A new bio-inspired optimizer for engineering applications

Authors

Weiguo Zhao,Liying Wang,Zhenxing Zhang,Honggang Fan,Jiajie Zhang,Seyedali Mirjalili,Nima Khodadadi,Qingjiao Cao

Journal

Expert Systems with Applications

Published Date

2024/3/15

An original swarm-based, bio-inspired metaheuristic algorithm, named electric eel foraging optimization (EEFO) is developed and tested in this work. EEFO draws inspiration from the intelligent group foraging behaviors exhibited by electric eels in nature. The algorithm mathematically models four key foraging behaviors: interaction, resting, hunting, and migration, to provide both exploration and exploitation during the optimization process. In addition, an energy factor is developed to manage the transition from global search to local search and the balance between exploration and exploitation in the search space. EEFO reveals various foraging patterns based on the foraging characteristics of electric eels. In this study, such dynamic patterns and behaviors are mathematically imitated to design an effective global optimizer. The effectiveness of EEFO is verified through a comparison with 12 other algorithms using the …

Whale optimization algorithm for optimization of truss structures with multiple frequency constraints

Authors

Nima Khodadadi,El-Sayed M El-kenawy,Marwa M Eid,Ziad Azzi,Abdelaziz A Abdelhamid,Seyedali Mirjalili

Published Date

2024/1/1

Optimizing a truss subject to vibration frequencies is a problem with high nonlinearity and computational cost. As a result, this issue necessitates the application of practical optimization algorithms. In this chapter, the Whale Optimization Algorithm is explored as a method for optimizing truss size within frequency constraints. It is compared to the Arithmetic Optimization Algorithm and the Material Generation Algorithm. The findings of this study indicate that the WOA is a highly effective approach that can produce results that are equal to or better than those of other metaheuristics. In general, the WOA tends to outperform its competitors in this area.

Greylag goose optimization: Nature-inspired optimization algorithm

Authors

El-Sayed M El-kenawy,Nima Khodadadi,Seyedali Mirjalili,Abdelaziz A Abdelhamid,Marwa M Eid,Abdelhameed Ibrahim

Journal

Expert Systems with Applications

Published Date

2024/3/15

Nature-inspired metaheuristic approaches draw their core idea from biological evolution in order to create new and powerful competing algorithms. Such algorithms can be divided into evolution-based and swarm-based algorithms. This paper proposed a new nature-inspired optimizer called the Greylag Goose Optimization (GGO) algorithm. The proposed algorithm (GGO) belongs to the class of swarm-based algorithms and is inspired by the Greylag Goose. Geese are excellent flyers and during their seasonal migrations, they fly in a group and can cover thousands of kilometers in a single flight. While flying, a group of geese forms themselves as a “V” configuration. In this way, the geese in the front can minimize the air resistance of the ones in the back. This allows the geese to fly around 70% farther as a group than they could individually. The GGO algorithm is first validated by being applied to nineteen datasets …

A novel version of whale optimization algorithm for solving optimization problems

Authors

Nima Khodadadi,El-Sayed M El-kenawy,Sepehr Faridmarandi,Mansoureh Shahabi Ghahfarokhi,Abdelhameed Ibrahim,Seyedali Mirjalili

Published Date

2024/1/1

The hunting behavior of humpback whales served as the basis for developing a previously invented swarm-based optimization algorithm known as the whale optimization algorithm (WOA). To achieve more precise solutions with higher reliability and faster convergence, this research endeavors to improve upon the initial WOA formulation. The new technique, called the advanced whale optimization algorithm (AWOA), is put to the test in engineering optimization problems. The AWOA, along with other metaheuristic techniques presented in previous literature, was tested in four optimization problems. Based on the numerical results, it was concluded that the AWOA was more efficient compared to the WOA.

Data-Driven PSO-CatBoost Machine Learning Model to Predict the Compressive Strength of CFRP-Confined Circular Concrete Specimens

Authors

Nima Khodadadi,Hossein Roghani,Francisco De Caso,El-Sayed M El-kenawy,Yelena Yesha,Antonio Nanni

Journal

Thin-Walled Structures

Published Date

2024/3/1

This work articulates the development of a sophisticated machine-learning model for the prediction of compressive strength in Carbon Fiber-Reinforced Polymer Confined-Concrete (CFRP-CC) specimens. Despite extensive empirical studies conducted over the last three decades, prevailing predictive models predominantly rooted in linear or nonlinear regression analyses are constrained by their dependency on limited data scopes. Addressing this deficiency, our research delineates the formulation of an innovative Particle Swarm Optimization- Categorical Boosting (PSO-CatBoost) algorithm, underpinned by an expansive database encompassing 916 experimental outcomes from 116 scholarly articles, spanning the period from 1991 to mid-2023. This innovative approach effectively combines the strengths of Particle Swarm Optimization and the CatBoost algorithm. It carefully evaluates various vital factors that …

Guided whale optimization algorithm (guided WOA) with its application

Authors

Abdelhameed Ibrahim,El-Sayed M El-kenawy,Nima Khodadadi,Marwa M Eid,Abdelaziz A Abdelhamid

Published Date

2024/1/1

Biologically-inspired metaheuristic techniques are novel approaches that derive their central concept from the process of biological evolution. The end goal of these techniques is to develop powerful new algorithms that can compete with existing ones. The evolution-based algorithms and the swarm-based algorithms are two subcategories that may be found inside the bio-based algorithm category. Swarm intelligence powers the modern Whale Optimization Algorithm (WOA). Humpback whale hunting habits influenced it. The WOA optimization technique simulates humpback whale actions by hunting for a target, surrounding the prey, and attacking with a bubble net. The WOA algorithm has solved many optimization and feature selection challenges in recent years. The Guided WOA has undergone a number of modifications from the conventional WOA format. An enhanced method that can quickly direct the whales …

Voting Classifier and Metaheuristic Optimization for Network Intrusion Detection

Authors

Doaa Sami Khafaga,Faten Khalid Karim,Abdelaziz A Abdelhamid,El-Sayed M El-kenawy,Hend K Alkahtani,Nima Khodadadi,Mohammed Hadwan,Abdelhameed Ibrahim

Journal

Computers, Materials & Continua

Published Date

2023

Managing physical objects in the network’s periphery is made possible by the Internet of Things (IoT), revolutionizing human life. Open attacks and unauthorized access are possible with these IoT devices, which exchange data to enable remote access. These attacks are often detected using intrusion detection methodologies, although these systems’ effectiveness and accuracy are subpar. This paper proposes a new voting classifier composed of an ensemble of machine learning models trained and optimized using metaheuristic optimization. The employed metaheuristic optimizer is a new version of the whale optimization algorithm (WOA), which is guided by the dipper throated optimizer (DTO) to improve the exploration process of the traditional WOA optimizer. The proposed voting classifier categorizes the network intrusions robustly and efficiently. To assess the proposed approach, a dataset created from IoT devices is employed to record the efficiency of the proposed algorithm for binary attack categorization. The dataset records are balanced using the locality-sensitive hashing (LSH) and Synthetic Minority Oversampling Technique (SMOTE). The evaluation of the achieved results is performed in terms of statistical analysis and visual plots to prove the proposed approach’s effectiveness, stability, and significance. The achieved results confirmed the superiority of the proposed algorithm for the task of network intrusion detection.

Deep Convolutional Neural Network and Metaheuristic Optimization for Disease Detection in Plant Leaves

Authors

SK Towfek,Nima Khodadadi

Journal

Journal of Intelligent Systems and Internet of Things

Published Date

2023/1

In this research, we employed a deep convolutional neural network, often known as a Deep CNN, to propose a novel approach to the detection of illnesses in the leaves of plants. In order to train the Deep CNN model, a dataset that is already accessible is employed. This dataset contains photographs of the leaves of 39 distinct plant species. Six different methods of data augmentation were utilized, including image inversion, gamma correction, noise injection, principal component analysis (PCA), color enhancement, rotation, and scaling. We came to the conclusion that adding more data to a model can improve its accuracy. The proposed model was trained using many epochs, batch sizes, and dropout percentages over the course of its development. When utilizing validation data, the suggested model performs better than methods of transfer learning that are commonly utilized. Extensive simulations demonstrate that the proposed model is capable of an astounding 83.12% accuracy in data classification. The proposed research is more accurate than the many machine learning technologies that are currently in use. In addition to that, we put the suggested model through our consistency and reliability testing.

ANNA: advanced neural network algorithm for optimisation of structures

Authors

Nima Khodadadi,Siamak Talatahari,Amir H Gandomi

Journal

Proceedings of the Institution of Civil Engineers-Structures and Buildings

Published Date

2023/2/14

The purpose of this study is to develop an advanced neural network algorithm as a new optimisation for the optimal design of truss structures. The central concept of the algorithm is based on biological nerve structures and artificial neural networks. The performance of the proposed method is explored in engineering design problems. Two efficient methods for improving the standard neural network algorithm are considered here. The first is an enhanced initialisation mechanism based on opposite-based learning. The second relies on using a few tunable parameters to provide proper exploration and exploitation abilities for the algorithm, enabling better solutions to be found while the required structural analyses are reduced. The new algorithm's performance is investigated by using five well-known restricted benchmarks to assess its efficiency in relation to the latest optimisation techniques. The outcome of the …

Improved Dipper-Throated Optimization for Forecasting Metamaterial Design Bandwidth for Engineering Applications

Authors

Amal H Alharbi,Abdelaziz A Abdelhamid,Abdelhameed Ibrahim,SK Towfek,Nima Khodadadi,Laith Abualigah,Doaa Sami Khafaga,Ayman EM Ahmed

Journal

Biomimetics

Published Date

2023/6/7

Metamaterials have unique physical properties. They are made of several elements and are structured in repeating patterns at a smaller wavelength than the phenomena they affect. Metamaterials’ exact structure, geometry, size, orientation, and arrangement allow them to manipulate electromagnetic waves by blocking, absorbing, amplifying, or bending them to achieve benefits not possible with ordinary materials. Microwave invisibility cloaks, invisible submarines, revolutionary electronics, microwave components, filters, and antennas with a negative refractive index utilize metamaterials. This paper proposed an improved dipper throated-based ant colony optimization (DTACO) algorithm for forecasting the bandwidth of the metamaterial antenna. The first scenario in the tests covered the feature selection capabilities of the proposed binary DTACO algorithm for the dataset that was being evaluated, and the second scenario illustrated the algorithm’s regression skills. Both scenarios are part of the studies. The state-of-the-art algorithms of DTO, ACO, particle swarm optimization (PSO), grey wolf optimizer (GWO), and whale optimization (WOA) were explored and compared to the DTACO algorithm. The basic multilayer perceptron (MLP) regressor model, the support vector regression (SVR) model, and the random forest (RF) regressor model were contrasted with the optimal ensemble DTACO-based model that was proposed. In order to assess the consistency of the DTACO-based model that was developed, the statistical research made use of Wilcoxon’s rank-sum and ANOVA tests.

Innovative feature selection method based on hybrid sine cosine and dipper throated optimization algorithms

Authors

Abdelaziz A Abdelhamid,El-Sayed M El-Kenawy,Abdelhameed Ibrahim,Marwa M Eid,Doaa Sami Khafaga,Amel Ali Alhussan,Seyedali Mirjalili,Nima Khodadadi,Wei Hong Lim,Mahmoud Y Shams

Journal

IEEE Access

Published Date

2023/7/26

Introduction In pattern recognition and data mining, feature selection is one of the most crucial tasks. To increase the efficacy of classification algorithms, it is necessary to identify the most relevant subset of features in a given domain. This means that the feature selection challenge can be seen as an optimization problem, and thus meta-heuristic techniques can be utilized to find a solution. Methodology In this work, we propose a novel hybrid binary meta-heuristic algorithm to solve the feature selection problem by combining two algorithms: Dipper Throated Optimization (DTO) and Sine Cosine (SC) algorithm. The new algorithm is referred to as bSCWDTO. We employed the sine cosine algorithm to improve the exploration process and ensure the optimization algorithm converges quickly and accurately. Thirty datasets from the University of California Irvine (UCI) machine learning repository are used to evaluate the …

An Optimized Model Based on Deep Learning and Gated Recurrent Unit for COVID-19 Death Prediction

Authors

Zahraa Tarek,Mahmoud Y Shams,SK Towfek,Hend K Alkahtani,Abdelhameed Ibrahim,Abdelaziz A Abdelhamid,Marwa M Eid,Nima Khodadadi,Laith Abualigah,Doaa Sami Khafaga,Ahmed M Elshewey

Journal

Biomimetics

Published Date

2023/11/17

The COVID-19 epidemic poses a worldwide threat that transcends provincial, philosophical, spiritual, radical, social, and educational borders. By using a connected network, a healthcare system with the Internet of Things (IoT) functionality can effectively monitor COVID-19 cases. IoT helps a COVID-19 patient recognize symptoms and receive better therapy more quickly. A critical component in measuring, evaluating, and diagnosing the risk of infection is artificial intelligence (AI). It can be used to anticipate cases and forecast the alternate incidences number, retrieved instances, and injuries. In the context of COVID-19, IoT technologies are employed in specific patient monitoring and diagnosing processes to reduce COVID-19 exposure to others. This work uses an Indian dataset to create an enhanced convolutional neural network with a gated recurrent unit (CNN-GRU) model for COVID-19 death prediction via IoT. The data were also subjected to data normalization and data imputation. The 4692 cases and eight characteristics in the dataset were utilized in this research. The performance of the CNN-GRU model for COVID-19 death prediction was assessed using five evaluation metrics, including median absolute error (MedAE), mean absolute error (MAE), root mean squared error (RMSE), mean square error (MSE), and coefficient of determination (R2). ANOVA and Wilcoxon signed-rank tests were used to determine the statistical significance of the presented model. The experimental findings showed that the CNN-GRU model outperformed other models regarding COVID-19 death prediction.

Metaheuristics for clustering problems

Authors

Farhad Soleimanian Gharehchopogh,Benyamin Abdollahzadeh,Nima Khodadadi,Seyedali Mirjalili

Published Date

2023/1/1

Data clustering is an essential, unsupervised classification method for discovering hidden patterns or information of a dataset. The literature shows metaheuristics are a reliable alternative to conventional clustering algorithms for data clustering problems. In this chapter, we compare 12 metaheuristics in this area. The algorithms used are Genetic Algorithm (GA), Gray Wolf Optimizer (GWO), Differential Evolution (DE), Biogeography-based Optimization (BBO), Harmony Search (HS), Particle Swarm Optimization (PSO), African Vulture Optimization Algorithm (AVOA), Firefly Algorithm (FFA), Symbiotic Organism Search (SOS), Artificial Bee Colony (ABC) algorithm, Whale Optimization Algorithm (WOA), and Artificial Gorilla Troops Optimization (AGTO). These algorithms were selected based on their unique capabilities. To test and evaluate the performance of the selected algorithms to solve the clustering problem, we use …

Feature selection in wind speed forecasting systems based on meta-heuristic optimization

Authors

El-Sayed M El-Kenawy,Seyedali Mirjalili,Nima Khodadadi,Abdelaziz A Abdelhamid,Marwa M Eid,M El-Said,Abdelhameed Ibrahim

Journal

Plos one

Published Date

2023/2/7

Technology for anticipating wind speed can improve the safety and stability of power networks with heavy wind penetration. Due to the unpredictability and instability of the wind, it is challenging to accurately forecast wind power and speed. Several approaches have been developed to improve this accuracy based on processing time series data. This work proposes a method for predicting wind speed with high accuracy based on a novel weighted ensemble model. The weight values in the proposed model are optimized using an adaptive dynamic grey wolf-dipper throated optimization (ADGWDTO) algorithm. The original GWO algorithm is redesigned to emulate the dynamic group-based cooperative to address the difficulty of establishing the balance between exploration and exploitation. Quick bowing movements and a white breast, which distinguish the dipper throated birds hunting method, are employed to improve the proposed algorithm exploration capability. The proposed ADGWDTO algorithm optimizes the hyperparameters of the multi-layer perceptron (MLP), K-nearest regressor (KNR), and Long Short-Term Memory (LSTM) regression models. A dataset from Kaggle entitled Global Energy Forecasting Competition 2012 is employed to assess the proposed algorithm. The findings confirm that the proposed ADGWDTO algorithm outperforms the literature’s state-of-the-art wind speed forecasting algorithms. The proposed binary ADGWDTO algorithm achieved average fitness of 0.9209 with a standard deviation fitness of 0.7432 for feature selection, and the proposed weighted optimized ensemble model (Ensemble using ADGWDTO …

Waterwheel plant algorithm: A novel metaheuristic optimization method

Authors

Abdelaziz A Abdelhamid,SK Towfek,Nima Khodadadi,Amel Ali Alhussan,Doaa Sami Khafaga,Marwa M Eid,Abdelhameed Ibrahim

Journal

Processes

Published Date

2023/5/15

Attempting to address optimization problems in various scientific disciplines is a fundamental and significant difficulty requiring optimization. This study presents the waterwheel plant technique (WWPA), a novel stochastic optimization technique motivated by natural systems. The proposed WWPA’s basic concept is based on modeling the waterwheel plant’s natural behavior while on a hunting expedition. To find prey, WWPA uses plants as search agents. We present WWPA’s mathematical model for use in addressing optimization problems. Twenty-three objective functions of varying unimodal and multimodal types were used to assess WWPA’s performance. The results of optimizing unimodal functions demonstrate WWPA’s strong exploitation ability to get close to the optimal solution, while the results of optimizing multimodal functions show WWPA’s strong exploration ability to zero in on the major optimal region of the search space. Three engineering design problems were also used to gauge WWPA’s potential for improving practical programs. The effectiveness of WWPA in optimization was evaluated by comparing its results with those of seven widely used metaheuristic algorithms. When compared with eight competing algorithms, the simulation results and analyses demonstrate that WWPA outperformed them by finding a more proportionate balance between exploration and exploitation.

See List of Professors in Nima Khodadadi University(Iran University of Science and Technology)

Nima Khodadadi FAQs

What is Nima Khodadadi's h-index at Iran University of Science and Technology?

The h-index of Nima Khodadadi has been 29 since 2020 and 29 in total.

What are Nima Khodadadi's top articles?

The articles with the titles of

Hippopotamus optimization algorithm: a novel nature-inspired optimization algorithm

Predictive Analytics and Machine Learning in Direct Marketing for Anticipating Bank Term Deposit Subscriptions

Puma optimizer (PO): A novel metaheuristic optimization algorithm and its application in machine learning

Modelling the compressive strength of geopolymer recycled aggregate concrete using ensemble machine learning

Multi-objective archived-based whale optimization algorithm

Electric eel foraging optimization: A new bio-inspired optimizer for engineering applications

Whale optimization algorithm for optimization of truss structures with multiple frequency constraints

Greylag goose optimization: Nature-inspired optimization algorithm

...

are the top articles of Nima Khodadadi at Iran University of Science and Technology.

What are Nima Khodadadi's research interests?

The research interests of Nima Khodadadi are: Structural Optimization, Artificial Intelligence, Evolutionary algorithms, Engineering Optimization

What is Nima Khodadadi's total number of citations?

Nima Khodadadi has 2,005 citations in total.

What are the co-authors of Nima Khodadadi?

The co-authors of Nima Khodadadi are A. Kaveh, Abdelhameed Ibrahim, Siamak TalatAhari, Seyed Mohammad Mirjalili, Armin Dadras Eslamlou, Francisco De Caso.

    Co-Authors

    H-index: 88
    A. Kaveh

    A. Kaveh

    Iran University of Science and Technology

    H-index: 61
    Abdelhameed Ibrahim

    Abdelhameed Ibrahim

    Mansoura University

    H-index: 52
    Siamak TalatAhari

    Siamak TalatAhari

    University of Tabriz

    H-index: 21
    Seyed Mohammad Mirjalili

    Seyed Mohammad Mirjalili

    Concordia University

    H-index: 16
    Armin Dadras Eslamlou

    Armin Dadras Eslamlou

    Iran University of Science and Technology

    H-index: 9
    Francisco De Caso

    Francisco De Caso

    University of Miami

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