aliasgharheidaricom

Ali-Asghar-Heidari

Ali Asghar Heidari has been an Exceptionally Talented Researcher with the School of Computing, National University of Singapore (NUS), University of Tehran, and an elite researcher of Iran’s National Elites Foundation (INEF). He was born in 1989 and has studied information systems as an outstanding ranked one student with several awards from the College of Engineering, University of Tehran. He has been ranked among the top scientists for Computer science prepared by Guide2Research (https://www.guide2research.com/u/ali-asghar-heidari), the best portal for computer science research, as an outstanding researcher with an impressive record of cooperation on many international research projects with different top researchers from the optimization and artificial intelligence community. He has been ranked in the world’s top 2% scientists list of Stanford University, and Publons has recognized him as the top 1% peer reviewer in computer science and cross-field because he has reviewed more than 350 ISI papers for top journals he published on them. He has authored more than 110 research articles with over 6300 citations (i10-index of 74 and H-index of 44) in prestigious international journals, such as IEEE internet of thing, IEEE Transactions on Industrial Informatics, Information Fusion, Information Sciences, Future Generation Computer Systems, Renewable, and Sustainable Energy Reviews, Energy, Cleaner Production, Energy Reports, Energy Conversion and Management, Applied Soft Computing, Knowledge-Based Systems, IEEE Access, and Expert Systems with Applications. He has several highly cited and hot cited articles. His research interests include performance optimization, advanced machine learning, evolutionary computation, optimization, prediction, solar energy, information systems, and mathematical modeling. He was the second top reviewer and “outstanding reviewer” of applied soft computing journal in 2018. For more information, researchers can refer to his website https://aliasgharheidari.com.

Artemisinin-Optimizer-using-Malaria-Therapy-Algorithm-and-Applications-to-Medical-Image-Segmentation

The source codes of Artemisinin Optimization are also publicly available at https://aliasgharheidari.com/AO.html, This study presents the analysis and principle of AO algorithm to optimize different problems.

Harris-Hawks-Optimization-Algorithm-and-Applications

Source codes for HHO paper: Harris hawks optimization: Algorithm and applications: https://www.sciencedirect.com/science/article/pii/S0167739X18313530. In this paper, a novel population-based, nature-inspired optimization paradigm is proposed, which is called Harris Hawks Optimizer (HHO).

Hunger-Games-Search-Visions-Conception-Implementation-Deep-Analysis-and-Performance-Shifts

Visit: https://aliasgharheidari.com/HGS.html. HGS optimizer is a population-based method with stochastic switching elements that enrich its main exploratory and exploitative behaviors and flexibility of HGS in dealing with challenging problem landscapes. The algorithm has been compared to LSHADE, SPS_L_SHADE_EIG, LSHADE_cnEpSi, SHADE, SADE, MPEDE, and JDE methods.

INFO-An-Efficient-Optimization-Algorithm-based-on-Weighted-Mean-of-Vectors

The source codes of this algorithm are also publicly available at https://aliasgharheidari.com/INFO.html. This study presents the analysis and principle of an innovative optimizer named weIghted meaN oF vectOrs (INFO) to optimize different problems.

Parrot-optimizer-Algorithm-and-applications-to-medical-problems

The source codes of Parrot optimizer are also publicly available at https://aliasgharheidari.com/PO.html, This study presents the analysis and principle of an effective algorithm to optimize different problems.

RIME-A-physics-based-optimization

RIME A physics based optimization algorithm, Neurocomputing, 2023 https://doi.org/10.1016/j.neucom.2023.02.010, This paper proposes an efficient optimization algorithm based on the physical phenomenon of rime-ice

RUN-Beyond-the-Metaphor-An-Efficient-Optimization-Algorithm-Based-on-Runge-Kutta-Method

The optimization field suffers from the metaphor-based “pseudo-novel” or “fancy” optimizers. Most of these cliché methods mimic animals' searching trends and possess a small contribution to the optimization process itself. Most of these cliché methods suffer from the locally efficient performance, biased verification methods on easy problems, and high similarity between their components' interactions. This study attempts to go beyond the traps of metaphors and introduce a novel metaphor-free population-based optimization based on the mathematical foundations and ideas of the Runge Kutta (RK) method widely well-known in mathematics. The proposed RUNge Kutta optimizer (RUN) was developed to deal with various types of optimization problems in the future. The RUN utilizes the logic of slope variations computed by the RK method as a promising and logical searching mechanism for global optimization. This search mechanism benefits from two active exploration and exploitation phases for exploring the promising regions in the feature space and constructive movement toward the global best solution. Furthermore, an enhanced solution quality (ESQ) mechanism is employed to avoid the local optimal solutions and increase convergence speed. The RUN algorithm's efficiency was evaluated by comparing with other metaheuristic algorithms in 50 mathematical test functions and four real-world engineering problems. The RUN provided very promising and competitive results, showing superior exploration and exploitation tendencies, fast convergence rate, and local optima avoidance. In optimizing the constrained engineering problems, the metaphor-free RUN demonstrated its suitable performance as well. The authors invite the community for extensive evaluations of this deep-rooted optimizer as a promising tool for real-world optimization. The source codes, supplementary materials, and guidance for the developed method will be publicly available at different hubs at http://aliasgharheidari.com/RUN.html.

Slime-Mould-Algorithm-A-New-Method-for-Stochastic-Optimization-

In this paper, a new stochastic optimizer, which is called slime mould algorithm (SMA), is proposed based upon the oscillation mode of slime mould in nature. The proposed SMA has several new features with a unique mathematical model that uses adaptive weights to simulate the process of producing positive and negative feedback of the propagation wave of slime mould based on bio-oscillator to form the optimal path for connecting food with excellent exploratory ability and exploitation propensity. The proposed SMA is compared with up-to-date metaheuristics in an extensive set of benchmarks to verify the efficiency. Moreover, four classical engineering structure problems are utilized to estimate the efficacy of the algorithm in optimizing engineering problems. The results demonstrate that the algorithm proposed benefits from competitive, often outstanding performance on different search landscapes. The source codes and info of SMA are publicly available at: http://www.alimirjalili.com/SMA.html

Wavelet-PM2.5-Prediction-System

Heidari, A.A.; Akhoondzadeh, M.; Chen, H. A Wavelet PM2.5 Prediction System Using Optimized Kernel Extreme Learning with Boruta-XGBoost Feature Selection. Mathematics 2022, 10, 3566. https://doi.org/10.3390/math10193566

aliasgharheidaricom/RIME-A-physics-based-optimization

RIME A physics based optimization algorithm, Neurocomputing, 2023 https://doi.org/10.1016/j.neucom.2023.02.010, This paper proposes an efficient optimization algorithm based on the physical phenomenon of rime-ice

aliasgharheidaricom/Parrot-optimizer-Algorithm-and-applications-to-medical-problems

The source codes of Parrot optimizer are also publicly available at https://aliasgharheidari.com/PO.html, This study presents the analysis and principle of an effective algorithm to optimize different problems.

aliasgharheidaricom/Artemisinin-Optimizer-using-Malaria-Therapy-Algorithm-and-Applications-to-Medical-Image-Segmentation

The source codes of Artemisinin Optimization are also publicly available at https://aliasgharheidari.com/AO.html, This study presents the analysis and principle of AO algorithm to optimize different problems.

aliasgharheidaricom/Wavelet-PM2.5-Prediction-System

Heidari, A.A.; Akhoondzadeh, M.; Chen, H. A Wavelet PM2.5 Prediction System Using Optimized Kernel Extreme Learning with Boruta-XGBoost Feature Selection. Mathematics 2022, 10, 3566. https://doi.org/10.3390/math10193566

aliasgharheidaricom/Double-Mutational-Salp-Swarm-Algorithm-From-Optimal-Performance-Design-to-Analysis

Double Mutational Salp Swarm Algorithm: From Optimal Performance Design to Analysis, 04 October 2022 in Journal of Bionic Engineering

aliasgharheidaricom/aliasgharheidaricom

Config files for my GitHub profile.

aliasgharheidaricom/AI-Ecosystem

This is a collection of AI ecosystem, which gathers and organizes various interesting and useful AI-related projects

aliasgharheidaricom/Auto-GPT

An experimental open-source attempt to make GPT-4 fully autonomous.

aliasgharheidaricom/Awesome-AI

A curated list of awesome things related to artificial intelligence tools around the world wide web

aliasgharheidaricom/awesome-chatgpt-prompts

This repo includes ChatGPT prompt curation to use ChatGPT better.

aliasgharheidaricom/awesome-compbio-chatgpt

An awesome repository of community-curated applications of ChatGPT and other LLMs im computational biology

aliasgharheidaricom/awesome-gpt4

A curated list of prompts, tools, and resources regarding the GPT-4 language model.

aliasgharheidaricom/awesome-open-gpt

Collection of Open Source Projects Related to GPT/GPT相关开源项目合集🚀、精选🛠

aliasgharheidaricom/awesome-python-data-science

Probably the best curated list of data science software in Python.

aliasgharheidaricom/ChatGPT

ChatGPT clone using openAI API

aliasgharheidaricom/chatgpt-clone

this app is a ChatGPT Clone with DALL.E using OpenAIs text-davinci-003 and image generation Model

aliasgharheidaricom/ChatGPT-Clone-1

A ChatGPT clone along with an image generator Machine Learing model developed by me.

aliasgharheidaricom/ChatGPT-Data-Science-Prompts

A repository of 60 useful data science prompts for ChatGPT

aliasgharheidaricom/ChatGPT-Machine-Learning-Prompts

aliasgharheidaricom/DALL-E-CLONE

AI image generator, using openai DALL-E API 🎉

aliasgharheidaricom/EasyLM

Easy to use model parallel large language models in JAX/Flax with pjit support on cloud TPU pods.

aliasgharheidaricom/feature-engineering-and-feature-selection

A Guide for Feature Engineering and Feature Selection, with implementations and examples in Python.

aliasgharheidaricom/Messi_X-ChatGPT-Clone

Website Link :

aliasgharheidaricom/mlforecast

Scalable machine 🤖 learning for time series forecasting.

aliasgharheidaricom/permetrics

Artificial intelligence (AI, ML, DL) performance metrics implemented in Python

aliasgharheidaricom/SGX-Full-OrderBook-Tick-Data-Trading-Strategy

Providing the solutions for high-frequency trading (HFT) strategies using data science approaches (Machine Learning) on Full Orderbook Tick Data.

aliasgharheidaricom/shap

A game theoretic approach to explain the output of any machine learning model.

aliasgharheidaricom/teach-anything

Teach any questions in seconds (by OpenAI)

aliasgharheidaricom/transformers

🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

aliasgharheidaricom/Transformers-for-NLP-2nd-Edition

Transformer models from BERT to GPT-4, environments from Hugging Face to OpenAI. Fine-tuning, training, and prompt engineering examples. A bonus section with ChatGPT, GPT-3.5-turbo, GPT-4, and DALL-E including jump starting GPT-4, speech-to-text, text-to-speech, text to image generation with DALL-E and more