aliasgharheidaricom
Dr. Ali Asghar Heidari (http://aliasgharheidari.com/) has been an exceptionally talented researcher at SOC, NUS Singapore, and the University of Tehran
National University of SingaporeSingapore
Pinned Repositories
Escape-An-optimization-method-based-on-crowd-evacuation-behaviors
Escape Algorithm (ESC) models crowd evacuation behaviors to achieve effective optimization through a balance of exploration and exploitation strategies
FATA-An-Efficient-Optimization-Method-Based-on-Geophysics
The source codes of FATA optimizer are also publicly available at https://aliasgharheidari.com/FATA.html. This study presents the analysis and principle of an effective algorithm to optimize different problems.
Harris-Hawks-Optimization-Algorithm-and-Applications
Harris Hawks Optimization (HHO) is a nature-inspired metaheuristic algorithm that simulates the cooperative hunting behavior of Harris' hawks. Widely used in engineering, machine learning, and resource allocation, HHO is renowned for its simplicity, versatility, and effectiveness in finding global optima.
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.
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.
Polar-Lights-Optimizer-Algorithm-and-Applications-in-Image-Segmentation-and-Feature-Selection
Polar Lights Optimizer (PLO) introduces unique strategies based on the aurora phenomenon, using gyration motion for local exploitation and aurora oval walk for global exploration.
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
The-Moss-Growth-Optimization-MGO-Concepts-and-performance
Moss Growth Optimization (MGO) mimics the natural growth processes of moss to achieve effective optimization through a combination of local and global search strategies
aliasgharheidaricom's Repositories
aliasgharheidaricom/Harris-Hawks-Optimization-Algorithm-and-Applications
Harris Hawks Optimization (HHO) is a nature-inspired metaheuristic algorithm that simulates the cooperative hunting behavior of Harris' hawks. Widely used in engineering, machine learning, and resource allocation, HHO is renowned for its simplicity, versatility, and effectiveness in finding global optima.
aliasgharheidaricom/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
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/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.
aliasgharheidaricom/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.
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/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.
aliasgharheidaricom/Polar-Lights-Optimizer-Algorithm-and-Applications-in-Image-Segmentation-and-Feature-Selection
Polar Lights Optimizer (PLO) introduces unique strategies based on the aurora phenomenon, using gyration motion for local exploitation and aurora oval walk for global exploration.
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/The-Moss-Growth-Optimization-MGO-Concepts-and-performance
Moss Growth Optimization (MGO) mimics the natural growth processes of moss to achieve effective optimization through a combination of local and global search strategies
aliasgharheidaricom/Educational-Competition-Optimizer
The source codes of ECO optimizer are also publicly available at https://aliasgharheidari.com/ECO.html
aliasgharheidaricom/aliasgharheidaricom
Config files for my GitHub profile.
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-open-gpt
Collection of Open Source Projects Related to GPT/GPT相关开源项目合集🚀、精选🛠
aliasgharheidaricom/EasyLM
Easy to use model parallel large language models in JAX/Flax with pjit support on cloud TPU pods.
aliasgharheidaricom/FATA-An-Efficient-Optimization-Method-Based-on-Geophysics
The source codes of FATA optimizer are also publicly available at https://aliasgharheidari.com/FATA.html. This study presents the analysis and principle of an effective algorithm to optimize different problems.
aliasgharheidaricom/mafese
Feature Selection using Metaheuristics Made Easy: Open Source MAFESE Library in Python
aliasgharheidaricom/mlforecast
Scalable machine 🤖 learning for time series forecasting.
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
aliasgharheidaricom/Escape-An-optimization-method-based-on-crowd-evacuation-behaviors
Escape Algorithm (ESC) models crowd evacuation behaviors to achieve effective optimization through a balance of exploration and exploitation strategies
aliasgharheidaricom/AI-Ecosystem
This is a collection of AI ecosystem, which gathers and organizes various interesting and useful AI-related projects
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/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/DALL-E-CLONE
AI image generator, using openai DALL-E API 🎉
aliasgharheidaricom/teach-anything
Teach any questions in seconds (by OpenAI)