
Ali Asghar HeidariNational University of Singapore | NUS · Department of Computer Science
Ali Asghar Heidari
PhD. Exceptionally Talented Researcher funded by Iran's National Elites Foundation (INEF)
About
323
Publications
275,714
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Introduction
Dr. Ali Asghar Heidari has been an Exceptionally Talented Ph.D. Researcher with the School of Computing National University of Singapore (NUS), University of Tehran, and Iran's National Elites Foundation (INEF). He contributes to the best journals of IEEE Elsevier and has a deep vision of performance optimization. Website for all open source codes: https://aliasgharheidari.com
Additional affiliations
December 2017 - January 2020
Iran's National Elites Foundation (INEF)
Position
- National Elite Researcher
Description
- From 2017 to 2020 and from 2021 to 2025. Iran's National Elites Foundation (INEF) recognizes and supports a few researchers from all universities at a competitive national level. Exceptionally talented members of the foundation include all those who expose exceptionally high intellectual capacity, academic aptitude, creative ability, and artistic talents, especially contributors to the promotion of global science.
September 2015 - September 2020
Education
December 2018 - February 2020
September 2015 - September 2019
September 2012 - September 2015
Publications
Publications (323)
Analyzing surveillance videos is mandatory for the public and industrial security. Overwhelming growth in computer vision fields has been made to automate the surveillance system in terms of human activity recognition such as behavior analysis, Violence Detection (VD), etc. However, it is challenging to detect and analyze the violent scenes intelli...
An efficient swarm intelligence algorithm is proposed to solve continuous multi-type optimization problems, named the fata morgana algorithm (FATA). By mimicking the process of mirage formation, FATA designs the mirage light filtering principle (MLF) and the light propagation strategy (LPS), respectively. The MLF strategy, combined with the definit...
This study introduces Polar Lights Optimization (PLO), a metaheuristic algorithm inspired by the aurora phenomenon or polar lights. The aurora is a unique natural spectacle that occurs when energetic particles from the solar wind converge at the Earth's poles, influenced by the geomagnetic field and the Earth's atmosphere. By analyzing the motion o...
The moss growth optimization (MGO), introduced in this paper, is an algorithm inspired by the moss growth in the natural environment. The MGO algorithm initially determines the evolutionary direction of the population through a mechanism called the determination of wind direction, which employs a method of partitioning the population. Meanwhile, dr...
Meta-heuristic algorithms, particularly those based on swarm intelligence, are highly effective for solving black-box optimization problems. However, maintaining a balance between exploration and exploitation within these algorithms remains a significant challenge. This paper introduces a useful algorithm, called Escape or Escape Algorithm (ESC), i...
To solve bankruptcy prediction tasks, we proposed an improved rime optimization technique (RMRIME). The proposed RMRIME algorithm first employs roulette wheel selection step, introducing random individuals into the position updating process to expand the search space and boost the RMRIME’s exploration power. Also, a mutation idea is utilized, which...
The Harris Hawks optimization (HHO) algorithm, categorized as a powerful meta-heuristic algorithm (MA), has gained extensive usage because it lacks parameter adjustment and shows powerful optimization ability. At the same time, it cannot be ignored within the realm of feature selection. However, HHO is deficient in its capacity to escape local opti...
RIME, a physics-based heuristic algorithm, simulates the natural phenomenon of rime generation and possesses a robust capacity for global exploration, enabling it to escape local optima. However, testing revealed that RIME suffers from slow convergence during the later stages of evaluation, weak individual exploitation capabilities, and subpar popu...
With the increase in the number of objectives, the curse of dimensionality will
eventually occur in some practical multi-objective optimization problems. This situation
will become more deteriorate when the multi-objective changes into many-objective
optimization problems (MaOPs) with more than 15 objectives, which makes it more
difficult for evolu...
The Weighted Mean of Vectors Algorithm (INFO) is an enhanced weighted average method that optimizes vector positions using three strategies: updating rule, vector combination, and local search. This algorithm exhibits notable optimization capabilities and high convergence accuracy. However, it is not without limitations; specifically, it tends to b...
The increasing demand for electricity presents substantial challenges in power system planning, particularly optimizing the Optimal Power Flow (OPF) problem. The OPF problem entails establishing the best settings for control variables in a power system to reduce objectives such as generating cost and transmission losses while meeting operational re...
This paper proposes an improved version of the Partial Reinforcement Optimizer (PRO), termed LNPRO. The LNPRO has undergone a learner phase, which allows for further communication of information among the PRO population, changing the state of the PRO in terms of self-strengthening. Furthermore, the Nelder-Mead simplex is used to optimize the best a...
Meta-heuristic algorithms, particularly those based on swarm intelligence, are highly effective for solving black-box optimization problems. However, maintaining a balance between exploration and exploitation within these algorithms remains a significant challenge. This paper introduces a useful algorithm, called Escape or Escape Algorithm (ESC), i...
The persistently high incidence of breast cancer emphasizes the need for precise detection in its diagnosis. Computer-aided medical systems are designed to provide accurate information and reduce human errors, in which accurate and effective segmentation of medical images plays a pivotal role in improving clinical outcomes. Multilevel Threshold Ima...
Feature selection (FS) is a crucial data preprocessing technique that selects important features to enhance learning efficiency, yet it encounters challenges due to the high-dimensional search space. This paper introduces an improved version of the RIME optimization algorithm called ACGRIME. Inspired by rime-ice formation, RIME initially showed goo...
The bat algorithm (BA) is a metaheuristic algorithm for global optimisation that simulates the echolocation behaviour of bats with varying pulse rates of emission and loudness, which can be used to find the globally optimal solutions for various optimisation problems. Knowing the recent criticises of the originality of equations, the principle of B...
The Runge–Kutta optimiser (RUN) algorithm, renowned for its powerful optimisation capabilities, faces challenges in dealing with increasing complexity in real‐world problems. Specifically, it shows deficiencies in terms of limited local exploration capabilities and less precise solutions. Therefore, this research aims to integrate the topological s...
RIME is a recently introduced optimization algorithm that draws inspiration from natural phenomena. However, RIME has certain limitations. For example, it is prone to falling into Local Optima (LO), thus failing to find the Global Optima (GO), and has the problem of slow convergence. To solve these problems, this paper introduces an improved RIME a...
The moss growth optimization (MGO), introduced in this paper, is an algorithm inspired by the moss growth in the natural environment. The MGO algorithm initially determines the evolutionary direction of the population through a mechanism called the determination of wind direction, which employs a method of partitioning the population. Meanwhile, dr...
In engineering applications, optimal parameter design is crucial. While Slime Mould Algorithm (SMA) excels in parameter discovery under constrained conditions, it faces challenges in achieving global convergence and avoiding local opsecttimal traps in complex tasks. This paper introduces an enhanced variant of SMA, termed CCHSMA, which integrates a...
Lupus nephritis (LN) is a severe renal complication of systemic lupus erythematosus (SLE), characterized by kidney inflammation and damage, often leading to significant morbidity and mortality. Thus, aiding clinicians in the timely diagnosis of LN is imperative, with medical imaging serving as an essential adjunctive diagnostic tool.. Image segment...
Chronic obstructive pulmonary disease (COPD) causes irreversible airflow limitations, increasing global morbidity and mortality. Acute exacerbations (AECOPDs) worsen symptoms and may require mechanical ventilation, leading to complications. Understanding factors affecting AECOPD prognosis during mechanical ventilation is crucial. Inspired by rime i...
As a popular meta-heuristic algorithm, the Moth-Flame Optimization (MFO) algorithm has garnered significant interest owing to its high flexibility and straightforward implementation. However, when addressing engineering constraint problems with specific parameters, MFO also exhibits limitations such as fast convergence and a tendency to converge to...
The Educational Competition Optimizer (ECO) is a novel optimization algorithm inspired by competition dynamics in educational systems. It has been successfully applied to various optimization problems, particularly in improving the performance of machine learning models. In this module, we showcase how ECO can enhance the optimization of three key...
The Educational Competition Optimizer (ECO) is a novel optimization algorithm inspired by competition dynamics in educational systems. It has been successfully applied to various optimization problems, particularly in improving the performance of machine learning models. In this module, we showcase how ECO can enhance the optimization of three key...
The Educational Competition Optimizer (ECO) is a novel optimization algorithm inspired by competition dynamics in educational systems. It has been successfully applied to various optimization problems, particularly in improving the performance of machine learning models. In this module, we showcase how ECO can enhance the optimization of three key...
Colorectal polyps are one of the common gastrointestinal disorders. The precise segmentation of these polyps will assist physicians in diagnosing and treating these lesions. Polyps typically exhibit a variety of sizes and shapes, coupled with diverse scale variations. Additionally, the boundary between the polyp and the adjacent mucosa often lacks...
Integrating renewable energy sources, such as wind and photovoltaic power generation, into the power grid is crucial for sustainable power system development and mitigating pollutant emissions. However, these sources’ inherent uncertainty and randomness pose significant challenges to grid operations. Metaheuristic algorithms offer efficient solutio...
The source codes of PLO are available at https://aliasgharheidari.com/PO.html. This study introduces Polar Lights Optimization (PLO), an algorithm based on the aurora phenomenon or polar lights. The aurora is a unique natural spectacle that occurs when energetic particles from the solar wind converge at the Earth's poles, influenced by the geomagne...
In mining mineral resources, it is vital to monitor the stability of the rock body in real time, reasonably regulate the area of ground pressure concentration, and guarantee the safety of personnel and equipment. The microseismic signals generated by monitoring the rupture of the rock body can effectively predict the rock body disaster, but the cur...
All PDF and codes available at: https://aliasgharheidari.com/FATA.html
An efficient swarm intelligence algorithm is proposed to solve continuous multi-type optimization problems, named the fata morgana algorithm (FATA). By mimicking the process of mirage formation, FATA designs the mirage light filtering principle (MLF) and the light propagation s...
Medical imaging is essential in modern healthcare because it assists physicians in the diagnosis of cancer. Various tissues and features in medical imaging can be recognized using image segmentation algorithms. This feature makes it possible to pinpoint and define particular areas, which makes it easier to precisely locate and characterize anomalit...
With the widespread application of high-throughput sequencing technology in recent years, the scale of high-dimensional gene sequence datasets has rapidly expanded. However, due to the high-dimensional nature of gene sequence data, researchers face the challenge of processing such complex data. One common preprocessing technique that can improve pe...
Lupus nephritis (LN) is the most common symptom of systemic lupus erythematosus, emphasizing its importance in the field of medicine. The growing frequency of LN has increased the need for effective image segmentation algorithms. With the increasing prevalence of LN, the demand for efficient image segmentation techniques has grown. To enhance the e...
As countries attach importance to environmental protection, clean energy has become a hot topic. Among them, solar energy, as one of the efficient and easily accessible clean energy sources, has received widespread attention. An essential component in converting solar energy into electricity are solar cells. However, a major optimization difficulty...
In recent research, metaheuristic strategies stand out as powerful tools for complex optimization, capturing widespread attention. This study proposes the Educational Competition Optimizer (ECO), an algorithm created for diverse optimization tasks. ECO draws inspiration from the competitive dynamics observed in real-world educational resource alloc...
Women are commonly diagnosed with breast cancer (BC), and early detection can significantly increase the cure rate. This study suggested a multi-threshold image segmentation (MTIS) technique for dividing BC histological slice images to assist in identifying lesions and boost diagnostic effectiveness. The selection of the threshold combination, a ch...
A stable and accurate forecast of water quality parameters is crucial for planning and managing future investment programs. A well-known water quality indicator is the total dissolved solids (TDS), which measures the number of metals, minerals, and salts dissolved in a particular volume of water. This paper introduces an innovative approach, which...
In recent research, metaheuristic strategies stand out as powerful tools for complex optimization, capturing widespread attention. This study proposes the Educational Competition Optimizer (ECO), an algorithm created for diverse optimization tasks. ECO draws inspiration from the competitive dynamics observed in real-world educational resource alloc...
Rime optimization algorithm (RIME) encounters issues such as an imbalance between exploitation and exploration, susceptibility to local optima, and low convergence accuracy when handling problems. This paper introduces a variant of RIME called IRIME to address these drawbacks. IRIME integrates the soft besiege (SB) and composite mutation strategy (...
Runge Kutta Optimization (RUN) is a widely utilized metaheuristic algorithm. However, it suffers from these issues: the imbalance between exploration and exploitation and the tendency to fall into local optima when it solves real-world optimization problems. To address these challenges, this study aims to endow each individual in the population wit...
In optimization, metaheuristic algorithms have received extensive attention and research due to their excellent performance. The slime mould algorithm (SMA) is a newly proposed metaheuristic algorithm. It has the characteristics of fewer parameters and strong optimization ability. However, with the increasing difficulty of optimization problems, SM...
The Bat algorithm, a metaheuristic optimization technique inspired by the foraging behaviour of bats, has been employed to tackle optimization problems. Known for its ease of implementation, parameter tunability, and strong global search capabilities, this algorithm finds application across diverse optimization problem domains. However, in the face...
Intrusion detection system (IDS) classify network traffic as either threatening or normal based on data features, aiming to identify malicious activities attempting to compromise computer systems. However, the volume of intrusion-related data is increasing daily, and the redundant features within this data hinder the improvement of IDS classificati...
The slime mould algorithm (SMA), as an emerging and promising swarm intelligence algorithm, has been studied in various fields. However, SMA suffers from issues such as easily getting trapped in local optima and slow convergence, which pose challenges when applied to practical problems. Therefore, this study proposes an improved SMA, named HESMA, b...
Image segmentation techniques play a vital role in aiding COVID-19 diagnosis. Multi-threshold image segmentation methods are favored for their computational simplicity and operational efficiency. Existing threshold selection techniques in multi-threshold image segmentation, such as Kapur based on exhaustive enumeration, often hamper efficiency and...
Feature selection (FS) is an essential aspect of artificial intelligence (AI), providing guidance in navigating the vast array of features within a dataset to uncover their utmost relevance and significance. This process holds immense importance in the advancement and effectiveness of AI, as it determines how well AI systems are trained and optimiz...
This article presents a novel optimization approach called RSWTLBO for accurately identifying unknown parameters in photovoltaic (PV) models. The objective is to address challenges related to the detection and maintenance of PV systems and the improvement of conversion efficiency. RSWTLBO combines adaptive parameter w, Single Solution Optimization...
Grey wolf optimizer (GWO) is a highly valued heuristic algorithm in many fields. However, for some complex problems, especially high‐dimensional and multimodal problems, the basic algorithm has limited computational power and cannot get a satisfactory answer. In order to find a better solution, an improved algorithm based on GWO is proposed herein....
The source codes of this paper will be available in https://aliasgharheidari.com/AO.html.
This study proposes an efficient metaheuristic algorithm called the Artemisinin Optimization (AO) algorithm. This algorithm draws inspiration from the process of artemisinin medicine therapy for malaria, which involves the comprehensive eradication of malarial...
The source codes of this paper will be available in https://aliasgharheidari.com/AO.html. This study proposes an efficient metaheuristic algorithm called the Artemisinin Optimization (AO) algorithm. This algorithm draws inspiration from the process of artemisinin medicine therapy for malaria, which involves the comprehensive eradication of malarial...
When confronted with high-dimensional data, evolutionary feature selection methods encounter the formidable challenge known as the “curse of dimensionality”. To overcome this challenge, our study delves into developing an optimized algorithm, enhanced RIME (ERIME), which ingeniously integrates feature information entropy pruning and the DBSCAN spat...
Modern medicine has produced large genetic datasets of high dimensions through advanced gene sequencing technology, and processing these data is of great significance for clinical decision-making. Gene selection (GS) is an important data preprocessing technique that aims to select a subset of feature information to improve performance and reduce da...
Previous studies have emphasized the potential of threshold image segmentation for early breast cancer detection. However, traditional methods encounter challenges regarding low segmentation efficiency and accuracy. Addressing this, the ant colony optimization algorithm for continuous optimization (ACOR) shows promise. Yet, existing ACOR variants s...
In medical image processing, multi-threshold image segmentation has been challenging, as selecting appropriate thresholds is crucial for distinguishing different structures within an image, especially when dealing with breast cancer images. Breast cancer images are complex with multiple tissue types, which pose challenges to precise diagnosis. A we...
Image segmentation is a crucial technique in analyzing X-ray medical images as it aids in uncovering relevant information concealed within a patient's body, a pivotal aspect of the diagnostic process. The effectiveness of computer-aided diagnosis systems largely depends on the accuracy of the image processing methods. In recent years, multi-thresho...
Tuberculous pleural effusion poses a significant threat to human health due to its potential for severe disease and mortality. Without timely treatment, it may lead to fatal consequences. Therefore, early identification and prompt treatment are crucial for preventing problems such as chronic lung disease, respiratory failure, and death. This study...
In recent years, with the increasing demand for social production, engineering design problems have gradually become more and more complex. Many novel and well-performing meta-heuristic algorithms have been studied and developed to cope with this problem. Among them, the Spherical Evolutionary Algorithm (SE) is one of the classical representative m...
Extracting photovoltaic (PV) model parameters based on the measured voltage and current information is crucial in the simulation and management of PV systems. To accurately and reliably extract the unknown parameters of different PV models, this paper proposes an improved multi-verse optimizer that integrates an iterative chaos map and the Nelder–M...
Stochastic optimization methods have gained significant prominence as effective techniques in contemporary research, addressing complex optimization challenges efficiently. This paper introduces the Parrot Optimizer (PO), an efficient optimization method inspired by key behaviors observed in trained Pyrrhura Molinae parrots. The study features qual...
In the pursuit of enhancing the efficiency of solar cells, accurate estimation of unspecified parameters in the solar photovoltaic (PV) cell model is imperative. An advanced salp swarm algorithm called the Super‐Evolutionary Nelder‐Mead Salp Swarm Algorithm (SENMSSA) is proposed to achieve this objective. The proposed SENMSSA addresses the limitati...