Zhi‐Hui Zhan
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View article: InstantSfM: Fully Sparse and Parallel Structure-from-Motion
InstantSfM: Fully Sparse and Parallel Structure-from-Motion Open
Structure-from-Motion (SfM), a method that recovers camera poses and scene geometry from uncalibrated images, is a central component in robotic reconstruction and simulation. Despite the state-of-the-art performance of traditional SfM meth…
View article: A hybrid feature-based heterogeneous graph transformer method for cheating official account mining
A hybrid feature-based heterogeneous graph transformer method for cheating official account mining Open
As cheating official accounts (COAs) in social media has posed a significant threat to society, how to detect COAs has attracted increasing attention worldwide. However, mining COAs is challenging because the COAs are always much fewer tha…
View article: Artificial intelligence-based methods for protein structure prediction: a survey
Artificial intelligence-based methods for protein structure prediction: a survey Open
View article: Feedback linearized sliding mode controller for high-power PEMFC thermal management system adapted to road driving cycle
Feedback linearized sliding mode controller for high-power PEMFC thermal management system adapted to road driving cycle Open
A feedback linearized sliding mode controller (FLSMC) is designed to achieve high-precision temperature control of a high-power proton exchange membrane fuel cell (PEMFC) under current disturbance, and a water-cooled heat exchanger is deve…
View article: A New Scope and Domain Measure Comparison Method for Global Convergence Analysis in Evolutionary Computation
A New Scope and Domain Measure Comparison Method for Global Convergence Analysis in Evolutionary Computation Open
Convergence analysis is a fundamental research topic in evolutionary computation (EC). The commonly used analysis method models the EC algorithm as a homogeneous Markov chain for analysis, which is not always suitable for different EC vari…
View article: Large-Scale Heliostat Field Optimization for Solar Power Tower System Using Matrix-Based Differential Evolution
Large-Scale Heliostat Field Optimization for Solar Power Tower System Using Matrix-Based Differential Evolution Open
View article: Hm-Net: An Improved Few-Shot Learning Model for the Detection of Feature-Degraded Images
Hm-Net: An Improved Few-Shot Learning Model for the Detection of Feature-Degraded Images Open
View article: An Efficient Genetic Algorithm for Multi-Uav System Coverage Path Planning on Large-Scale Target Sets
An Efficient Genetic Algorithm for Multi-Uav System Coverage Path Planning on Large-Scale Target Sets Open
View article: Two-Stage Differential Evolution Algorithm with Clustering for Heterogeneous Multiply Unmanned Aerial Vehicles System Search and Rescue Route Planning
Two-Stage Differential Evolution Algorithm with Clustering for Heterogeneous Multiply Unmanned Aerial Vehicles System Search and Rescue Route Planning Open
View article: A Performance Investigation of Multimodal Multiobjective Optimization Algorithms in Solving Two Types of Real-World Problems
A Performance Investigation of Multimodal Multiobjective Optimization Algorithms in Solving Two Types of Real-World Problems Open
In recent years, multimodal multiobjective optimization algorithms (MMOAs) based on evolutionary computation have been widely studied. However, existing MMOAs are mainly tested on benchmark function sets such as the 2019 IEEE Congress on E…
View article: Evolutionary computation for unmanned aerial vehicle path planning: a survey
Evolutionary computation for unmanned aerial vehicle path planning: a survey Open
Unmanned aerial vehicle (UAV) path planning aims to find the optimal flight path from the start point to the destination point for each aerial vehicle. With the rapid development of UAV technology, UAVs are required to tackle missions in i…
View article: A Landscape-Aware Differential Evolution for Multimodal Optimization Problems
A Landscape-Aware Differential Evolution for Multimodal Optimization Problems Open
How to simultaneously locate multiple global peaks and achieve certain accuracy on the found peaks are two key challenges in solving multimodal optimization problems (MMOPs). In this paper, a landscape-aware differential evolution (LADE) a…
View article: Learning to Transfer for Evolutionary Multitasking
Learning to Transfer for Evolutionary Multitasking Open
Evolutionary multitasking (EMT) is an emerging approach for solving multitask optimization problems (MTOPs) and has garnered considerable research interest. The implicit EMT is a significant research branch that utilizes evolution operator…
View article: Guest Editorial: Special issue on trustworthy machine learning for behavioural and social computing
Guest Editorial: Special issue on trustworthy machine learning for behavioural and social computing Open
View article: A dual population collaborative harmony search algorithm with adaptive population size for the system reliability-redundancy allocation problems
A dual population collaborative harmony search algorithm with adaptive population size for the system reliability-redundancy allocation problems Open
Aiming at the problem that the diversity of the current double population algorithm with dynamic population size reduction cannot be guaranteed in real time in iteration and is easy to fall into local optimum, this study presents a dual po…
View article: Protein Structure Prediction Using a New Optimization-Based Evolutionary and Explainable Artificial Intelligence Approach
Protein Structure Prediction Using a New Optimization-Based Evolutionary and Explainable Artificial Intelligence Approach Open
Protein structure prediction (PSP) is an important scientific problem because it helps humans to understand how proteins perform their biological functions. This paper models the PSP problem as a multi-objective optimization problem with t…
View article: Knowledge Structure Preserving-Based Evolutionary Many-Task Optimization
Knowledge Structure Preserving-Based Evolutionary Many-Task Optimization Open
As a challenging research topic in evolutionary multitask optimization (EMTO), evolutionary many-task optimization (EMaTO) aims at solving more than three tasks simultaneously. The design of the EMaTO algorithm generally needs to consider …
View article: An Efficient Genetic Algorithm for Multiple Unmanned Aerial Vehicle System Coverage Path Planning on Large-Scale Target Sets
An Efficient Genetic Algorithm for Multiple Unmanned Aerial Vehicle System Coverage Path Planning on Large-Scale Target Sets Open
View article: Adaptive Elite Learning Particle Swarm Optimization Algorithm with Complementary Sub-Strategies for Multimodal Problems
Adaptive Elite Learning Particle Swarm Optimization Algorithm with Complementary Sub-Strategies for Multimodal Problems Open
View article: Grid Classification-Based Surrogate-Assisted Particle Swarm Optimization for Expensive Multiobjective Optimization
Grid Classification-Based Surrogate-Assisted Particle Swarm Optimization for Expensive Multiobjective Optimization Open
Surrogate-assisted evolutionary algorithms (SAE-As), mainly including regression-based SAEAs and classification-based SAEAs, are promising for solving expensive multi-objective optimization problems (EMOPs). Regression-based SAEAs usually …
View article: Multiple Tasks for Multiple Objectives: A New Multiobjective Optimization Method via Multitask Optimization
Multiple Tasks for Multiple Objectives: A New Multiobjective Optimization Method via Multitask Optimization Open
Handling conflicting objectives and finding multiple Pareto optimal solutions are two challenging issues in solving multiobjective optimization problems (MOPs). Inspired by the efficiency of multitask optimization (MTO) in finding multiple…
View article: Hyperparameters optimization of convolutional neural network based on local autonomous competition harmony search algorithm
Hyperparameters optimization of convolutional neural network based on local autonomous competition harmony search algorithm Open
Because of the good performance of convolutional neural network (CNN), it has been extensively used in many fields, such as image, speech, text, etc. However, it is easily affected by hyperparameters. How to effectively configure hyperpara…
View article: Block-Level Knowledge Transfer for Evolutionary Multitask Optimization
Block-Level Knowledge Transfer for Evolutionary Multitask Optimization Open
Evolutionary multitask optimization is an emerging research topic that aims to solve multiple tasks simultaneously. A general challenge in solving multitask optimization problems (MTOPs) is how to effectively transfer common knowledge betw…
View article: Knowledge Learning for Evolutionary Computation
Knowledge Learning for Evolutionary Computation Open
Evolutionary computation (EC) is a kind of meta-heuristic algorithm that takes inspiration from natural evolution and swarm intelligence behaviors. In the EC algorithm, there is a huge amount of data generated during the evolutionary proce…
View article: Transferable Adaptive Differential Evolution for Many-Task Optimization
Transferable Adaptive Differential Evolution for Many-Task Optimization Open
The evolutionary multitask optimization (EMTO) algorithm is a promising approach to solve many-task optimization problems (MaTOPs), in which similarity measurement and knowledge transfer (KT) are two key issues. Many existing EMTO algorith…
View article: Knowledge graph of wastewater-based epidemiology development: A data-driven analysis based on research topics and trends
Knowledge graph of wastewater-based epidemiology development: A data-driven analysis based on research topics and trends Open
View article: A Novel Evolutionary Algorithm With Column and Sub-Block Local Search for <i>Sudoku</i> Puzzles
A Novel Evolutionary Algorithm With Column and Sub-Block Local Search for <i>Sudoku</i> Puzzles Open
Sudoku puzzles are not only popular intellectual games but also NP-hard combinatorial problems related to various real-world applications, which have attracted much attention worldwide. Although many efficient tools, such as evolutionary c…
View article: An Efficient Coyote Optimization Algorithm Based on Population States Monitoring and its Applications
An Efficient Coyote Optimization Algorithm Based on Population States Monitoring and its Applications Open
View article: Learning-Aided Evolution for Optimization
Learning-Aided Evolution for Optimization Open
Learning and optimization are the two essential abilities of human beings for problem solving. Similarly, computer scientists have made great efforts to design artificial neural network (ANN) and evolutionary computation (EC) to simulate t…
View article: Bi-Directional Feature Fixation-Based Particle Swarm Optimization for Large-Scale Feature Selection
Bi-Directional Feature Fixation-Based Particle Swarm Optimization for Large-Scale Feature Selection Open
Feature selection, which aims to improve the classification accuracy and reduce the size of the selected feature subset, is an important but challenging optimization problem in data mining. Particle swarm optimization (PSO) has shown promi…