KINDI Center for Computing Research: Recent submissions
Now showing items 21-40 of 297
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Learning strategies for particle swarm optimizer: A critical review and performance analysis
( Elsevier , 2025 , Article)Nature has long inspired the development of swarm intelligence (SI), a key branch of artificial intelligence that models collective behaviors observed in biological systems for solving complex optimization problems. Particle ... -
A comprehensive survey of adaptive strategies in differential evolutionary algorithms
( Elsevier , 2025 , Article)Classical differential evolution (DE) encounters premature convergence when dealing with diverse optimization problems. This challenge has encouraged extensive research efforts aimed at improving and enhancing the original ... -
A reinforcement learning-assisted genetic programming algorithm for team formation problem considering person-job matching
( Elsevier , 2025 , Article)Efficient team formation is crucial for successful completion of new projects in a company. To address the team formation problem considering person-job matching (TFP-PJM), a 0-1 integer programming model with intuitionistic ... -
Carbon footprint of global Bitcoin mining: emissions beyond borders
( Springer , 2025 , Article)Executing a single Bitcoin transaction equates approximately to the greenhouse gas emissions of a moderate-sized electric or gasoline engine sedan vehicle traveling between 1600 and 2600 km. This research undertakes an ... -
Insects in agricultural greenhouses: a metagenomic analysis of microbes in Trialeurodes vaporariorum infesting tomato and cucumber crops
( Frontiers Media SA , 2025 , Article)Introduction: With the predicted 9-10 billion world population increase by 2050 and its accompanying need for sustainable food production, and with the harsh climate conditions challenging agriculture and food security in ... -
Class-incremental Learning for Time Series: Benchmark and Evaluation
( Association for Computing Machinery (ACM) , 2024 , Conference)Real-world environments are inherently non-stationary, frequently introducing new classes over time. This is especially common in time series classification, such as the emergence of new disease classification in healthcare ... -
Vanilla Gradient Descent for Oblique Decision Trees
( IOS Press , 2024 , Conference)Decision Trees (DTs) constitute one of the major highly non-linear AI models, valued, e.g., for their efficiency on tabular data. Learning accurate DTs is, however, complicated, especially for oblique DTs, and does take a ... -
An archive-assisted multi-modal multi-objective evolutionary algorithm
( Elsevier , 2024 , Article)The multi-modal multi-objective optimization problems (MMOPs) pertain to characteristic of the decision space that exhibit multiple sets of Pareto optimal solutions that are either identical or similar. The resolution of ... -
Energy-efficient multi-objective distributed assembly permutation flowshop scheduling by Q-learning based meta-heuristics
( Elsevier , 2024 , Article)This study addresses energy-efficient multi-objective distributed assembly permutation flowshop scheduling problems with minimisation of maximum completion time, mean of earliness and tardiness, and total carbon emission ... -
A staged fuzzy evolutionary algorithm for constrained large-scale multiobjective optimization
( Elsevier , 2024 , Article)Constrained multiobjective optimization problems (CMOPs) are prevalent in practical applications, where constraints play a significant role. Building on techniques from constrained single-objective optimization, classical ... -
Constrained large-scale multiobjective optimization based on a competitive and cooperative swarm optimizer
( Elsevier , 2024 , Article)Many engineering application problems can be modeled as constrained multiobjective optimization problems (CMOPs), which have attracted much attention. In solving CMOPs, existing algorithms encounter difficulties in balancing ... -
Prediction and Feedback Assisted Evolutionary Algorithms for Scheduling Urban Traffic Signals
( Institute of Electrical and Electronics Engineers Inc. (IEEE) , 2025 , Article)With the acceleration of urbanization, the traffic congestion issue is becoming more and more prominent in large cities. The effective scheduling of urban traffic signals becomes critical. This study proposes three novel ... -
High frequency volatility forecasting and risk assessment using neural networks-based heteroscedasticity model
( Elsevier , 2025 , Article)High frequency volatility forecasting is essential for timely risk management and informed decision-making in dynamic financial markets. However, accurate forecasting is challenging due to the rapid nature of market movements ... -
Differential evolution-based mixture distribution models for wind energy potential assessment: A comparative study for coastal regions of China
( Elsevier , 2025 , Article)Mixture distributions generally have higher flexibility than single distributions in describing wind speeds. However, the determination of their components is critical. This work evaluates suitable distributions for the ... -
A Reinforced Neighborhood Search Method Combined With Genetic Algorithm for Multi-Objective Multi-Robot Transportation System
( Institute of Electrical and Electronics Engineers Inc. (IEEE) , 2025 , Article)With the rapid advancement of artificial intelligence, autonomous multi-robot systems have been successfully applied to various domains. Therefore, developing intelligent routing and scheduling systems to efficiently ... -
An individual adaptive evolution and regional collaboration based evolutionary algorithm for large-scale constrained multiobjective optimization problems
( Elsevier , 2025 , Article)Large-scale constrained multiobjective optimization problems (LSCMOPs) refer to constrained multiobjective optimization problems (CMOPs) with large-scale decision variables. When using evolutionary algorithms to solve ... -
Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection
( Institute of Electrical and Electronics Engineers Inc. (IEEE) , 2025 , Article)This paper focuses on a significant yet challenging task: out-of-distribution detection (OOD detection), which aims to distinguish and reject test samples with semantic shifts, so as to prevent models trained on in-distribution ... -
Your data is not perfect: Towards cross-domain out-of-distribution detection in class-imbalanced data
( Elsevier , 2024 , Article)Out-of-distribution detection (OOD detection) aims to detect test samples drawn from a distribution that is different from the training distribution, in order to prevent models trained on in-distribution (ID) data from ... -
A single-objective Sequential Search Assistance-based Multi-Objective Algorithm Framework
( Elsevier , 2025 , Article)In recent years, multi-objective optimization has garnered significant attention from researchers. Evolutionary algorithms are proven to be highly effective in solving complex optimization problems in plenty of cases. ... -
Alzheimer’s disease diagnosis from MRI and SWI fused image using self adaptive differential evolutionary RVFL classifier
( Elsevier , 2025 , Article)Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that involves gradual memory loss and eventually leads to severe cognitive decline at the final stage. Advanced neuroimaging modalities, including magnetic ...
