Xiao-Zhi Gao

Professor

University of Eastern Finland

Finland

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Professor Xiao-Zhi Gao is a faculty member at the University of Eastern Finland. His research encompasses multiobjective optimization, evolutionary algorithms, and artificial intelligence applications in cloud computing and IoT security. Recent publications include work on product design changes, load forecasting, and UAV path planning. He contributes significantly to the development of computational techniques addressing complex constraints and enhancing optimization processes.

Xiao-Zhi Gao

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Articles (19)

Handling Multiobjective Optimization Problems With Complex Constraints: A Constraints Grouping-Based Approach

SHERA: SHAP-Enhanced Resource Allocation for VM Scheduling and Efficient Cloud Computing

Two-Stage Cooperation Multiobjective Evolutionary Algorithm Guided by Constraint-Sensitive Variables

Image Retrieval Using Multilayer Feature Aggregation Histogram

Guest Editorial of the Special Section on Neural Computing-Driven Artificial Intelligence for Consumer Electronics

A Survey of IoT Privacy Security: Architecture, Technology, Challenges, and Trends

An Interval Multi-Objective Evolutionary Generation Algorithm for Product Design Change Plans in Uncertain Environments

Enhancing Individual UAV Path Planning With Parallel Multi-Swarm Treatment Coronavirus Herd Immunity Optimizer (PMST-CHIO) Algorithm

Sleep-induced Network with Reducing Information Loss for Short-term Load Forecasting

Human-cognition-inspired deep model with its application to ocean wave height forecasting

Objective-Constraint Mutual-Guided Surrogate-Based Particle Swarm Optimization for Expensive Constrained Multimodal Problems

Q-Learning-Based Hyperheuristic Evolutionary Algorithm for Dynamic Task Allocation of Crowdsensing

Trajectory Optimization of High-Speed Robotic Positioning with Suppressed Motion Jerk via Improved Chicken Swarm Algorithm

A Spider Monkey Optimization Based on Beta-Hill Climbing Optimizer for Unmanned Combat Aerial Vehicle (UCAV)

An Efficient Few-Shot Object Detection Method for Railway Intrusion via Fine-Tune Approach and Contrastive Learning

Surrogate and Autoencoder-Assisted Multitask Particle Swarm Optimization for High-Dimensional Expensive Multimodal Problems

A Stable Lightweight and Adaptive Feature Enhanced Convolution Neural Network for Efficient Railway Transit Object Detection

A Fast Hybrid Feature Selection Based on Correlation-Guided Clustering and Particle Swarm Optimization for High-Dimensional Data

An Immersive Human-Robot Interactive Game Framework Based on Deep Learning for Children’s Concentration Training

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Nadhira Khezami

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