J
Jianwei Zhao
Researcher at Hebei University of Technology
Publications - 27
Citations - 1380
Jianwei Zhao is an academic researcher from Hebei University of Technology. The author has contributed to research in topics: Evolutionary algorithm & Optimization problem. The author has an hindex of 13, co-authored 23 publications receiving 661 citations. Previous affiliations of Jianwei Zhao include Chinese Ministry of Education.
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Multiobjective Evolution of Fuzzy Rough Neural Network via Distributed Parallelism for Stock Prediction
TL;DR: Modifications to the existing models of fuzzy rough neural network are proposed and a powerful evolutionary framework for fuzzyrough neural networks is developed by inheriting the merits of both the merits and the objectives of prediction precision and network simplicity are considered.
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Applying graph-based differential grouping for multiobjective large-scale optimization
TL;DR: This paper proposes multiobjective graph-based differential grouping with shift (mogDG-shift) to decompose the large number of variables in an MOLSOP, and combines the algorithms together with the original algorithms to show improved optimization performance.
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Security-Aware Industrial Wireless Sensor Network Deployment Optimization
TL;DR: This article simultaneously considers the security, lifetime, and coverage issues by deploying sensor nodes and relay nodes in an industrial environment to analyze the multipath routing for enhancing security and proposes enhanced distributed parallel algorithms that outperform their counterparts.
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Quantum-enhanced multiobjective large-scale optimization via parallelism
TL;DR: Verifications performed on several test suites indicate that the proposed quantum-enhanced algorithms are superior to the state-of-the-art algorithms in terms of both effectiveness and efficiency.
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Large-Scale Many-Objective Deployment Optimization of Edge Servers
TL;DR: The placement problem of ESs in the IoV is studied, and the six-objective ES deployment optimization model is constructed by simultaneously considering transmission delay, workload balancing, energy consumption, deployment costs, network reliability, and ES quantity.