Resource allocation in 5G IoV architecture based on SDN and fog-cloud computing
B Cao, Z Sun, J Zhang, Y Gu - IEEE transactions on intelligent …, 2021 - ieeexplore.ieee.org
In the traditional cloud-based Internet of Vehicles (IoV) architecture, it is difficult to guarantee
the low latency requirements of the current intelligent transportation system (ITS). As a …
the low latency requirements of the current intelligent transportation system (ITS). As a …
A survey of evolutionary algorithms for multi-objective optimization problems with irregular Pareto fronts
Evolutionary algorithms have been shown to be very successful in solving multi-objective
optimization problems (MOPs). However, their performance often deteriorates when solving …
optimization problems (MOPs). However, their performance often deteriorates when solving …
Learning to optimize: reference vector reinforcement learning adaption to constrained many-objective optimization of industrial copper burdening system
The performance of decomposition-based algorithms is sensitive to the Pareto front shapes
since their reference vectors preset in advance are not always adaptable to various problem …
since their reference vectors preset in advance are not always adaptable to various problem …
Multi-objective feature selection with missing data in classification
Y Xue, Y Tang, X Xu, J Liang… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Feature selection (FS) is an important research topic in machine learning. Usually, FS is
modelled as a bi-objective optimization problem whose objectives are: 1) classification …
modelled as a bi-objective optimization problem whose objectives are: 1) classification …
An adaptive reference vector-guided evolutionary algorithm using growing neural gas for many-objective optimization of irregular problems
Most reference vector-based decomposition algorithms for solving multiobjective
optimization problems may not be well suited for solving problems with irregular Pareto …
optimization problems may not be well suited for solving problems with irregular Pareto …
Multimodal multiobjective evolutionary optimization with dual clustering in decision and objective spaces
This article suggests a multimodal multiobjective evolutionary algorithm with dual clustering
in decision and objective spaces. One clustering is run in decision space to gather nearby …
in decision and objective spaces. One clustering is run in decision space to gather nearby …
Grid search based multi-population particle swarm optimization algorithm for multimodal multi-objective optimization
In the multimodal multi-objective optimization problems (MMOPs), there may exist two or
multiple equivalent Pareto optimal sets (PS) with the same Pareto Front (PF). The difficulty of …
multiple equivalent Pareto optimal sets (PS) with the same Pareto Front (PF). The difficulty of …
A fuzzy decision variables framework for large-scale multiobjective optimization
In large-scale multiobjective optimization, too many decision variables hinder the
convergence search of evolutionary algorithms. Reducing the search range of the decision …
convergence search of evolutionary algorithms. Reducing the search range of the decision …
Many-objective job-shop scheduling: A multiple populations for multiple objectives-based genetic algorithm approach
The job-shop scheduling problem (JSSP) is a challenging scheduling and optimization
problem in the industry and engineering, which relates to the work efficiency and operational …
problem in the industry and engineering, which relates to the work efficiency and operational …
A survey on learnable evolutionary algorithms for scalable multiobjective optimization
Recent decades have witnessed great advancements in multiobjective evolutionary
algorithms (MOEAs) for multiobjective optimization problems (MOPs). However, these …
algorithms (MOEAs) for multiobjective optimization problems (MOPs). However, these …