97 citations · 287 across the 26 of their papers we have counts for
20 papers · 1 filter
Multi-Objective Archiving
Miqing Li, Manuel López-Ibáñez, Xin Yao
Most multi-objective optimisation algorithms maintain an archive explicitly or implicitly during their search. Such an archive can be solely used to store high-quality solutions pr…
How Good Is Neural Combinatorial Optimization? A Systematic Evaluation on the Traveling Salesman Problem
Shengcai Liu, Yu Zhang, Ke Tang +1
Traditional solvers for tackling combinatorial optimization (CO) problems are usually designed by human experts. Recently, there has been a surge of interest in utilizing deep lear…
An Efficient Multi-Indicator and Many-Objective Optimization Algorithm based on Two-Archive
Ziming Wang, Xin Yao
Indicator-based algorithms are gaining prominence as traditional multi-objective optimization algorithms based on domination and decomposition struggle to solve many-objective opti…
Evolutionary Optimization for Proactive and Dynamic Computing Resource Allocation in Open Radio Access Network
Gan Ruan, Leandro L. Minku, Zhao Xu +1
Intelligent techniques are urged to achieve automatic allocation of the computing resource in Open Radio Access Network (O-RAN), to save computing resource, increase utilization ra…
Memetic Search for Vehicle Routing with Simultaneous Pickup-Delivery and Time Windows
Shengcai Liu, Ke Tang, Xin Yao
The Vehicle Routing Problem with Simultaneous Pickup-Delivery and Time Windows (VRPSPDTW) has attracted much research interest in the last decade, due to its wide application in mo…
Few-shots Parallel Algorithm Portfolio Construction via Co-evolution
Ke Tang, Shengcai Liu, Peng Yang +1
Generalization, i.e., the ability of solving problem instances that are not available during the system design and development phase, is a critical goal for intelligent systems. A…
A Hybrid Evolutionary Algorithm for Reliable Facility Location Problem
Han Zhang, Jialin Liu, Xin Yao
The reliable facility location problem (RFLP) is an important research topic of operational research and plays a vital role in the decision-making and management of modern supply c…
Competitive Coevolution as an Adversarial Approach to Dynamic Optimization
Xiaofen Lu, Ke Tang, Stefan Menzel +1
Dynamic optimization, for which the objective functions change over time, has attracted intensive investigations due to the inherent uncertainty associated with many real-world pro…
Algorithm Portfolio for Individual-based Surrogate-Assisted Evolutionary Algorithms
Hao Tong, Jialin Liu, Xin Yao
Surrogate-assisted evolutionary algorithms (SAEAs) are powerful optimisation tools for computationally expensive problems (CEPs). However, a randomly selected algorithm may fail in…
Voronoi-based Efficient Surrogate-assisted Evolutionary Algorithm for Very Expensive Problems
Hao Tong, Changwu Huang, Jialin Liu +1
Very expensive problems are very common in practical system that one fitness evaluation costs several hours or even days. Surrogate assisted evolutionary algorithms (SAEAs) have be…
A Parallel Divide-and-Conquer based Evolutionary Algorithm for Large-scale Optimization
Peng Yang, Ke Tang, Xin Yao
Large-scale optimization problems that involve thousands of decision variables have extensively arisen from various industrial areas. As a powerful optimization tool for many real-…
Analysis of Noisy Evolutionary Optimization When Sampling Fails
Chao Qian, Chao Bian, Yang Yu +2
In noisy evolutionary optimization, sampling is a common strategy to deal with noise. By the sampling strategy, the fitness of a solution is evaluated multiple times (called \emph{…
Multiobjective Test Problems with Degenerate Pareto Fronts
Liangli Zhen, Miqing Li, Ran Cheng +2
In multiobjective optimisation, a set of scalable test problems with a variety of features allow researchers to investigate and evaluate the abilities of different optimisation alg…
Interactive Decomposition Multi-Objective Optimization via Progressively Learned Value Functions
Ke Li, Renzhi Chen, Dragan Savic +1
Decomposition has become an increasingly popular technique for evolutionary multi-objective optimization (EMO). A decomposition-based EMO algorithm is usually designed to approxima…
Maximizing Submodular or Monotone Approximately Submodular Functions by Multi-objective Evolutionary Algorithms
Chao Qian, Yang Yu, Ke Tang +2
Evolutionary algorithms (EAs) are a kind of nature-inspired general-purpose optimization algorithm, and have shown empirically good performance in solving various real-word optimiz…
What Weights Work for You? Adapting Weights for Any Pareto Front Shape in Decomposition-based Evolutionary Multi-Objective Optimisation
Miqing Li, Xin Yao
The quality of solution sets generated by decomposition-based evolutionary multiobjective optimisation (EMO) algorithms depends heavily on the consistency between a given problem's…
Experience-based Optimization: A Coevolutionary Approach
Shengcai Liu, Ke Tang, Xin Yao
This paper studies improving solvers based on their past solving experiences, and focuses on improving solvers by offline training. Specifically, the key issues of offline training…
Dominance Move: A Measure of Comparing Solution Sets in Multiobjective Optimization
Miqing Li, Xin Yao
One of the most common approaches for multiobjective optimization is to generate a solution set that well approximates the whole Pareto-optimal frontier to facilitate the later dec…
Negatively Correlated Search
Ke Tang, Peng Yang, Xin Yao
Evolutionary Algorithms (EAs) have been shown to be powerful tools for complex optimization problems, which are ubiquitous in both communication and big data analytics. This paper…
Convex Hull-Based Multi-objective Genetic Programming for Maximizing ROC Performance
Pu Wang, Michael Emmerich, Rui Li +3
ROC is usually used to analyze the performance of classifiers in data mining. ROC convex hull (ROCCH) is the least convex major-ant (LCM) of the empirical ROC curve, and covers pot…