309 citations · 749 across the 34 of their papers we have counts for
14 papers · 1 filter
Approximation of a Pareto Set Segment Using a Linear Model with Sharing Variables
Ping Guo, Qingfu Zhang, Xi Lin
In many real-world applications, the Pareto Set (PS) of a continuous multiobjective optimization problem can be a piecewise continuous manifold. A decision maker may want to find a…
Pareto Set Learning for Expensive Multi-Objective Optimization
Xi Lin, Zhiyuan Yang, Xiaoyuan Zhang +1
Expensive multi-objective optimization problems can be found in many real-world applications, where their objective function evaluations involve expensive computations or physical…
Graph Neural Network Encoding for Community Detection in Attribute Networks
Jianyong Sun, Wei Zheng, Qingfu Zhang +1
In this paper, we first propose a graph neural network encoding method for multiobjective evolutionary algorithm to handle the community detection problem in complex attribute netw…
On the Combined Impact of Population Size and Sub-problem Selection in MOEA/D
Geoffrey Pruvost, Bilel Derbel, Arnaud Liefooghe +2
This paper intends to understand and to improve the working principle of decomposition-based multi-objective evolutionary algorithms. We review the design of the well-established M…
Multi-objectivization Inspired Metaheuristics for the Sum-of-the-Parts Combinatorial Optimization Problems
Jialong Shi, Jianyong Sun, Qingfu Zhang
Multi-objectivization is a term used to describe strategies developed for optimizing single-objective problems by multi-objective algorithms. This paper focuses on multi-objectiviz…
Homotopic Convex Transformation: A New Landscape Smoothing Method for the Traveling Salesman Problem
Jialong Shi, Jianyong Sun, Qingfu Zhang +1
This paper proposes a novel landscape smoothing method for the symmetric Traveling Salesman Problem (TSP). We first define the Homotopic Convex (HC) transformation of a TSP as a co…
A Batched Scalable Multi-Objective Bayesian Optimization Algorithm
Xi Lin, Hui-Ling Zhen, Zhenhua Li +2
The surrogate-assisted optimization algorithm is a promising approach for solving expensive multi-objective optimization problems. However, most existing surrogate-assisted multi-o…
A Simple Yet Efficient Rank One Update for Covariance Matrix Adaptation
Zhenhua Li, Qingfu Zhang
In this paper, we propose an efficient approximated rank one update for covariance matrix adaptation evolution strategy (CMA-ES). It makes use of two evolution paths as simple as t…
Push and Pull Search for Solving Constrained Multi-objective Optimization Problems
Zhun Fan, Wenji Li, Xinye Cai +5
This paper proposes a push and pull search (PPS) framework for solving constrained multi-objective optimization problems (CMOPs). To be more specific, the proposed PPS divides the…
Evolutionary Multitasking for Multiobjective Continuous Optimization: Benchmark Problems, Performance Metrics and Baseline Results
Yuan Yuan, Yew-Soon Ong, Liang Feng +7
In this report, we suggest nine test problems for multi-task multi-objective optimization (MTMOO), each of which consists of two multiobjective optimization tasks that need to be s…
Evolutionary Many-Objective Optimization Based on Adversarial Decomposition
Mengyuan Wu, Ke Li, Sam Kwong +1
The decomposition-based method has been recognized as a major approach for multi-objective optimization. It decomposes a multi-objective optimization problem into several single-ob…
Difficulty Adjustable and Scalable Constrained Multi-objective Test Problem Toolkit
Zhun Fan, Wenji Li, Xinye Cai +5
Multi-objective evolutionary algorithms (MOEAs) have progressed significantly in recent decades, but most of them are designed to solve unconstrained multi-objective optimization p…
Matching-Based Selection with Incomplete Lists for Decomposition Multi-Objective Optimization
Mengyuan Wu, Ke Li, Sam Kwong +2
The balance between convergence and diversity is a key issue of evolutionary multi-objective optimization. The recently proposed stable matching-based selection provides a new pers…
Learning from Non-Stationary Stream Data in Multiobjective Evolutionary Algorithm
Jianyong Sun, Hu Zhang, Aimin Zhou +1
Evolutionary algorithms (EAs) have been well acknowledged as a promising paradigm for solving optimisation problems with multiple conflicting objectives in the sense that they are…