Browsing by Author "Zheng, Jinhua"
Now showing items 1-14 of 14
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An adaptation reference-point-based multiobjective evolutionary algorithm
Zou, Juan; Fu, Liuwei; Yang, Shengxiang; Zheng, Jinhua; Ruan, Gan; Pei, Tingrui; Wang, Lei (Article)It is well known that maintaining a good balance between convergence and diversity is crucial to the performance of multiobjective optimization algorithms (MOEAs). However, the Pareto front (PF) of multiobjective optimization ... -
Adaptive neighborhood selection for many-objective optimization problems
Zou, Juan; Zhang, Yuping; Yang, Shengxiang; Liu, Yuan; Zheng, Jinhua (Article)It is generally accepted that conflicts between convergence and distribution deteriorate with an increase in the number of objectives. Furthermore, Pareto dominance loses its effectiveness in many-objectives optimization ... -
A dynamic multiobjective evolutionary algorithm based on a dynamic evolutionary environment model
Zou, Juan; Li, Qingya; Yang, Shengxiang; Zheng, Jinhua; Peng, Zhou; Pei, Tingrui (Article)Traditional dynamic multiobjective evolutionary algorithms usually imitate the evolution of nature, maintaining diversity of population through different strategies and making the population track the Pareto optimal solution ... -
The effect of diversity maintenance on prediction in dynamic multi-objective optimization
Ruan, Gan; Yu, Guo; Zheng, Jinhua; Zou, Juan; Yang, Shengxiang (Article)There are many dynamic multi-objective optimization problems (DMOPs) in real-life engineering applications whose objectives change over time. After an environmental change occurs, prediction strategies are commonly used ... -
ETEA: A Euclidean minimum spanning tree-based evolutionary algorithm for multiobjective optimization
Li, Miqing; Yang, Shengxiang; Zheng, Jinhua; Liu, Xiaohui (Article)The Euclidean minimum spanning tree (EMST), widely used in a variety of domains, is a minimum spanning tree of a set of points in space where the edge weight between each pair of points is their Euclidean distance. Since ... -
An evolutionary dynamic multi-objective optimization algorithm based on center-point prediction and sub-population autonomous guidance
Zhou, Jianwei; Zou, Juan; Yang, Shengxiang; Ruan, Gan; Ou, Junwei; Zheng, Jinhua (Conference)Dynamic multi-objective optimization problems (DMOPs) provide a challenge in that objectives conflict each other and change over time. In this paper, a hybrid approach based on prediction and autonomous guidance is proposed, ... -
A knee-point-based evolutionary algorithm using weighted subpopulation for many-objective optimization
Zou, Juan; Ji, Chunhui; Yang, Shengxiang; Zhang, Yuping; Zheng, Jinhua; Li, Ke (Article)Among many-objective optimization problems (MaOPs), the proportion of nondominated solutions is too large to distinguish among different solutions, which is a great obstacle in the process of solving MaOPs. Thus, this paper ... -
A many-objective evolutionary algorithm based on rotated grid
Zou, Juan; Fu, Liuwei; Yang, Shengxiang; Zheng, Jinhua; Yu, Guo; Hu, Yaru (Article)Evolutionary optimization algorithms, a meta-heuristic approach, often encounter considerable challenges in many-objective optimization problems (MaOPs). The Pareto-based dominance loses its effectiveness in MaOPs, which ... -
A Pareto-based evolutionary algorithm using decomposition and truncation for dynamic multi-objective optimization
Ou, Junwei; Zheng, Jinhua; Ruan, Gan; Hu, Yaru; Zou, Juan; Li, Miqing; Yang, Shengxiang; Tan, Xu (Article)Maintaining a balance between convergence and diversity of the population in the objective space has been widely recognized as the main challenge when solving problems with two or more conflicting objectives. This is added ... -
A Pareto-based many-objective evolutionary algorithm using space partitioning selection and angle-based truncation
Bai, Hui; Zheng, Jinhua; Yu, Guo; Yang, Shengxiang; Zou, Juan (Article)Evolutionary algorithms (EAs) have shown to be efficient in dealing with many-objective optimization problems (MaOPs) due to their ability to obtain a set of compromising solutions which not only converge toward the Pareto ... -
A performance indicator for reference-point-based multiobjective evolutionary optimization
Hou, Zhanglu; Yang, Shengxiang; Zou, Juan; Zheng, Jinhua; Yu, Guo; Ruan, Gan (Conference)Aiming at the difficulty in evaluating preference-based evolutionary multiobjective optimization, this paper proposes a new performance indicator. The main idea is to project the preferred solutions onto a constructed ... -
A prediction strategy based on center points and knee points for evolutionary dynamic multi-objective optimization
Zou, Juan; Li, Qingya; Yang, Shengxiang; Bai, Hui; Zheng, Jinhua (Article)In real life, there are many dynamic multi-objective optimization problems which vary over time, requiring an optimization algorithm to track the movement of the Pareto front (Pareto set) with time. In this paper, we propose ... -
A predictive strategy based on special points for evolutionary dynamic multi-objective optimization
Li, Qingya; Zou, Juan; Yang, Shengxiang; Zheng, Jinhua; Ruan, Gan (Article)There are some real-world problems in which multiple objectives conflict with each other and the objectives change with time. These problems require an optimization algorithm to track the moving Pareto front or Pareto set ... -
A proportion-based selection scheme for multi-objective optimization
Fu, Liuwei; Zou, Juan; Yang, Shengxiang; Ruan, Gan; Zheng, Jinhua; Ma, Zhongwei (Conference)Classical multi-objective evolutionary algorithms (MOEAs) have been proven to be inefficient for solving multiobjective optimizations problems when the number of objectives increases due to the lack of sufficient selection ...