.png){kind=icon}
返回
Adaptive Multiobjective Particle Swarm Optimization Based on Evolutionary State Estimation
.png){kind=icon}
PRE
AI
DOI：10.1109/TCYB.2019.2949204.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
A rational leader selection strategy can enhance a swarm to manage the convergence and diversity during the entire search process. In this article, a novel adaptive multiobjective particle swarm optimization (MOPSO) is proposed on the basis of an evolutionary state estimation mechanism, which is used to detect the evolutionary environment whether in exploitation or exploration state. During the search process, different types of leaders, such as a convergence global best solution (c-gBest) and several diversity global best solutions (d-gBests), are to be selected from the external archive for particles under different evolutionary environments. The c-gBest is selected for improving the convergence when the swarm is in an exploitation state, while the d-gBests are chosen for enhancing the diversity in an exploration state. Furthermore, a modified archive maintenance strategy based on some predefined reference points is adopted to maximize the diversity of the Pareto solutions in the external archive. The experimental results demonstrate that the proposed algorithm performs significantly better than the several state-of-the-art multiobjective PSO algorithms and multiobjective evolutionary algorithms on 31 benchmark functions in terms of convergence and diversity of those obtained approximate Pareto fronts.
Keyword:
Convergence
Optimization
Particle swarm optimization
Maintenance engineering
State estimation
Approximation algorithms
Benchmark testing
Evolutionary state estimation (ESE)
leader selection strategy
multiobjective optimization
particle swarm optimization (PSO)
reference points-based archive maintenance strategy
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
10.5
论文数:
1.1W
被引数:
5.0W
机构
引用论文
.png){kind=icon}
分享收藏A two-stage R2 indicator based evolutionary algorithm for many-objective optimization一种基于两阶段R2指标的多目标优化进化算法
OAAI
OAAI分享收藏PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective OptimizationPlatEMO: 用于进化多目标优化的MATLAB平台OAAI
OAAI分享收藏Adaptive Multiobjective Particle Swarm Optimization Based on Parallel Cell Coordinate System基于并行单元坐标系的自适应多目标粒子群优化算法
PREAI
分享收藏Multiobjective evolutionary algorithms: A comparative case study and the Strength Pareto approach多目标进化算法: 比较案例研究和强度帕累托方法
PREAI
分享收藏
分享收藏
查看更多内容