.png){kind=icon}
返回
Population statistics for particle swarm optimization: Single-evaluation methods in noisy optimization problems
.png){kind=icon}
PRE
AI
DOI：10.1007/s00500-014-1438-y.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Particle swarm optimization (PSO) is a meta-heuristic whose quality of results deteriorates significantly in optimization problems subject to noise. The underlying reason to such a deterioration is that the effect of noise hinders the ability of particles to distinguish good from bad solutions, leading them to suffer from deception, blindness and disorientation. Adeceived particle is not partially attracted to the true best solution in its neighborhood, a blinded particle misses an opportunity to improve upon its personal best solution, and a disoriented particle mistakenly prefers a worse solution. These conditions need to be addressed via noise mitigation mechanisms to prevent (or at least reduce) such a deterioration. Single-evaluation methods are the name by which we refer to PSO algorithms that address the effect of noise without performing additional function evaluations. The first of these algorithms was PSO with evaporation (PSO-E), which was proposed to reduce blindness in the swarms, and reports have suggested that it succeeds at finding better solutions than the regular PSO in different stochastic and dynamic optimization problems. However, PSO-E depends on an evaporation factor whose value is determined empirically, and the swarm is always at risk of exhibiting divergent behaviour. In this article, we propose a method to determine a priori the evaporation factor for PSO-E, and we also propose a new PSO with probabilistic updates (PSO-PU) to prevent the risk of divergence. Additionally, we take a different approach and develop a new PSO with average neighborhoods (PSO-AN) to blur the effect of noise and thereby reduce deception. Experiments on 20 large-scale benchmark functions subject to different levels of noise show that the regular PSO (lacking a noise mitigation mechanism) generally finds better solutions than PSO-E and PSO-PU because their approaches cause too much disorientation. However, PSO-AN finds better solutions than the regular PSO thanks to the improved quality of its neighborhood best solutions that partially attract the swarm towards better regions of the search space.
Keyword:
Particle swarm optimization
Population statistics
Noisy optimization problems
Single-evaluation methods
Evaporation mechanism
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:
2.5
论文数:
1.0W
被引数:
2.1W
机构
引用论文
Short course of high-dose steroids for anaphylaxis caused flare up of tuberculosis: A case report短期大剂量糖皮质激素治疗过敏性休克导致结核病复发：一份病例报告
OAAI
OAAI分享收藏Use of an enriched inoculum for determination of biodegradable dissolved organic carbon (BDOC) in drinking water.png)
Water Supply
Water SupplyIF0
PREAI
分享收藏Observation of continuum radiations from the Cluster fleet: first results from direction finding
OAAI
OAAI分享收藏Progression of hepatic aspergillosis following second renal transplantation in a patient with recurrent glomerulonephritis肝曲霉病进展：一例复发性肾小球肾炎患者在第二次肾移植后的情况OAAI
OAAI分享收藏
查看更多内容