摘要：Leak source localization is a very important topic that has receivedmuch research attention in recent years. The "Request and Reset" strategy is introduced here into the GC-PSO and D-PSO algorithms to improve the localization strategy. In the "Request and Reset" process, some of the low fitness particles are requested to be removed from their current group with their positions reset to assist the globally best particle, with those particles then combined into an "optimal group" to enhance the search around the globally best particle. Other two existing algorithms are improved by modifying the learning factor and inertia weight as comparison. Serval experiments are conducted by simulation to investigate the feature of the proposed algorithms, in which the impact of environmental size and population size as well as error adaptability are considered. Experimental results demonstrated the feasibility and advantage of the proposed approaches. MGC-PSO has the superior performance to the other methods in aspect of success rate and iteration time. (c) 2018 Elsevier B.V. All rights reserved.

关键字：Odor source Multi-robot Particle swarm optimization Request and Reset

ISSN号：0925-2312

卷、期、页：卷: 292 页: 82-90

发表日期：2018-05-31

期刊分区（SCI为中科院分区）：二区

收录情况：SCIE（科学引文索引网络版）,ESI（基本科学指标数据库）,EI（工程索引）

发表期刊名称：NEUROCOMPUTING

参与作者：晏玉婷,张儒,李俊明

第一作者：王吉

论文类型：期刊论文

论文概要：晏玉婷,张儒,王吉,李俊明，Modified PSO algorithms with "Request and Reset" for leak source localization using multiple robots，NEUROCOMPUTING，2018，卷: 292 页: 82-90

论文题目：Modified PSO algorithms with "Request and Reset" for leak source localization using multiple robots