A Novel Swarm Intelligence Algorithm with a Parasitism-Relation-Based Structure for Mobile Robot Path Planning.

Hui Ren, Luli Gao, Xiaochen Shen, Mengnan Li, Wei Jiang

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed

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2 citing papers in PubMed.

Transmission Line-Planning Method Based on Adaptive Resolution Grid and Improved Dijkstra Algorithm.Sensors (Basel, Switzerland) · 2023
Article
Study on Multi-Mode Switching Control Strategy of Active Suspension Based on Road Estimation.Sensors (Basel, Switzerland) · 2023
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5 authors.

Hui RenSchool of Information and Communication Engineering, Communication University of China, No.1 Dingfuzhuang East Street, Chaoyang District, Beijing 100024, China.

Luli GaoSchool of Information and Communication Engineering, Communication University of China, No.1 Dingfuzhuang East Street, Chaoyang District, Beijing 100024, China.ORCID 0000-0003-4207-3382

Xiaochen ShenSchool of Information and Communication Engineering, Communication University of China, No.1 Dingfuzhuang East Street, Chaoyang District, Beijing 100024, China.ORCID 0000-0001-7058-0512

Mengnan LiSchool of Information and Communication Engineering, Communication University of China, No.1 Dingfuzhuang East Street, Chaoyang District, Beijing 100024, China.

Wei JiangSchool of Information and Communication Engineering, Communication University of China, No.1 Dingfuzhuang East Street, Chaoyang District, Beijing 100024, China.

Funding

the National Key Research and Development Funding 2018YFB1403703

6 · The paper itself

Abstract

A multi-swarm-evolutionary structure based on the parasitic relationship in the biosphere is proposed in this paper and, according to the conception, the Para-PSO-ABC algorithm (ParaPA), combined with merits of the modified particle swarm optimization (MPSO) and artificial bee colony algorithm (ABC), is conducted with the multimodal routing strategy to enhance the safety and the cost issue for the mobile robot path planning problem. The evolution is divided into three stages, where the first is the independent evolutionary stage, with the same evolution strategies for each swarm. The second is the fusion stage, in which individuals are evolved hierarchically in the parasitism structure. Finally, in the interaction stage, a multi-swarm-elite strategy is used to filter the information through a predefined cross function among swarms. Meanwhile, the segment obstacle-avoiding strategy is proposed to accelerate the searching speed with two fitness functions. The best path is selected according to the performance on the safety and consumption issues. The introduced algorithm is examined with different obstacle allocations and simulated in the real routing environment compared with some typical algorithms. The results verify the productiveness of the parasitism-relation-based structure and the stage-based evolution strategy in path planning.

Indexed as

dual-community-evolutionary structuremulti-swarm evolutionPara-PSO-ABC algorithmparasitic relationshippath planning

Identifiers

PMID36850351

DOI10.3390/s23041751

PMCPMC9960856

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