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Optimal Path Finding With Beetle Antennae Search Algorithm by Using Ant Colony Optimization Initialization and Different Searching Strategies

Xiangyuan Jiang, Zongyuan Lin, Tianhao He, Xiaojing Ma, Sile Ma, Shuai Li Orcid Logo

IEEE Access, Volume: 8, Pages: 15459 - 15471

Swansea University Author: Shuai Li Orcid Logo

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DOI (Published version): 10.1109/access.2020.2965579

Abstract

Intelligent algorithm acts as one of the most important solutions to path planning problem. In order to solve the problems of poor real-time and low accuracy of the heuristic optimization algorithm in 3D path planning, this paper proposes a novel heuristic intelligent algorithm derived from the Beet...

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Published in: IEEE Access
ISSN: 2169-3536
Published: Institute of Electrical and Electronics Engineers (IEEE) 2020
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URI: https://cronfa.swan.ac.uk/Record/cronfa53741
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spelling 2020-10-27T13:52:41.0062003 v2 53741 2020-03-05 Optimal Path Finding With Beetle Antennae Search Algorithm by Using Ant Colony Optimization Initialization and Different Searching Strategies 42ff9eed09bcd109fbbe484a0f99a8a8 0000-0001-8316-5289 Shuai Li Shuai Li true false 2020-03-05 MECH Intelligent algorithm acts as one of the most important solutions to path planning problem. In order to solve the problems of poor real-time and low accuracy of the heuristic optimization algorithm in 3D path planning, this paper proposes a novel heuristic intelligent algorithm derived from the Beetle Antennae Search (BAS) algorithm. The algorithm proposed in this paper has the advantages of wide search range and high search accuracy, and can still maintain a low time complexity when multiple mechanisms are introduced. This paper combines the BAS algorithm with three non-trivial mechanisms proposed to solve the problems of low search efficiency and poor convergence accuracy in 3D path planning. The algorithm contains three non-trivial mechanisms, including local fast search, aco initial path generation, and searching information orientation. At first, local fast search mechanism presents a specific bounded area and add fast iterative exploration to speed up the convergence of path finding. Then aco initial path generation mechanism is initialized by Ant Colony Optimization (ACO) as a pruning basis. The initialization of the ACO algorithm can quickly obtain an effective path. Using the exploration trend of this path, the algorithm can quickly obtain a locally optimal path. Thirdly, searching information orientation mechanism is employed for BAS algorithm to guarantee the stability of the path finding, thereby avoiding blind exploration and reducing wasted computing resources. Simulation results show that the algorithm proposed in this paper has higher search accuracy and exploration speed than other intelligent algorithms, and improves the adaptability of the path planning algorithms in different environments. The effectiveness of the proposed algorithm is verified in simulation. Journal Article IEEE Access 8 15459 15471 Institute of Electrical and Electronics Engineers (IEEE) 2169-3536 Beetle antennae search, optimal path finding, bio-inspired optimization, search orientation 10 1 2020 2020-01-10 10.1109/access.2020.2965579 COLLEGE NANME Mechanical Engineering COLLEGE CODE MECH Swansea University 2020-10-27T13:52:41.0062003 2020-03-05T09:24:44.8990092 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering Xiangyuan Jiang 1 Zongyuan Lin 2 Tianhao He 3 Xiaojing Ma 4 Sile Ma 5 Shuai Li 0000-0001-8316-5289 6 53741__16774__ffc154b846ed431e89050ca33e921124.pdf jiang2020.pdf 2020-03-05T09:27:43.8413467 Output 14736238 application/pdf Version of Record true Released under the terms of a Creative Commons Attribution 4.0 License (CC-BY). true eng http://creativecommons.org/licenses/by/4.0/
title Optimal Path Finding With Beetle Antennae Search Algorithm by Using Ant Colony Optimization Initialization and Different Searching Strategies
spellingShingle Optimal Path Finding With Beetle Antennae Search Algorithm by Using Ant Colony Optimization Initialization and Different Searching Strategies
Shuai Li
title_short Optimal Path Finding With Beetle Antennae Search Algorithm by Using Ant Colony Optimization Initialization and Different Searching Strategies
title_full Optimal Path Finding With Beetle Antennae Search Algorithm by Using Ant Colony Optimization Initialization and Different Searching Strategies
title_fullStr Optimal Path Finding With Beetle Antennae Search Algorithm by Using Ant Colony Optimization Initialization and Different Searching Strategies
title_full_unstemmed Optimal Path Finding With Beetle Antennae Search Algorithm by Using Ant Colony Optimization Initialization and Different Searching Strategies
title_sort Optimal Path Finding With Beetle Antennae Search Algorithm by Using Ant Colony Optimization Initialization and Different Searching Strategies
author_id_str_mv 42ff9eed09bcd109fbbe484a0f99a8a8
author_id_fullname_str_mv 42ff9eed09bcd109fbbe484a0f99a8a8_***_Shuai Li
author Shuai Li
author2 Xiangyuan Jiang
Zongyuan Lin
Tianhao He
Xiaojing Ma
Sile Ma
Shuai Li
format Journal article
container_title IEEE Access
container_volume 8
container_start_page 15459
publishDate 2020
institution Swansea University
issn 2169-3536
doi_str_mv 10.1109/access.2020.2965579
publisher Institute of Electrical and Electronics Engineers (IEEE)
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering
document_store_str 1
active_str 0
description Intelligent algorithm acts as one of the most important solutions to path planning problem. In order to solve the problems of poor real-time and low accuracy of the heuristic optimization algorithm in 3D path planning, this paper proposes a novel heuristic intelligent algorithm derived from the Beetle Antennae Search (BAS) algorithm. The algorithm proposed in this paper has the advantages of wide search range and high search accuracy, and can still maintain a low time complexity when multiple mechanisms are introduced. This paper combines the BAS algorithm with three non-trivial mechanisms proposed to solve the problems of low search efficiency and poor convergence accuracy in 3D path planning. The algorithm contains three non-trivial mechanisms, including local fast search, aco initial path generation, and searching information orientation. At first, local fast search mechanism presents a specific bounded area and add fast iterative exploration to speed up the convergence of path finding. Then aco initial path generation mechanism is initialized by Ant Colony Optimization (ACO) as a pruning basis. The initialization of the ACO algorithm can quickly obtain an effective path. Using the exploration trend of this path, the algorithm can quickly obtain a locally optimal path. Thirdly, searching information orientation mechanism is employed for BAS algorithm to guarantee the stability of the path finding, thereby avoiding blind exploration and reducing wasted computing resources. Simulation results show that the algorithm proposed in this paper has higher search accuracy and exploration speed than other intelligent algorithms, and improves the adaptability of the path planning algorithms in different environments. The effectiveness of the proposed algorithm is verified in simulation.
published_date 2020-01-10T04:06:51Z
_version_ 1763753497973489664
score 11.036006

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