NDLI: Solving path planning problem based on logistic beetle algorithm search

Content Provider	IET Digital Library
Author	Liu, Ang Jiang, Jin
Abstract	Mobile robot path planning is an important part of the mobile robot field. The swarm intelligence optimisation algorithm has certain advantages in solving such multi-objective optimisation problems, so this Letter proposes to apply the pigeon-inspired optimisation (PIO) algorithm to the path planning problem. However, the traditional PIO algorithm is suffering from the problems of easy to get into local optimisation, low stability and premature convergence. To overcome its shortcomings, this Letter proposes a logistic beetle algorithm search–PIO (LBAS-PIO) algorithm. In the optimisation process of the LBAS-PIO algorithm, the PIO algorithm is initialised by the logistic mapping, making the search space more extensive. Learning from the idea of the beetle algorithm search, each pigeon has its own judgment of the environment and the number of iterations and search time are reduced. Further, the path evaluation function of the algorithm and the execution order of the operators are optimised to make the algorithm smoother and easier to find the global optimal solution. Simulation and experimental results illustrated the superiority of the LBAS-PIO algorithm and the LBAS-PIO algorithm better meets the needs of mobile robots for the optimal path planning.
Starting Page	1105
Ending Page	1108
Page Count	4
ISSN	00135194
Volume Number	56
e-ISSN	1350911X
Issue Number	Issue 21, Oct (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/el/56/21
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/el.2020.1895
Journal	Electronics Letters
Publisher Date	2020-09-14
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Combinatorial Mathematics LBAS-PIO Algorithm Local Optimisation Logistic Beetle Algorithm Mobile Robot Path Planning Mobile Robots Multi-robot System Multiobjective Optimisation Problem Optimal Path Planning Optimisation Process Optimisation Technique Particle Swarm Optimisation Path Planning Search Problem Search–pigeon-inspired Optimisation Algorithm Search–PIO Algorithm Spatial Variables Control Swarm Intelligence Swarm Intelligence Optimisation Algorithm
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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