Solving path planning problem based on logistic beetle algorithm search–pigeon‐inspired optimisation algorithm. Issue 21 (14th September 2020)

• Record Type: Journal Article
• Authors: Liu, Ang; Jiang, Jin
• Is Part Of: Electronics letters. Volume 56:Issue 21(2020)
• Page Start: 1105
• Page End: 1108
• Publication Date: 2020-09-14
• Subjects: mobile robots -- swarm intelligence -- search problems -- path planning -- particle swarm optimisation -- multi‐robot systems; logistic beetle algorithm -- search–pigeon‐inspired optimisation algorithm -- mobile robot path planning -- swarm intelligence optimisation algorithm -- multiobjective optimisation problems -- local optimisation -- search–PIO algorithm -- optimisation process -- LBAS‐PIO algorithm -- optimal path planning; Electronics -- Periodicals; 621.381
• DOI: 10.1049/el.2020.1895
• Languages: English
• ISSNs: 0013-5194

Abstract

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.