Three-Dimensional Route Planning Based on the Beetle Swarm Optimization Algorithm

Abstract

Swarm intelligence algorithms have better intelligence and adaptation compared with the traditional route planning method. A three-dimensional route planning method based on the beetle swarm optimization (BSO) algorithm was proposed. The iterative updating strategy of the BSO algorithm cooperated with the search mechanism of the beetle monomer and the updating strategy of the particle swarm optimization (PSO) algorithm, thus accelerating iterative convergence and decreasing the probability of trapping in the local optimal solution of the algorithm. The practical engineering problem of three-dimensional route planning was addressed by processing uneven ground barriers using the penalty function, and a smooth route is gained from cubic spline interpolation. In this study, a three-dimensional environmental model was constructed by using actual elevation data from the USGS/NASA SRTM, and a simulation experiment of three-dimensional route planning was performed using the proposed method. The proposed method was compared with other algorithms. Experimental results demonstrated that when the iteration time was set to 50, the route planning length based on BSO algorithm was about 90% of the route planning based on the PSO algorithm. Moreover, the proposed route planning method had high convergence rate and stable convergence result and is applicable to three-dimensional route planning.INDEX TERMS Beetle swarm optimization (BSO), particle swarm optimization (PSO), route planning.

A Hardware Descriptive Approach to Beetle Antennae Search

“…where move_x1 denotes the beetle moving distance along the horizontal direction, and move_x2 denotes the beetle moving distance along the vertical direction. According to (16), (17), (18) and (19), the position updates for the beetle are implemented in Verilog HDL as follows:…”

“…In addition, BAS is applied to the design of power system and the construction of stock investment model [9], [14], [16], [17]. Mu et al [18] used the BAS algorithm in 3D path planning. Fan et al [21] applied the algorithm to an electro-hydraulic position servo control system.…”

A Distributed Multi-Agent Dynamic Area Coverage Algorithm Based on Reinforcement Learning

“…At present, there exist some process optimization methods, including ant colony optimization (ACO) [20], particle swarm optimization (PCO) [21] and model predictive control (MPC) [22]. These methods can optimize the motion path of each agent in the MAS.…”

Motion Planning of Redundant Manipulator With Variable Joint Velocity Limit Based on Beetle Antennae Search Algorithm

“…The beetle antennae search (BAS) algorithm is a new type of computational intelligence algorithms which is applied in scientific and engineering applications, e.g., geomechanics analysis [22], network training [23], controller parameter tuning in servo system [24], unmanned aerial vehicle path planning [25], route planning [26], pattern classification [23], power system [27], ship predictive collision avoidance [28], bridge sensor placement [29], and investment portfolio problems [30]. Because of the excellent nonlinear optimization ability, BAS can be regarded as a potential efficient optimizer in robot applications.…”