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

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 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. INDEX TERMS Beetle antennae search, optimal path finding, bio-inspired optimization, search orientation.

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“…The beetle will sense the odor by its two antennas. When the distance between the beetle and the food location changes, the concentration of the smell also changes [47]. By continuously moving and searching for the odor concentration, the beetle can finally arrive at the point of the source.…”

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“…The optimal control parameters can be quickly determined in the control algorithm, and the heading angle control under the optimal parameters can be realized. Jiang applied the BAS algorithm and its improved algorithm to 3D path planning research [60]. According to the size of the adaptive step size, BAS can effectively jump out of the local optimal value in the early stage of exploration and quickly converge at the end of the search.…”

An Improved Beetle Antennae Search Algorithm Based on the Elite Selection Mechanism and the Neighbor Mobility Strategy for Global Optimization Problems

“…Literature [34] devised a new fallback BAS for path planning. Jiang et al combined BAS with non-trivial mechanisms to solve the 3-D path planning problem [35]. Shuo Xie et al proposed an improved Q-learning BAS to solve the model predictive ship collision avoidance [36].…”