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Beaver Optimization Algorithm: A New Bio-inspired Optimizer for Mobile Robot Path Planning Problems
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AI
DOI：10.1007/s10846-026-02407-8.png)
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Abstract
En 中文
Numerous bio-heuristic algorithms have been developed, each with unique characteristics. This paper introduces a novel bionic algorithm, the Beaver Optimization Algorithm (BOA), inspired by the adaptive behaviors of beavers in their natural habitats. Beavers exhibit distinct flexibility and efficiency in feeding, reproduction, and evading predators, which the authors have observed and modeled in the BOA. The BOA is designed to address the path planning problem of mobile robots, drawing from beavers’ dynamic behaviors of beavers like den construction and food foraging in aquatic environments. The algorithm mathematically simulates these behaviors, incorporating elements such as den migration, localized foraging, and random hiding strategies. These simulated behaviors are translated into mechanism that offer feasible solutions for optimization problems. To evaluate the BOA’s efficacy, the authors conducted rigorous testing against 23 benchmark functions, comparing its performance with other prominent nature-inspired algorithms, including particle swarm optimization algorithms, genetic algorithms and so on. The results indicate that the BOA outperforms many of these algorithms on the majority of benchmark functions. Building on the BOA, the paper also presents a strategy for handling multi-objective optimization, leading to the development Multi-Objective Beaver Optimization Algorithm (MOBOA). This extended algorithm was tested on multi-objective benchmark functions and compared against four other multi-objective algorithms. The results demonstrate that MOBOA exhibits superior performance in these scenarios. Furthermore, the application of the BOA to mobile robot path planning is explored. The algorithm successfully optimizes path lengths ins constructed model, validating its feasibility and effectiveness through comparative analysis. Similarity, MOBOA shows promising results in addressing multi-objective path planning challenges for robots, where multiple factors such as path length, energy efficiency, and safety must be simultaneously optimized. In conclusion, the Beaver Optimization Algorithm and its multi-objective counterpart, MOBOA, represent innovative approaches in bio-heuristic optimization. Their application to complex problems like mobile robot path planning underscores their potential in providing efficient and effective solutions across various domains.
Keywords:
Beaver optimization algorithms
Heuristic algorithms
Stochastic search algorithms
Multiple objective
Mobile robots path planning
Journal
J
IF:
2.8
Papers:
3.8K
Citations:
6.9K