# A Dynamic Multi-Niche Biogeography-Based Optimization Algorithm and Its Application to Robot Path Planning

## TL;DR

This paper introduces a new optimization algorithm that improves performance and diversity in solving complex problems like robot path planning.

## Contribution

The novel DMBBO algorithm enhances BBO with dynamic niches, dual migration, and elite preservation strategies.

## Key findings

- DMBBO outperformed 23 algorithms on multiple benchmark test suites in accuracy and convergence.
- The algorithm produced shorter and more stable paths in robotic path planning scenarios.
- Statistical tests confirmed the significant improvements of DMBBO over existing methods.

## Abstract

Biogeography-based optimization (BBO) is a population-based metaheuristic algorithm inspired by species migration among habitats. However, the original BBO often suffers from premature convergence and insufficient population diversity when solving complex optimization problems. To address these limitations, this paper proposes a novel dynamic multi-niche biogeography-based optimization (DMBBO) algorithm. DMBBO incorporates three effective strategies: a dynamic multi-niche population structure to maintain diversity and enhance parallel search capability, a dual-source migration mechanism to improve information exchange efficiency, and a niche-level hybrid elite preservation strategy to stabilize convergence behavior and improve solution quality. Extensive experiments were conducted on the CEC2022, CEC2020, and CEC2019 benchmark test suites under different dimensional settings. The experimental results demonstrated that DMBBO consistently outperformed 23 state-of-the-art algorithms in terms of optimization accuracy, convergence speed, and robustness, with statistically significant improvements validated by Friedman ranking and Wilcoxon rank-sum tests. An ablation study and convergence behavior analysis further confirmed the effectiveness of the proposed strategies. Additionally, DMBBO was applied to robotic path planning problems in grid-based environments involving six different scenarios with varying map sizes and obstacle densities. The results showed that DMBBO is capable of generating shorter and more stable paths in both simple and complex environments, highlighting its strong applicability to constrained optimization problems.