Biogeography-Based Optimization Techniques in Computational Problems

Biogeography-Based Optimization (BBO) is a population-based metaheuristic inspired by the geographical distribution of biological species. In this paradigm, each candidate solution is treated as a “habitat” characterised by its Habitat Suitability Index (HSI), which quantifies solution quality. Migration operators transfer solution features from high-HSI habitats to low-HSI habitats, thereby enhancing global search through information sharing. Complementary mutation operators introduce random variations to prevent premature convergence and maintain diversity. Over the past decade, researchers have hybridised BBO with other heuristics—such as tabu search, differential evolution and particle swarm optimisation—to strengthen local exploitation, overcome stagnation and tackle complex combinatorial and continuous problems. Recent advances include adaptive parameter schemes that modulate migration rates, incorporation of membrane-computing frameworks to structure solution exchange, and strategies tailored for high-dimensional spaces. These developments have broadened the applicability of BBO to real-world tasks such as neural network hyperparameter tuning, engineering design optimisation and large-scale data-driven model calibration, demonstrating robust performance and scalability across heterogeneous domains.

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Adaptive Habitat Biogeography-Based Optimizer (AHBBO) introduces a dynamically resizing habitat model and regulated mutation schedule to overcome premature convergence in complex landscapes. By allowing variable habitat sizes and adaptive dispersal rates, AHBBO achieves enhanced exploration and exploitation balance. Evaluated on over fifty benchmark functions and applied to deep convolutional neural network hyperparameter tuning, this method demonstrates accelerated convergence and consistent accuracy improvements over standard optimisers.

A Dual Biogeography-Based Optimisation algorithm (SCBBO) integrates a sine-cosine update mechanism with a dynamic hybrid mutation operator and Latin hypercube sampling to address high-dimensional and constrained design problems. The dual learning strategy generates paired candidate solutions, improving convergence reliability. Comparative studies on functions up to 10,000 dimensions and engineering design benchmarks show SCBBO’s superior optimisation accuracy and stability in large-scale settings.

Biogeography-Based Optimization Techniques in Computational Problems publication trend

The graph below shows the total number of articles in biogeography-based optimization techniques in computational problems across all publications each year (not limited to Nature Index journals).

Technical terms

Biogeography-Based Optimization (BBO): A bioinspired population-based algorithm that models candidate solutions as habitats exchanging features via migration and mutation.

Migration operator: A procedure that probabilistically transfers characteristics (Suitability Index Variables) from high-quality solutions to lower-quality ones, promoting information sharing.

Mutation operator: A mechanism introducing random perturbations into solutions to increase diversity and avoid local optima.

Habitat Suitability Index (HSI): A scalar metric representing the fitness or quality of a candidate solution within the optimisation space.

References

1. Adaptive habitat biogeography-based optimizer for optimizing deep CNN hyperparameters in image classification. Heliyon (2024).
2. A Biogeography‐Based Optimization Algorithm Hybridized with Tabu Search for the Quadratic Assignment Problem. Computational Intelligence and Neuroscience (2015).
3. Improved Biogeography‐Based Optimization Algorithm by Hierarchical Tissue‐Like P System with Triggering Ablation Rules. Mathematical Problems in Engineering (2021).
4. A Dual Biogeography-Based Optimization Algorithm for Solving High-Dimensional Global Optimization Problems and Engineering Design Problems. IEEE Access (2022).
5. Migration Ratio Model Analysis of Biogeography-Based Optimization Algorithm and Performance Comparison. International Journal of Computational Intelligence Systems (2016).
6. A new hybrid metaheuristic method based on biogeography-based optimization and particle swarm optimization algorithm to estimate money demand in Iran. MethodsX (2021).