Bilby Optimization Algorithm (BOA): A Novel Bio-inspired Metaheuristic Algorithm for Solving Optimization Problems

The Bilby Optimization Algorithm (BOA) is developed by drawing inspiration from the natural behavior of the nocturnal mammal bilby, with the primary objective of establishing an effective balance between global exploration and local refinement.The global exploration phase of the algorithm is modeled on the animal's long-range, nonlinear, and stochastically guided movements driven by nocturnal olfactory sensing while foraging for food resources.In the guided exploration phase, the algorithm employs the rapid refuge switching pattern to simulate sudden and purposeful movements toward promising regions, thereby preventing entrapment in local optima.The exploitation phase is inspired by the bilby's digging capability and focuses on precise local refinement to enhance solution quality and ensure stable convergence.Numerical analysis and simulation of BOA on six high-dimensional multimodal benchmark functions indicate that the algorithm can effectively manage population diversity and search-space coverage while avoiding premature convergence.Performance evaluation over 23 standard benchmark functions, along with comparisons against nine competing algorithms, shows that in the unimodal category, BOA achieves the global optimum with high speed and accuracy.In high-dimensional multimodal functions, BOA simultaneously leverages exploration and exploitation to identify promising search paths and avoid local optima.Furthermore, in fixed-dimensional multimodal functions, the algorithm improves solution quality with remarkable precision and stability.Simulation findings reveal that BOA outperforms competitors in 21 out of 23 functions-equivalent to 91.3% superiority-clearly confirming its strong capability in maintaining a balanced exploration-exploitation trade-off and adapting effectively to complex search landscapes.

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