Discussing the article: "Buzzard Optimization Algorithm (BUZOA)"

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Check out the new article: Buzzard Optimization Algorithm (BUZOA).

BUZOA is a population-based metaheuristic algorithm in which each agent randomly selects one of three hunting tactics at each iteration: a narrow search around its personal best, a classic PSO step toward the flock leader, or full teleportation to a random point in the search space. The article examines the implementation of the algorithm in MQL5, identifies a coefficient sign error in the original formulation, and presents benchmark results on a standard test bench.

Among population-based optimization algorithms, particle swarm optimization (PSO) is a method on which most modern hybrid algorithms are based. The basic PSO scheme has been known for decades; its strengths and weaknesses have been studied, and attempts to improve it generally move in one direction: making the mathematics more complex, adding adaptive coefficients, and introducing new operators. But what if we look at the problem from a different angle — not by making the equations more complex, but by changing the agent’s decision-making logic itself? What if, at every moment in time, each particle randomly chooses one of several qualitatively different behavioral strategies, just as living beings do in nature? This is precisely the approach devised by the authors of BUZOA, and the question is whether this biologically motivated mode switching actually offers something that classical PSO-like algorithms do not — or whether it is merely an attractive metaphor with no practical value.

The reader will get a fully analyzed metaheuristic BUZOA algorithm, implemented in MQL5 and tested as part of our test bench, with a detailed analysis of its three hunting modes, a comparison of the literal formulas from the article by Arshagi, Ashurian, and Gabeli (2019) with their rational interpretation, a benchmark on the Hilly, Forest, and Megacity test functions, and recommendations for parameter tuning. At the same time, the reader will see how a biological analogy — sight, smell, and tracking predators — is translated into the mathematical language of velocity, inertia, and random scattering, and will be able to assess how well this analogy works in practice.

Author: Andrey Dik