Discussing the article: "Beluga Whale Optimization (BWO) Algorithm"

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Check out the new article: Beluga Whale Optimization (BWO) Algorithm.

A candidate for our ranking table is Beluga Whale Optimization, a metaheuristic based on three behavioral patterns of the beluga whale: pair swimming, hunting using Lévy flight, and population renewal through whale fall. During implementation, we discovered that the algorithm does not so much optimize as read the geometry of the test bench; we examine how this works and develop an honest, promising modification called BWOm.

We continue our series on population-based optimization algorithms. Let me remind you why we started all this in the first place. We systematically review nature-inspired and other algorithms, implement each one within a single framework, run them through the same set of test functions, and record the results in a shared ranking table. The table is not an end in itself, nor is it a collection for the sake of collecting. Its purpose is purely practical: a trader who is optimizing the parameters of a trading strategy needs a reliable search tool, not just any tool that comes along. The more algorithms have undergone the same fair and reproducible testing, the more confidently we can determine which one is best suited for a specific task. That is why we are looking for the best — systematically, one by one.

Today's candidate is Beluga Whale Optimization (BWO), a beluga whale optimization algorithm. It was proposed in 2022 in a paper by Zhong, Li, and Meng (Knowledge-Based Systems) and, as is often the case in this field, draws on a behavioral metaphor. The authors observed three distinct behavioral patterns in beluga whales and used each one as the basis for a separate phase of the algorithm. Synchronized pair swimming, which serves as the basis for exploration of the search space. A hunting dive incorporating elements of Lévy flight — the exploitation phase. A whale fall — a natural process in which a dead whale sinks to the bottom — is transformed in the algorithm into a means of renewing the population. In my opinion, it is an elegant idea: three fundamentally different operators that transition smoothly into one another over the course of a run, with almost no free parameters — the authors present this as one of the method’s strengths. It sounds promising, which makes it all the more interesting to see how it turns out on our test bench.

Author: Andrey Dik