Discussing the article: "Beetle Swarm Optimization (BSO)"

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Check out the new article: Beetle Swarm Optimization (BSO).

We consider a BAS+PSO (BSO) hybrid, where BAS provides a local direction signal and PSO facilitates the exchange of best solutions within the swarm. The article presents a mathematical model, pseudocode, an implementation of the class in MQL5, and test results from a standard test bench. This material allows reproducing the algorithm, configuring its parameters, and understanding how three objective-function evaluations per iteration affect efficiency.

Beetle Swarm Optimization Algorithm expands the population to a group of beetles, each of which retains the two-antenna detection mechanism but exchanges information with its peers according to rules borrowed from PSO. Each beetle’s position is updated as a weighted combination of two components: the PSO component guides the beetle toward the personal and global best solutions, while the BAS component adjusts its movement based on the local gradient estimated by a pair of antennae. The coefficient "λ" governs the balance between the swarm's "social knowledge" and each individual's "personal perception."

In this article, we will examine the mathematical framework of the BSO algorithm, break down all the key formulas, and present detailed pseudocode and an implementation in MQL5 within the standard test bench for comparing metaheuristic algorithms.


Author: Andrey Dik

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