Discussing the article: "Artificial Searching Swarm Algorithm (ASSA)"

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Check out the new article: Artificial Searching Swarm Algorithm (ASSA).

The article discusses the implementation of the Artificial Searching Swarm Algorithm (ASSA) in MQL5 as part of a unified test bench. The article examines three behavioral movement rules, the signal and global bulletin board mechanisms, space normalization, and the stepRatio and Pc parameters. Readers will receive a ready-made foundation for integrating ASSA, as well as an answer to the question of how successful the tactical metaphor proved to be as a basis for the competitiveness of the optimization algorithm.

The Artificial Searching Swarm Algorithm (ASSA) was published in 2012. This paper presents the results of ASSA testing on ten benchmark functions—ranging from the classic Rosenbrock, Griewank, and Rastrigin functions to constrained problems—and compares the algorithm with a genetic algorithm, PSO, and AFSA. The authors demonstrate that, with a sufficient number of iterations, ASSA consistently outperforms its competitors in high-dimensional problems, while remaining simple to implement and insensitive to initial conditions.

Mathematically, the behavior of each agent is described by three mutually exclusive movement rules. The Synergistic Movement rule is triggered with probability "Pc" when an active call signal is present: the agent takes a step toward the calling agent's coordinates, with the step length proportional to the normalized distance to the target and to a random multiplier. The search rule is triggered in the absence of a signal: the agent generates a probe point, being pulled simultaneously toward its historical personal best and the swarm’s global best, and takes a step in the calculated direction. If, however, the probe coincides with the agent’s current position—meaning that there is no personal or collective improvement nearby—a stochastic rule is activated: a random step in an arbitrary direction, which supports exploratory activity and protects the algorithm from premature stagnation. When any of the three movements brings the agent to a point with a better function value, it immediately broadcasts its coordinates, and the signaling cycle resumes.

Author: Andrey Dik