Deterministic Oscillatory Search (DOS)
DOS is the founding four's outlier: a fully deterministic population-based optimizer that
"combines the advantages of traditional gradient-based methods with the efficiency of swarm
algorithms" — no randomness. Source: andrey-dik's MQL5 implementation (C_AO_DOS,
S_DOS_Velocity).
How it works
Particles move via three mechanisms:
- Oscillatory movement — on detecting fitness deterioration a particle "bounces" (reverses direction, halves velocity), a zigzag that localizes nearby extrema.
- Fitness-slope tracking — each particle holds a three-state flag (improving / worsening / unknown) to judge movement quality.
- Swarming — when oscillation stops improving, particles move toward the global best.
Distinctive feature
Determinism / reproducibility: "given the same initial conditions, the algorithm always arrives at the same result." The natural experiment on whether stochasticity is load-bearing in metaheuristics (see exploration-vs-exploitation and synthesis).
Benchmark (context-relative)
Ranked 45th (32.36%) on the population-optimization-benchmark — underperformed stochastic methods, "particularly on low-dimensional functions" — suggesting the randomness it removes was doing useful work. Benchmark-relative (no-free-lunch-theorem).
Why T2 (re-tiered 2026-08-25)
Held at T3 since ingest, which graded the venue — MQL5, a broker's community article site — rather than what the page rests on. This page's load-bearing claims are a mechanism description of an algorithm published by its original proposers, not Dik and a comparative score, both from andrey-dik, who did not write the algorithm and has no stake in where it lands. That is somebody measuring somebody else's work, which QUALITY.md's ladder puts at T2.
The spoke's standing finding — that in this field the scorer is usually the author — is real and does not apply here; it applies to DOAm, which stays T3 because there Dik is scoring his own modification. The confound that does remain is implementation: Dik re-implements each optimizer in MQL5, so his figure grades his code as much as the method, and his scoring scheme changed mid-series (population-optimization-benchmark). Both stay flagged in prose, where a methodological limit belongs — not in the tier.
Note the floor is unmoved by this: T1 ÷ tiered counts T1 against the same denominator either way, so
the change alters the tier mix and not the spoke's floor of 0.18.
Related
metaheuristic-optimization · exploration-vs-exploitation · population-optimization-benchmark · no-free-lunch-theorem · andrey-dik · exchange-market-algorithm