Strategic oscillation
Algorithms AIMech:StrategicOscillation DRAFT
A search-control strategy that alternates search directions or regimes around selected boundaries, controlling excursions and returns to explore solutions beyond the region of a single one-direction search.
Identity Notes & Concept Scope
The concept is deliberate boundary-relative control, not incidental oscillation of objective values and not every tabu-search method. Feasibility/infeasibility is an important application, while the primary source also describes cardinality boundaries, opposing constructive/destructive searches and rebounds without crossing. The rudimentary Glover–Hao rule is one instance, not the universal definition. No normalized identity match was found across all 442 pinned AIO labels/scoped synonyms and 304 preceding native IDs/names/aliases. Full-checkout fixed-string searches included ignored and hidden files in worktree and main, returning 176 and 176 lines respectively. Inspected text hits were prior discovery leads, uses of local search inside existing methods, source references and their saved reports/reviews. These were references to methods rather than existing candidate records. Six exact AIO CLASS nodes with scoped synonyms/comments and outgoing edges and six native neighboring mechanisms were inspected. Absence is bounded to this snapshot and semantic neighborhood.
Claims & Grounded Evidence
1 Claim
Mechanism
#method
Choose a boundary or paired search regimes, move-selection rules for the relevant sides or directions, and a policy controlling reversal or excursion depth. Drive search toward and beyond a boundary, or rebound from it at specified depths, then reverse or change regimes so subsequent trajectories explore alternative approaches. Glover describes tabu conditions that compel crossings/returns or graduated penalties whose weights change with position and direction; a fixed sine wave or single depth schedule is not required. For the later illustrated feasibility instance, while feasible choose an objectively best improving feasible move when one exists, otherwise an objectively best available move, which may enter infeasibility. While infeasible, restrict to moves that improve the infeasibility measure and select the objectively best among them; if none improves infeasibility, first restrict to the least deterioration of that measure and then select by objective quality. These two ranking levels are not interchangeable. Keep feasible solution quality distinct from temporary infeasible search states.
⚠️ Curated Limitations & Negative Results
- The rudimentary rule does not ensure that every local step crosses a boundary, and generic strategic oscillation is not restricted to feasibility crossing. The boundary, quality/infeasibility measures, depth control, memory, ties and stopping must be specified for an executable instance.
- Finding a better objective in infeasible space does not make it a valid answer to the constrained problem. The strategy alone provides no general feasibility, cycle-freedom or global-optimality guarantee.
- The later paper analyzes a constructed hard example and explicitly disclaims a universally prevailing strategy. Its complete example derivation, broader appendix and empirical performance were not audited here; no generalized theorem or benchmark claim is curated.
Artificial Intelligence Ontology (AIO) Grounding
Explicit AIO Snapshot Assessment: No Match
Rationale: No suitable AIO target was found for this general neighborhood-search method within the inspected snapshot and semantic neighborhood. Optional model-training applications or objective evaluation do not establish a learning-task or model identity.
Search Scope: All 442 pinned AIO identities/scoped synonyms and 304 prior native IDs/names/aliases; ignored/hidden text in both checkouts; semantic label search for search, gradient and optimization; six exact AIO nodes and six native neighbors.
| Examined AIO Candidate | Rejection Reason |
|---|---|
AIO:TrainingStrategy
|
The definition concerns preprocessing or training machine-learning models. General local-search optimization is not restricted to such training. |
AIO:MachineLearningTask
|
The target concerns learning methods from data; minimizing a supplied combinatorial objective is not necessarily that task. |
AIO:ReinforcementLearning
|
Memory, adaptive penalties or an acceptance criterion do not themselves define a policy/environment learning problem. Exploration terminology is insufficient for a forced mapping. |
AIO:ReinforcementLearningLLM
|
The method is not a language model fine-tuned through reward feedback. |
AIO:EvolutionaryLLM
|
The target is a large language model. Its related evolutionary-algorithms synonym does not identify all optimizers or convert a single-trajectory method into a model. |
AIO:MarkovChain
|
Some memoryless configurations admit a Markov description, while others use search history. That mathematical representation alone does not identify the optimization framework with the pinned network/model class or its semantic parent. |
Evidence Sources & Primary Literature
2 Sources| Source ID | Publication / Document | Type / Access | Reference & Checksum |
|---|---|---|---|
method-source
|
Fred Glover
|
primary paper
full_text
|
ID: doi:10.1287/ijoc.1.3.190
sha256:fb1102afc415…
|
additional-1
|
Fred Glover, Jin-Kao Hao
|
primary paper
full_text
|
ID: doi:10.1007/s10479-009-0597-1
sha256:de0f17fec683…
|
Curation Provenance
Curator: Codex
Method: ai_assisted
Model: GPT-6
Created: 2026-10-11T14:27:03Z
Updated: 2026-10-11T14:27:03Z
Source File:
data/curated/StrategicOscillation.yaml