Holistic swarm optimization: A novel metaphor-less algorithm guided by whole population information for addressing exploration-exploitation dilemma
Bibliographic record
Abstract
This paper introduces a novel metaphor-less optimization algorithm called Holistic Swarm Optimization (HSO), designed to enhance the search process by utilizing data from the entire population. Unlike conventional algorithms that rely on partial or local information, HSO adopts a comprehensive approach, ensuring that each decision is informed by the overall distribution and fitness landscape of the population. The algorithm dynamically balances exploration and exploitation through an adaptive framework that integrates root-mean-squared (RMS) fitness-based displacement coefficients, simulated annealing-based selection, and adaptive mutation. This structure enables HSO to efficiently navigate complex, multimodal optimization problems while avoiding local optima. The performance of HSO is evaluated on two widely used benchmark test suites–CEC 2005 and CEC 2014–and a series of real-world engineering design problems. Results show that HSO delivers competitive and stable performance when compared to several state-of-the-art metaphor-based and metaphor-less algorithms. These findings demonstrate the effectiveness of a holistic population-guided approach in achieving robust optimization outcomes, making HSO a promising alternative for solving diverse and challenging problems without reliance on metaphorical inspirations. The source codes and implementation guidance for the HSO algorithm are available for public access on the https://github.com/ebrahimakbary/HSO .
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".