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 metaphorless optimization algorithm, the Holistic Swarm Optimization (HSO), designed to enhance the search process by leveraging data from the entire population. Unlike traditional methods that rely on partial or subset information, HSO's comprehensive approach ensures that each decision is informed by a holistic understanding of the population's distribution and fitness landscape, leading to more robust and thorough search outcomes. HSO dynamically balances exploration and exploitation by adjusting its strategies in real-time based on feedback. This adaptive mechanism optimizes the balance between exploring new areas and refining existing solutions, crucially avoiding local optima and promoting global search capabilities. The algorithm introduces effective displacement coefficients calculated based on using root-mean-squared (RMS) fitness values by exploiting the information from the whole population in a setup that ensures movement toward better and away from worse solutions. Additionally, adaptive mutation and simulated annealing-based selection are integrated, enhancing the exploration of new search spaces and adaptively adjusting the acceptance probability of new positions, respectively. The robustness of HSO has been validated across various benchmark and engineering design problems, demonstrating its efficacy and reliability in solving complex optimization tasks. The primary contributions of this research include the comprehensive utilization of population data, the introduction of effective displacement coefficients, and the dynamic balancing of exploration and exploitation. The proposed HSO algorithm not only outperforms existing metaphorless and metaphor-based metaheuristics but also underscores the advantages of using a holistic population-based approach. The source codes and implementation guidance for the HSO algorithm are available for public access on 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".