An Improved Harris Hawks Optimization Algorithm Based on Bi-Goal Evolution and Multi-Leader Selection Strategy for Multi-Objective Optimization
Why this work is in the frame
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Bibliographic record
Abstract
The Harris Hawks Optimizer (HHO) is a bio-inspired metaheuristic acknowledged for its effectiveness in addressing mono-objective optimization problems.However, its application has been limited to these specific challenges.To overcome this constraint and to navigate complex multi-objective optimization challenges, a Guided Multi-Objective variant of HHO, termed as Guided Multi-Objective Harris Hawks Optimization (GMOHHO), is introduced in this study.In the developed GMOHHO algorithm, an archival mechanism is integrated.This mechanism is specifically designed to store non-dominated solutions and to enhance their retrievability during the search process.Moreover, a robust multi-leader selection procedure is implemented, facilitating the steering of the primary set of solutions towards potential areas within the search space.Further, the Bi-Goal Evolution (BIGE) framework is utilized.This framework aids in the transformation of a search space with multitudinous objectives into a bi-objective one, thereby augmenting environmental selection.This integration ensures a balanced compromise between the convergence and diversity of solutions.The performance of the proposed GMOHHO algorithm was appraised across a series of test functions.The results consistently displayed its supremacy over the conventional HHO approach as well as other cutting-edge multi-objective optimization techniques.With its noteworthy capability to address a broad range of multiobjective optimization problems, the GMOHHO algorithm delivers high-quality solutions within acceptable computational timeframes.This study, therefore, paves the way for a promising approach to multi-objective optimization, potentially expanding the application sphere of the HHO algorithm.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.009 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it