An Improved Harris Hawks Optimization Algorithm Based on Bi-Goal Evolution and Multi-Leader Selection Strategy for Multi-Objective Optimization
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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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".