A Self-Organizing Multimodal Multi-Objective Coati Optimization Algorithm
Bibliographic record
Abstract
The Coati Optimization Algorithm (COA) has emerged as a prominent evolutionary algorithm renowned for its efficacy in addressing real-world problems. Its wide-ranging applicability across diverse domains is a testament to its exceptional performance and versatility. Compared to other evolutionary algorithms, COA has been proven to possess excellent global and local search capabilities. This paper introduces a novel self-organizing multimodal multi-objective Coati Optimization Algorithm (MMOCOA) designed specifically to tackle multimodal multi-objective problems. The proposed algorithm aims to effectively handle the complexities associated with such problems by incorporating self-organizing mechanisms into the Coati optimization framework. Primarily, MMOCOA utilizes a self-organizing speciation method as its primary approach to identify the Pareto optimal solutions. This speciation tactic can establish stable niches and continually updates them to actively search for and preserve the optimal Pareto solutions. Furthermore, an improved self-organization mechanism is proposed to enhance the generation speed of the niches. Additionally, MMOCOA incorporates a non-dominated sorting method and a specialized crowding distance technique to effectively preserve the diversity of both the decision and objective space. To assess the effectiveness of MMOCOA, this study presents a comprehensive evaluation using eleven multimodal multi-objective test functions. Additionally, MMOCOA is benchmarked against five state-of-the-art multimodal multi-objective optimization algorithms. The experimental results highlight the superior performance of MMOCOA, as it demonstrates the capability to discover a larger number of Pareto solutions compared to the other algorithms under consideration.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".