Enterprise AI Research Analysis
Offline Multi-Task Multi-Objective Data-Driven Evolutionary Algorithm with Language Surrogate Model and Implicit Q-Learning
This paper introduces Q-MetaSur, an LLM-based meta-surrogate model for offline multi-task multi-objective optimization (MTMOO). It uses a unified textual representation for task metadata, decision variables, and objective values, and a two-stage offline training strategy (supervised fine-tuning followed by RL fine-tuning with Conservative Q-Learning). The model shows superior objective approximation accuracy and helps evolutionary algorithms achieve better optimization convergence and Pareto optimality on benchmarks like CEC2019, demonstrating robust generalization and effective knowledge transfer across diverse tasks and objectives.
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sMAE Reduction
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R² Improvement
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Tasks Covered
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Textual Representation
Q-MetaSur uses a unified textual representation for metadata, decision variables, and objective values. Numerical values are encoded in scientific notation (SNE), and different objectives are delineated. This allows a single LLM to jointly model multiple tasks and a variable number of objectives without modifying its architecture.
Two-Stage Training
A two-stage offline training strategy is employed. First, supervised fine-tuning (SFT) with priority-weighted cross-entropy learns a stable 'numerical language'. Second, reinforcement learning fine-tuning (RLFT) with Conservative Q-Learning (CQL) optimizes a sequence-level reward for continuous numerical accuracy, enhancing generalization.
Data Augmentation
To ensure training robustness and stable convergence, perturb-and-score augmentation is introduced. This expands the limited offline dataset into a rich set of (sequence, reward) pairs by adding low, medium, and high-level noises to ground-truth labels. This improves the numerical quality of the Pareto front and emergent generalization.
Q-MetaSur Workflow
Input: Task Metadata & Decision Variables
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SNE Encoding to Token Sequence
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LLM Encoder-Decoder
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Two-Stage Fine-Tuning (SFT + RL-CQL)
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Advantage-Guided Inference
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Output: Multi-Objective Fitness Values
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Out of 12 instance-backbone pairs, Q-MetaSur achieves best MSS (Mean Standardized Score).
| Instance | Q-MetaSur (sMAE↓) | Q-MetaSur (R²↑) | RBFN (sMAE↓) | RBFN (R²↑) | KAN (sMAE↓) | KAN (R²↑) | FTGP (sMAE↓) | FTGP (R²↑) |
|---|---|---|---|---|---|---|---|---|
| CEC19_MTMOO-Inst1 | 0.0693 | 0.8316 | 0.2064 | -0.0751 | 0.4582 | -3.8994 | 0.2077 | -0.0611 |
| CEC19_MTMOO-Inst2 | 0.0000 | 1.0000 | 0.2615 | -0.1994 | 1.8741 | -72.8734 | 0.2510 | -0.0567 |
| CEC19_MTMOO-Inst3 | 0.0000 | 1.0000 | 0.2513 | -0.1242 | 0.2533 | -0.2909 | 0.2458 | -0.0411 |
| CEC19_MTMOO-Inst4 | 0.0627 | 0.8630 | 0.1908 | 0.0248 | 0.4278 | -3.3217 | 0.1956 | 0.0106 |
| CEC19_MTMOO-Inst5 | 0.0000 | 1.0000 | 0.1221 | 0.7146 | 0.1397 | 0.6364 | 0.0443 | 0.8468 |
| CEC19_MTMOO-Inst6 | 0.0511 | 0.9101 | 0.2173 | -0.1431 | 0.2236 | -0.3208 | 0.2110 | -0.0504 |
Sensor Coverage Optimization
The paper applies Q-MetaSur to a sensor coverage optimization problem, aiming to minimize uncovered area and costs. This real-world bi-objective problem involves optimizing the location and radius of S sensors.
With 30 different values for S (6 to 93 dimensions), Q-MetaSur achieved the best overall performance across 30 optimization tasks, demonstrating its effectiveness in heterogeneous multi-task multi-objective settings under limited function evaluations. The model's ability to generalize to unseen tasks and dimensions is crucial here.
Outcome: Q-MetaSur significantly improved convergence-diversity trade-off and provided statistically significant and robust overall gains compared to other surrogates.
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