A comparison of three heuristic optimization algorithms for solving the multi-objective land allocation (MOLA) problem
Bibliographic record
Abstract
Multi-objective land allocation (MOLA) can be regarded as a spatial optimization problem that allocates appropriate use to specific land units concerning some objectives and constraints. Simulating annealing (SA), genetic algorithm (GA), and particle swarm optimization (PSO) have been popularly applied to solve MOLA problems, but their performance has not been well evaluated. This paper applies the three algorithms to a common MOLA problem that aims to maximize land suitability and spatial compactness and minimize land conversion cost subject to the number of units allocated for each use. Their performance has been evaluated based on the solution quality and the computational cost. The results demonstrate that: (1) GA consistently achieves quality solutions that satisfy both the objectives and the constraints and the computational cost is lower. (2) The popular penalty function method does not work well for SA in handling the constraints. (3) The solution quality of PSO needs to be improved. Techniques that better adapt PSO for discrete variables in MOLA problems need to be developed. (4) All three algorithms take high computational costs to achieve quality solutions in handling the objective of maximizing spatial compactness. How to encourage compact allocation is a common problem for them.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".