Research map / Learning theory and algorithms / Combinatorial and search optimization
Anytime Heuristic and Monte Carlo Methods for Large-Scale Simultaneous Coalition Structure Generation and Assignment
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the hybrid greedy-plus-hill-climb search | SCSGA · UPD, 1000 agents, 20 alternatives | percent of expected optimum: 99% | Helped |
| Use the best proposed heuristic or Monte Carlo approach | SCSGA · UPD, 1000 agents | percent of expected optimum: 99.5% | Helped |
| Use permutative hill climb instead of the state-of-the-art MP solver | SCSGA · Small-scale synthetic problems, 15 agents and 5 alternatives | percent of optimum: roughly 98% of the optimal after 2 seconds | Helped |
| Use local-search methods (RHC, RHY, or RGA) rather than simulated annealing | SCSGA · Large-scale problems, UPD and NPD | solution quality | Helped |
| Use random-rollout MCTS as the search method | SCSGA · Large-scale problems with more than 300 agents | solution quality | Hurt |
| Use simulated annealing as the search method | SCSGA · Large-scale problems as agent count increases | solution quality | Hurt |
| Use simulated annealing rather than other methods | SCSGA · Sparse synthetic distributions | solution quality | Mixed |
| Increase computation time for simulated annealing | SCSGA · Synthetic benchmarks | solution quality | Helped |
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