Problem
Research questions and friction points this paper is trying to address.
Metaheuristic Optimization Frameworks
Discrete Optimization
Strict Time Limits
A Metaheuristic Optimization Framework for Discrete Optimization under Strict Time Limits
🤖 AI Summary
本文提出STILO框架，通过集成蚁群优化、遗传算法和模拟退火的精细配置空间来解决严格时间限制下的离散优化问题。
📝 Abstract
Real-time applications often rely on optimization approaches that can find high-quality solutions to hard problems on the order of milliseconds. Metaheuristic optimization frameworks (MOFs) are useful tools for such tasks, as they provide large sets of general-purpose search mechanisms that can return solutions under different computational budgets. However, existing work largely overlooks the available computation time as an explicit dimension of analysis. In this work, we introduce STILO, a MOF specifically designed for optimization under strict time limits. STILO integrates fine-grained configuration spaces for ant colony optimization (ACO), genetic algorithm (GA), and simulated annealing (SA), combining existing and novel operators. We performed experiments using both synthetic and benchmark instances of various discrete optimization problems. The results indicate that the proposed discrete distance calculation mechanism for SA is useful for optimization under strict time limits. They also show that the relative effectiveness of the proposed problem-independent graph structures for ACO can vary across time limits, even for the same instance characteristics. More generally, the results demonstrate that the effectiveness of algorithm families and operators depends not only on the problem type, but also on the characteristics of the instance and the available computational budget.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Metaheuristic Optimization Framework
Strict Time Limits
Discrete Distance Calculation Mechanism
Problem-Independent Graph Structures
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