Summary of Large Neighborhood Prioritized Search For Combinatorial Optimization with Answer Set Programming, by Irumi Sugimori et al.
Large Neighborhood Prioritized Search for Combinatorial Optimization with Answer Set Programming
by Irumi Sugimori, Katsumi Inoue, Hidetomo Nabeshima, Torsten Schaub, Takehide Soh, Naoyuki Tamura, Mutsunori Banbara
First submitted to arxiv on: 18 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary LNPS, a metaheuristic, is proposed to solve combinatorial optimization problems in Answer Set Programming (ASP). Starting with an initial solution, LNPS iteratively tries to find better solutions by destroying and prioritized searching. This approach allows for flexible search without strongly depending on destroy operators. An implementation of LNPS based on ASP is presented, demonstrating significant enhancements to solving performance. Empirical comparisons are made between LNPS and adaptive large neighborhood search. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LNPS is a new way to solve complex problems in computer programming. It starts with an initial answer and then tries to find better answers by changing the problem slightly and searching again. This process is repeated many times, allowing LNPS to find good solutions quickly. The authors of this paper show that LNPS can be very effective for solving certain types of optimization problems. |
Keywords
» Artificial intelligence » Optimization
