Summary of Randomized Heuristic Repair For Large-scale Multidimensional Knapsack Problem, by Jean P. Martins
Randomized heuristic repair for large-scale multidimensional knapsack problem
by Jean P. Martins
First submitted to arxiv on: 24 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Neural and Evolutionary Computing (cs.NE)
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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 The proposed efficiency-based randomization strategy for the heuristic repair in multidimensional knapsack problems (MKP) addresses a critical issue in recent metaheuristics. The NP-hard MKP aims to find a subset of maximum total profit items within capacity constraints, which has been tackled using effective feasibility maintenance strategies since Chu and Beasley’s 1998 proposal. However, the deterministic nature of this approach limits its ability to provide diverse solutions over long runs. To overcome this limitation, the authors introduce a novel randomization strategy that enhances the variability of repaired solutions without compromising quality, ultimately leading to improved overall results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The multidimensional knapsack problem is an important optimization challenge where you need to choose a group of items with the highest total value without exceeding certain limits. To solve this problem efficiently, researchers have developed clever methods called metaheuristics. One such method, proposed by Chu and Beasley in 1998, helps keep track of which solutions are possible while searching for good ones. However, this approach has a drawback – it always picks the same types of solutions, so it stops being useful after a while. To fix this issue, scientists have come up with a new way to randomize their search, making it more likely to find better answers. |
Keywords
» Artificial intelligence » Optimization
