arXiv cs.AI· Jakub Cebula, Konrad Gmyrek, Pawe{\l} B. Myszkowski, Marko {\DJ}urasevi\'c, {\L}ukasz P. Olech, Adam Paliszek-Sa{\l}adyga·· 3 小时前AI 评分22
工业过程元启发式开发方法（IPMD）及 BNTGP 求解 NP 难多目标优化问题案例研究
Tailoring Optimization Approaches with Domain Knowledge in Industry Process for Meta-heuristic Development
AI 导读
论文提出 IPMD（Industry Process for Meta-heuristic Development）方法体系，系统化地将元启发式与超启发式应用于优化问题。
正文 · 原文
Abstract:The paper presents an Industry Process for Meta-heuristic Development (IPMD) schema, a systematic methodology for applying meta- and hyper-heuristic applications to optimization problems. In this context, we present a case study of Balancing Non-dominated Tournament Genetic Programming (BNTGP) applied to solve two NP-hard multi-objective optimization problems: routing (Capacitated Vehicle Routing Problem with Time Windows) and scheduling (Multi-Skill Resource Constrained Project Scheduling Problem). The BNTGP method is based on a metaheuristic design. Its genetic programming-based representation makes BNTGP a hyper-heuristic, as it enables the exploration of a search space of heuristics for solving problems in general. The resulting heuristic can then be applied across a set of problem instances.
| Subjects: | Neural and Evolutionary Computing (cs.NE); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2611.13940 [cs.NE] |
| (or arXiv:2611.13940v1 [cs.NE] for this version) | |
| https://doi.org/10.48550/arXiv.2611.13940 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Łukasz Olech Piotr [view email]
[v1]
Tue, 10 Nov 2026 11:24:26 UTC (62 KB)
来源：arXiv cs.AI · arxiv.org