Local-MIP: Efficient local search for mixed integer programming

IF 4.6 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Peng Lin , Shaowei Cai , Mengchuan Zou , Jinkun Lin
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Abstract

Mixed Integer Programming (MIP) is a fundamental model in operations research with broad industrial applications. Local search is a powerful methodology for solving complex optimization problems; however, the development of local search algorithms for MIP still needs exploration. In this work, we propose Local-MIP, an efficient local search algorithm tailored for MIP that integrates novel operators and employs a two-mode architecture to adaptively apply operators based on the current solution's feasibility. For the feasible mode, we propose the lift move operator and a corresponding lift process to improve the objective value while maintaining feasibility. For the infeasible mode, we propose the breakthrough move and mixed tight move operators to respectively optimize the objective function and satisfy constraints. To apply operators intelligently, we develop a dynamic weighting scheme that balances the priorities of the objective function and constraints. Furthermore, we propose a two-level scoring function structure that hierarchically selects operations, guiding the search toward high-quality feasible solutions. Experiments are conducted on public benchmarks to compare Local-MIP with state-of-the-art MIP solvers in finding high-quality solutions. The results show that Local-MIP significantly outperforms CPLEX, HiGHS, SCIP, and Feasibility Jump while remaining competitive with the commercial solver Gurobi on challenging problems within short time limits. Moreover, Local-MIP establishes 10 new records on MIPLIB open instances.
local - mip:混合整数规划的高效局部搜索
混合整数规划(MIP)是运筹学中的一个基本模型,具有广泛的工业应用。局部搜索是解决复杂优化问题的有力方法;然而,MIP局部搜索算法的发展仍有待探索。在这项工作中,我们提出了local -MIP,这是一种为MIP定制的高效本地搜索算法,它集成了新的算子,并采用双模式架构根据当前解决方案的可行性自适应应用算子。对于可行模式,我们提出了升降操作人和相应的升降过程,在保持可行性的同时提高目标值。针对不可行模式,提出了突破移动算子和混合紧密移动算子,分别优化目标函数和满足约束条件。为了智能地应用算子,我们开发了一种动态加权方案来平衡目标函数和约束的优先级。此外,我们提出了一个两级评分函数结构,该结构分层选择操作,指导搜索高质量的可行解决方案。在公共基准上进行实验,以比较Local-MIP与最先进的MIP解决方案在寻找高质量解决方案方面的差异。结果表明,Local-MIP显著优于CPLEX、high、SCIP和可行性跳跃,同时在短时间内与商业求解器Gurobi在具有挑战性的问题上保持竞争力。Local-MIP在MIPLIB开放实例上建立了10条新记录。
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来源期刊
Artificial Intelligence
Artificial Intelligence 工程技术-计算机:人工智能
CiteScore
11.20
自引率
1.40%
发文量
118
审稿时长
8 months
期刊介绍: The Journal of Artificial Intelligence (AIJ) welcomes papers covering a broad spectrum of AI topics, including cognition, automated reasoning, computer vision, machine learning, and more. Papers should demonstrate advancements in AI and propose innovative approaches to AI problems. Additionally, the journal accepts papers describing AI applications, focusing on how new methods enhance performance rather than reiterating conventional approaches. In addition to regular papers, AIJ also accepts Research Notes, Research Field Reviews, Position Papers, Book Reviews, and summary papers on AI challenges and competitions.
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