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A merge search algorithm and its application to the constrained pit problem in mining

Published: 02 July 2018 Publication History

Abstract

Many large-scale combinatorial problems contain too many variables and constraints for conventional mixed-integer programming (MIP) solvers to manage. To make the problems easier for the solvers to handle, various meta-heuristic techniques can be applied to reduce the size of the search space, by removing, or aggregating, variables and constraints. A novel meta-heuristic technique is presented in this paper called merge search, which takes an initial solution and uses the information from a large population of neighbouring solutions to determine promising areas of the search space to focus on. The population is merged to produce a restricted sub-problem, with far fewer variables and constraints, which can then be solved by a MIP solver. Merge search is applied to a complex problem from open-pit mining called the constrained pit (CPIT) problem, and compared to current state-of-the-art results on well known benchmark problems minelib [7] and is shown to give better quality solutions in five of the six instances.

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      cover image ACM Conferences
      GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference
      July 2018
      1578 pages
      ISBN:9781450356183
      DOI:10.1145/3205455
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      Published: 02 July 2018

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      Author Tags

      1. applied computing
      2. hybrid algorithms
      3. merge search
      4. mine planning
      5. mixed integer programming

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      • (2024)Multi-Objective Evolutionary Optimization for Large-Scale Open Pit Mine Scheduling2024 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC60901.2024.10612008(1-8)Online publication date: 30-Jun-2024
      • (2024)CMSA based on set covering models for packing and routing problemsAnnals of Operations Research10.1007/s10479-024-06295-9343:1(1-38)Online publication date: 8-Oct-2024
      • (2024)Introduction to CMSAConstruct, Merge, Solve & Adapt10.1007/978-3-031-60103-3_1(1-40)Online publication date: 23-Apr-2024
      • (2022)A Review of Population-Based Metaheuristics for Large-Scale Black-Box Global Optimization—Part IIIEEE Transactions on Evolutionary Computation10.1109/TEVC.2021.313083526:5(823-843)Online publication date: 1-Oct-2022
      • (2022)Comparison of Trajectory and Population-Based Algorithms for Optimizing Constrained Open-Pit Mining Problem2022 9th International Conference on Soft Computing & Machine Intelligence (ISCMI)10.1109/ISCMI56532.2022.10068481(109-112)Online publication date: 26-Nov-2022
      • (2021)Using Statistical Measures and Machine Learning for Graph Reduction to Solve Maximum Weight Clique ProblemsIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2019.295482743:5(1746-1760)Online publication date: 1-May-2021
      • (2020)Solution Merging in Matheuristics for Resource Constrained Job SchedulingAlgorithms10.3390/a1310025613:10(256)Online publication date: 9-Oct-2020
      • (2020)Automatic decomposition of mixed integer programs for lagrangian relaxation using a multiobjective approachProceedings of the 2020 Genetic and Evolutionary Computation Conference10.1145/3377930.3390233(263-270)Online publication date: 25-Jun-2020
      • (2019)An improved merge search algorithm for the constrained pit problem in open-pit miningProceedings of the Genetic and Evolutionary Computation Conference10.1145/3321707.3321812(294-302)Online publication date: 13-Jul-2019

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