Projects per year
Abstract
Conventional mixed-integer programming (MIP) solvers can struggle with many large-scale combinatorial problems, as they contain too many variables and constraints. Meta-heuristics can be applied to reduce the size of these problems by removing or aggregating variables or constraints. Merge search algorithms achieve this by generating populations of solutions, either by heuristic construction [4], or by finding neighbours to an initial solution [12]. This paper presents a merge search algorithm that improves the population generation heuristic in [12] and utilises a variable grouping heuristic that exploits the common information across a population to aggregate groups of variables in order to create a reduced subproblem. The algorithm is tested on some well known benchmarks for a complex problem called the constrained pit (CPIT) problem and it is compared to results produced by a merge search algorithm previously used on the same problem and the results published on the minelib [9] website.
Original language | English |
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Title of host publication | Proceedings of the 2019 Genetic and Evolutionary Computation Conference |
Place of Publication | New York NY USA |
Publisher | Association for Computing Machinery (ACM) |
Pages | 294-302 |
Number of pages | 9 |
ISBN (Electronic) | 9781450361118 |
DOIs | |
Publication status | Published - 13 Jul 2019 |
Event | The Genetic and Evolutionary Computation Conference 2019 - Prague, Czechia Duration: 13 Jul 2019 → 17 Jul 2019 Conference number: 21st https://gecco-2019.sigevo.org/index.html/HomePage https://dl.acm.org/doi/proceedings/10.1145/3321707 (Proceedings) |
Conference
Conference | The Genetic and Evolutionary Computation Conference 2019 |
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Abbreviated title | GECCO 2019 |
Country/Territory | Czechia |
City | Prague |
Period | 13/07/19 → 17/07/19 |
Internet address |
Keywords
- Applied computing
- Hybrid algorithms
- Merge search
- Mine planning
- Mixed integer programming
Projects
- 1 Finished
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Hybrid methods with decomposition for large scale optimization
Li, X., Ernst, A. & Kalyanmoy, D.
16/04/18 → 31/12/20
Project: Research