Construct, Merge, Solve and Adapt: Application to Unbalanced Minimum Common String Partition

Blum, Christian

doi:10.1007/978-3-319-39636-1_2

Cited by 7 publications

(10 citation statements)

References 9 publications

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“…The first column contains the problem instance name, and the second column provides the value of an optimal solution (if known). 4 The results of Gen-Cmsa-Bip and Gen/Cp-Cmsa-Bip are presented in three columns for each algorithm. The first column (with heading 'Best') contains the best result obtained over 10 runs, the second column (with heading 'Avg.')…”

Section: Resultsmentioning

confidence: 99%

“…Obviously, the parameters of CMSA have to be adjusted in order for the size of the reduced sub-instances to be such that the black-box solver can solve them efficiently. CMSA has been applied to several NP-hard combinatorial optimization problems, including minimum common string partition [6,4], the repetition-free longest common subsequence problem [5], and the multi-dimensional knapsack problem [15].…”

Section: Introductionmentioning

confidence: 99%

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Generic CP-Supported CMSA for Binary Integer Linear Programs

Blum

Santos

2018

Hybrid Metaheuristics

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Construct, Merge, Solve & Adapt (CMSA) is a general hybrid metaheuristic for solving combinatorial optimization problems. At each iteration, CMSA (1) constructs feasible solutions to the tackled problem instance in a probabilistic way and (2) solves a reduced problem instance (if possible) to optimality. The construction of feasible solutions is hereby problem-specific, usually involving a fast greedy heuristic. The goal of this paper is to design a problem-agnostic CMSA variant whose exclusive input is an integer linear program (ILP). In order to reduce the complexity of this task, the current study is restricted to binary ILPs. In addition to a basic problem-agnostic CMSA variant, we also present an extended version that makes use of a constraint propagation engine for constructing solutions. The results show that our technique is able to match the upper bounds of the standalone application of CPLEX in the context of rather easy-to-solve instances, while it generally outperforms the standalone application of CPLEX in the context of hard instances. Moreover, the results indicate that the support of the constraint propagation engine is useful in the context of problems for which finding feasible solutions is rather difficult.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Generic CP-Supported CMSA for Binary Integer Linear Programs

Blum

Santos

2018

Hybrid Metaheuristics

Self Cite

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“…Constraints (20) enforce the release times. Constraints (21) specify precedences between jobs a and b and Constraints (22) require that no more than one job is executed on a machine. Constraints (23) ensure that the resource constraints are satisfied.…”

Section: Modelmentioning

confidence: 99%

“…Both of these methods use the same basic idea: using a MIP to generate a "merged" solution in the subspace that is spanned by a pool of heuristic solutions. The first is a very recent approach-Merge Search (MS) [18] and the second method is Construct, Merge, Solve and Adapt (CMSA) [19][20][21][22]. The study by [18] shows that MS is well suited for solving the constrained pit problem.…”

Section: Introductionmentioning

confidence: 99%

“…Reference [19] apply CMSA to minimum common string partition and to the minimum covering arborescence problem, ref. [20] investigate the repetition-free longest common subsequence problem, and [21] examines the unbalanced common string partition problem. The study by [22] investigates a hybrid of CMSA and parallel ant colony optimisation (ACO) for resource constrained project scheduling.…”

Section: Introductionmentioning

confidence: 99%

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Solution Merging in Matheuristics for Resource Constrained Job Scheduling

2020

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Matheuristics have been gaining in popularity for solving combinatorial optimisation problems in recent years. This new class of hybrid method combines elements of both mathematical programming for intensification and metaheuristic searches for diversification. A recent approach in this direction has been to build a neighbourhood for integer programs by merging information from several heuristic solutions, namely construct, solve, merge and adapt (CMSA). In this study, we investigate this method alongside a closely related novel approach—merge search (MS). Both methods rely on a population of solutions, and for the purposes of this study, we examine two options: (a) a constructive heuristic and (b) ant colony optimisation (ACO); that is, a method based on learning. These methods are also implemented in a parallel framework using multi-core shared memory, which leads to improving the overall efficiency. Using a resource constrained job scheduling problem as a test case, different aspects of the algorithms are investigated. We find that both methods, using ACO, are competitive with current state-of-the-art methods, outperforming them for a range of problems. Regarding MS and CMSA, the former seems more effective on medium-sized problems, whereas the latter performs better on large problems.

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2016

Metaheuristics for String Problems in Bio‐Informatics

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Construct, Merge, Solve and Adapt: Application to Unbalanced Minimum Common String Partition

Cited by 7 publications

References 9 publications

Generic CP-Supported CMSA for Binary Integer Linear Programs

Generic CP-Supported CMSA for Binary Integer Linear Programs

Solution Merging in Matheuristics for Resource Constrained Job Scheduling

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