OR-Library: Distributing Test Problems by Electronic Mail

Beasley, John E.

doi:10.1057/jors.1990.166

Cited by 1,400 publications

(248 citation statements)

References 5 publications

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“…Population size, number of generations, selection percentage, and mutation probability were considered factors that may affect the computational performance. The experiments were performed using benchmark problems from Fisher et al, better known as FT, and Lawrence's, better known as LA, all of them taken from the OR-Library [52][53]. For the first experiment, low and medium complexity problems (Ft06, La01, La06, La09, La11, La14, and La17) were used; meanwhile, for the second experiment, problems of high complexity (Ft10, Ft20, La16, La21, La 24, La25, and La 38) were used.…”

Section: Computational Resultsmentioning

confidence: 99%

A memetic algorithm for minimizing the makespan in the Job Shop Scheduling problem

Lamos-Díaz¹,

Aguilar-Imitola²,

Pérez-Díaz³

et al. 2017

Rev. Fac. Ing.

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The Job Shop Scheduling Problem (JSP) is a combinatorial optimization problem cataloged as type NP-Hard. To solve this problem, several heuristics and metaheuristics have been used. In order to minimize the makespan, we propose a Memetic Algorithm (MA), which combines the exploration of the search space by a Genetic Algorithm (GA), and the exploitation of the solutions using a local search based on the neighborhood structure of Nowicki and Smutnicki. The genetic strategy uses an operation-based representation that allows generating feasible schedules, and a selection probability of the best individuals that are crossed using the JOX operator. The results of the implementation show that the algorithm is competitive with other approaches proposed in the literature.

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Section: Computational Resultsmentioning

confidence: 99%

A memetic algorithm for minimizing the makespan in the Job Shop Scheduling problem

Lamos-Díaz¹,

Aguilar-Imitola²,

Pérez-Díaz³

et al. 2017

Rev. Fac. Ing.

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“…Beasley [49] proposed a collection of test data sets, known as OR-Library and maintained by the Beasley University, which were studied by Falkenauer [27]. This collection contains a variety of test data sets for a variety of Operational Research problems, including the BPP in several dimensions.…”

Section: Tests Instancesmentioning

confidence: 99%

Generating Bin Packing Heuristic Through Grammatical Evolution Based on Bee Swarm Optimization

Sotelo-Figueroa

Soberanes

Carpio

et al. 2016

Nature-Inspired Design of Hybrid Intelligent Systems

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In the recent years, Grammatical Evolution (GE) has been used as a representation of Genetic Programming (GP). GE can use a diversity of search strategies including Swarm Intelligence (SI). Bee Swarm Optimization (BSO) is part of SI and it tries to solve the main problems of the Particle Swarm Optimization (PSO): the premature convergence and the poor diversity. In this paper we propose using BSO as part of GE as strategies to generate heuristics that solve the Bin Packing Problem (BPP). A comparison between BSO, PSO, and BPP heuristics is performed through the nonparametric Friedman test. The main contribution of this paper is to propose a way to implement different algorithms as search strategy in GE. In this paper, it is proposed that the BSO obtains better results than the ones obtained by PSO, also there is a grammar proposed to generate online and offline heuristics to improve the heuristics generated by other grammars and humans.

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“…An FSTinsertion requires O(n) time. 3 The actual behavior of the algorithm is that it inserts F i into T by pushing some of the FSTs in T out and reconnecting the components by adding edges from MST(Z ). More precisely, if an FST F i with k terminals is inserted into T , k − 1 cycles are created (Figure 6(a)).…”

Section: Greedy Fst Insertionmentioning

confidence: 99%

“…The CPU times in these two papers have been "normalized" using the Linpack benchmark. 8 The heuristics were evaluated on instances which are available from the OR-Library [3]. These instance are randomly generated problem instances with 10-1000 terminals, 15 instances for each size; optimal solutions are known for all these instances [24], [23].…”

Section: Comparison Tomentioning

confidence: 99%

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Concatenation-Based Greedy Heuristics for the Euclidean Steiner Tree Problem

Zachariasen¹,

Winter²

1999

Algorithmica

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We present a class of O(n log n) heuristics for the Steiner tree problem in the Euclidean plane. These heuristics identify a small number of subsets with few, geometrically close, terminals using minimum spanning trees and other well-known structures from computational geometry: Delaunay triangulations, Gabriel graphs, relative neighborhood graphs, and higher-order Voronoi diagrams. Full Steiner trees of all these subsets are sorted according to some appropriately chosen measure of quality. A tree spanning all terminals is constructed using greedy concatenation. New heuristics are compared with each other and with heuristics from the literature by performing extensive computational experiments on both randomly generated and library problem instances.

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OR-Library: Distributing Test Problems by Electronic Mail

Cited by 1,400 publications

References 5 publications

A memetic algorithm for minimizing the makespan in the Job Shop Scheduling problem

A memetic algorithm for minimizing the makespan in the Job Shop Scheduling problem

Generating Bin Packing Heuristic Through Grammatical Evolution Based on Bee Swarm Optimization

Concatenation-Based Greedy Heuristics for the Euclidean Steiner Tree Problem

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