A guided local search heuristic for the capacitated arc routing problem

Beullens, Patrick; Muyldermans, Luc; Cattrysse, Dirk; Oudheusden, Dirk Van

doi:10.1016/s0377-2217(02)00334-x

Cited by 157 publications

(100 citation statements)

References 15 publications

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“…For example, Mansini and Speranza (1998) describe a case study concerning the collection of waste and recyclables in Brescia. Beullens et al (2003) consider the issue of what savings are possible by co-collection, i.e. collecting recyclable items and waste for disposal at the same time using vehicles with appropriate separate compartments.…”

Section: Household Waste Collectionmentioning

confidence: 99%

“…Among fast constructive methods, there are for instance Augment-Merge (Golden and Wong 1981), Path-Scanning (Golden et al 1983 andUlusoy's splitting technique Ulusoy 1985). Available metaheuristics include tabu search algorithms (Eglese and Li 1996;Hertz et al 2000) and a tabu search combined with scatter search components (Greistorfer 2003), a variable neighborhood search (Hertz et al 2001), a guided local search (Beullens et al 2003), and hybrid genetic algorithms (Moscato 1999;Dellaert and Jeunet 2000). More recently Lacomme et al (2004) have produced a memetic algorithm and Brandão and Eglese (2006) have proposed a deterministic tabu search algorithm.…”

Section: Arc Routing Problemsmentioning

confidence: 99%

See 1 more Smart Citation

Combinatorial optimization and Green Logistics

Sbihi¹,

Eglese²

2009

Ann Oper Res

233

106

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Section: Household Waste Collectionmentioning

confidence: 99%

Section: Arc Routing Problemsmentioning

confidence: 99%

Combinatorial optimization and Green Logistics

Sbihi¹,

Eglese²

2009

Ann Oper Res

233

106

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show abstract

“…Two different datasets are considered for the experiments. The first one, named Group A, is composed of instances taken from the known benchmark test sets egl (Eglese 1994), Beullen's C, D, E, F (Beullens et al 2003) and val (Benavent et al 1992). The second group (Group B) corresponds to the large scale CARP instances of the dataset EGL-G (Brandão and Eglese 2008).…”

Section: Experimental Studiesmentioning

confidence: 99%

Dynamic selection of evolutionary operators based on online learning and fitness landscape analysis

et al. 2016

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Self-adaptive mechanisms for the identification of the most suitable variation operator in evolutionary algorithms rely almost exclusively on the measurement of the fitness of the offspring, which may not be sufficient to assess the optimality of an operator (e.g., in a landscape with an high degree of neutrality). This paper proposes a novel adaptive operator selection mechanism which uses a set of four fitness landscape analysis techniques and an online learning algorithm, dynamic weighted majority, to provide more detailed information about the search space to better determine the most suitable crossover operator. Experimental analysis on the capacitated arc routing problem has demonstrated that different crossover operators behave differently during the search process, and selecting the proper one adaptively can lead to more promising results.

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“…Besides, these results should be considered "optimal" only when the MAENS* reward measure is considered, while they might not be anymore when in presence of a set of multiple measures, as in the case of MAENS*-II. The experiments were performed on instances taken from the known benchmark test sets proposed in [7] and [3], named egl and C,D,E,F. The analysis of the results achieved by the oracle allowed to identify a subset of 42 instances.…”

Section: Experimental Studiesmentioning

confidence: 99%

Dynamic Selection of Evolutionary Algorithm Operators Based on Online Learning and Fitness Landscape Metrics

Consoli

Minku

Yao

2014

Lecture Notes in Computer Science

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Abstract. Self-adaptive mechanisms for the identification of the most suitable variation operator in Evolutionary meta-heuristics rely almost exclusively on the measurement of the fitness of the offspring, which may not be sufficient to assess the optimality of an operator (e.g., in a landscape with an high degree of neutrality). This paper proposes a novel Adaptive Operator Selection mechanism which uses a set of four Fitness Landscape Analysis techniques and an online learning algorithm, Dynamic Weighted Majority, to provide more detailed informations about the search space in order to better determine the most suitable crossover operator on a set of Capacitated Arc Routing Problem (CARP) instances. Extensive comparison with a state of the art approach has proved that this technique is able to produce comparable results on the set of benchmark problems.

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A guided local search heuristic for the capacitated arc routing problem

Cited by 157 publications

References 15 publications

Combinatorial optimization and Green Logistics

Combinatorial optimization and Green Logistics

Dynamic selection of evolutionary operators based on online learning and fitness landscape analysis

Dynamic Selection of Evolutionary Algorithm Operators Based on Online Learning and Fitness Landscape Metrics

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