COSEARCH: A Parallel Cooperative Metaheuristic

Talbi, El‐Ghazali; Bachelet, Vincent

doi:10.1007/s10852-005-9029-7

Cited by 52 publications

(30 citation statements)

References 16 publications

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“…• COSEARCH [25], which is a cooperative parallel method based on tabu search, genetic algorithms and a kick operator. The (published) results are obtained from a heterogeneous grid computing platform using around 150 processors.…”

Section: Experimentationmentioning

confidence: 99%

On Integrating Population-Based Metaheuristics with Cooperative Parallelism

López

Munera

Díaz

et al. 2018

2018 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)

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Section: Experimentationmentioning

confidence: 99%

On Integrating Population-Based Metaheuristics with Cooperative Parallelism

López

Munera

Díaz

et al. 2018

2018 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)

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“…Reliance on a single solution may restrict their ability in dealing with a large and heavily constrained search space, as it is widely known that single solution based methods are not well suited to cope with the large search spaces and heavily constrained problems [10]. In order to enhance the efficiency of the proposed hyper-heuristic framework and to diversify the search, we embed it with a memory mechanism as in [38] which contains a collection of both high quality and diverse solutions, updated as the algorithm progresses. The integrated memory mechanism Set GEP parameters Generate a population of solutions Calculate solutions fitness Select two solutions (S 1 and S 2 ) Apply crossover, mutation and inversion on S 1 and S 2 to generate S`1 and S`2.…”

Section: ) Memory Mechanismmentioning

confidence: 99%

Automatic Design of a Hyper-Heuristic Framework With Gene Expression Programming for Combinatorial Optimization Problems

Sabar

Ayob

Kendall

et al. 2015

IEEE Trans. Evol. Computat.

105

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Abstract-Hyper-heuristic approaches aim to automate heuristic design in order to solve multiple problems instead of designing tailor-made methodologies for individual problems. Hyper-heuristics accomplish this through a high level heuristic (heuristic selection mechanism and an acceptance criterion). This automates heuristic selection, deciding whether to accept or reject the returned solution. The fact that different problems or even instances, have different landscape structures and complexity, the design of efficient high level heuristics can have a dramatic impact on hyper-heuristic performance. In this work, instead of using human knowledge to design the high level heuristic, we propose a gene expression programming algorithm to automatically generate, during the instance solving process, the high level heuristic of the hyperheuristic framework. The generated heuristic takes information (such as the quality of the generated solution and the improvement made) from the current problem state as input and decides which low level heuristic should be selected and the acceptance or rejection of the resultant solution. The benefit of this framework is the ability to generate, for each instance, different high level heuristics during the problem solving process. Furthermore, in order to maintain solution diversity, we utilize a memory mechanism which contains a population of both high quality and diverse solutions that is updated during the problem solving process. The generality of the proposed hyper-heuristic is validated against six well known combinatorial optimization problem, with very different landscapes, provided by the HyFlex software. Empirical results comparing the proposed hyper-heuristic with state of the art hyper-heuristics, conclude that the proposed hyper-heuristic generalizes well across all domains and achieves competitive, if not superior, results for several instances on all domains.

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“…In this work, the adaptive memory mechanism (following the approach in [47], [48]) contains a collection of both high quality and diverse solutions, which are updated as the algorithm progresses. The size of the memory is fixed (equal to the number of acceptance criteria, which is 8).…”

Section: B Hybrid Grammatical Evolution Hyper-heuristic and Adaptivementioning

confidence: 99%

“…In this work, the quality represents the penalty cost which calculates the number of soft constraint violations (see Sections V-B and V-C). The diversity is measured using entropy information theory (1), (2) as follows [47], [48]: 2) Where -eij is the frequency of allocating object i to location j.…”

Section: B Hybrid Grammatical Evolution Hyper-heuristic and Adaptivementioning

confidence: 99%