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Greedy Randomized Adaptive Search and Variable Neighbourhood Search for the minimu

m labelling spanning tree problem

Kuo-Hsien Chuang 2008/11/05

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Introduction

Output graphFitness = 2

Input graph

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Literature review• Maximum vertex covering algorithm

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Literature review

• MVCA applying Pilot method– Let C = empty set of labels– Set C = {all c 屬於 (L – C), min(comp(C + c))}

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Exploited metaheuristics

• MGA

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Exploited metaheuristics• MGA

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Exploited metaheuristics

• MGA

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Exploited metaheuristics• GRASP

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Exploited metaheuristics

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Exploited metaheuristics

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Exploited metaheuristics

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Exploited metaheuristics• VNS

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Exploited metaheuristics

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Exploited metaheuristics

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Exploited metaheuristics

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Computational results

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Conclusion

• All the results allow us to state that VNS and GRASP are fast and extremely effective metaheuristics for the MLST problem

• Future research :

an algorithm based on Ant Colony Optimisation (ACO) is currently under study in order to try to obtain a larger diversification capability by extending the current greedy MVCA local search.