1 greedy randomized adaptive search and variable neighbourhood search for the minimum labelling...

1

Greedy Randomized Adaptive Search and Variable Neighbourhood Search for the minimu

m labelling spanning tree problem

Kuo-Hsien Chuang 2008/11/05

2

Introduction

Output graphFitness = 2

Input graph

3

Literature review• Maximum vertex covering algorithm

4

Literature review

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

5

Exploited metaheuristics

• MGA

6

Exploited metaheuristics• MGA

7

Exploited metaheuristics

• MGA

8

Exploited metaheuristics• GRASP

9

Exploited metaheuristics

10

Exploited metaheuristics

11

Exploited metaheuristics

12

Exploited metaheuristics• VNS

13

Exploited metaheuristics

14

Exploited metaheuristics

15

Exploited metaheuristics

16

Computational results

17

18

19

20

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.