**9. Acknowledgments**

22 Grid Computing

5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100

Test Cases

(c) *CAN*(4, *k*, 3)

In large problem domains, testing is limited by cost. Every test adds to the cost, so CAs are an

Simulated annealing (SA) is a general-purpose stochastic optimization method that has proven to be an effective tool for approximating globally optimal solutions to many types of NP-hard combinatorial optimization problems. But, the sequential implementation of SA algorithm has a slow convergence that can be improved using Grid or parallel

This work focused on constructing ternary CAs with a new approach of SA, which integrates

2. A carefully designed composed neighborhood function which allows the search to quickly reduce the total cost of candidate solutions, while avoiding to get stuck on some local

3. An effective cooling schedule allowing our SA algorithm to converge faster, producing at

1. An efficient method to generate initial solutions with maximum Hamming distance.

Fig. 6. Graphical comparison of the performance among TConfig, IPOG-F and our SA to

N

TConfig IPOG−F Our SA

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Test Cases

(b) *CAN*(3, *k*, 3)

**8. Conclusions**

implementations

minimal.

attractive option for testing.

N

TConfig IPOG−F Our SA

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Test Cases

construct ternary CAs when 5 ≤ *k* ≤ 100 and 2 ≤ *t* ≤ 4.

three key features that importantly determines its performance:

the same time good quality solutions.

N

TConfig IPOG−F Our SA

(a) *CAN*(2, *k*, 3)

The authors thankfully acknowledge the computer resources and assistance provided by Spanish Supercomputing Network (TIRANT-UV). This research work was partially funded by the following projects: CONACyT 58554, Calculo de Covering Arrays; 51623 Fondo Mixto CONACyT y Gobierno del Estado de Tamaulipas.
