### Table 3. Optimal design search using the new algorithm.

1991

"... In PAGE 11: ...5, which correspond to maximum values expected for each of the individual metrics. Table3 presents the results for the robust antenna design search. In iterations 1 to 102, Nk changed from 3 to 22.... ..."

Cited by 1

### Table 1: Results of the search algorithm. For each case, optimal parameters are indicated.

"... In PAGE 7: ... hamatum raid patterns. See Table1 for parameter values. which would correspond to Eciton hamatum colonies, the search algorithm typically starts nding poor- t solutions with random walk behaviour for individual ants (as we can see in gure 2.... In PAGE 8: ...o Eciton burchelli colonies. Again, the best solution found is consistent with E. burchelli raid patterns. See Table1 for parameter values. 0 10 20 30 40 Generations 0.... In PAGE 8: ... burchelli. See Table1... ..."

### Table 6: Performance of the Tabu Search and Ant Colony Optimization algorithms on real-world instances. Instance Tabu Search Ant Colony Optimization

"... In PAGE 34: ... 6.3 Results on real-world instances In Table6 we give the results obtained on instances derived from the real- world transportation problem. In this case too we can note a difference in the performance of the two algorithms.... ..."

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### Table 3: Performance of the Tabu Search and Ant Colony Optimization algorithms on instances from the CVRP litera- ture.

"... In PAGE 30: ... 6.2 Results on randomly created instances In Table3 we present the results obtained by running our algorithms on in- stances derived from the CVRP literature. The rst columns give the name of the original instance, the number of customers, the class and the total number of items, respectively.... ..."

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### Table 5: Meta-algorithmic optimal parameter search and task assignment run-times.

### Table 1 shows the time taken to compute the optimal paths for different Manhattan distance values using the general search algorithm.

"... In PAGE 4: ... Table1 : Time Complexity using the general search algorithm. The general search algorithm fails when the distance between start patch and target patch exceeds a Manhattan distance of 55.... ..."

### Table 2 Cell level performance of the statistical optimization table structure understanding algorithm with line search on real data set and whole data set

2004

"... In PAGE 15: ...48%) (0.00%) are shown in Table2 . Comparing Tables 1 and 2, we will see the line search results are a little better but there is no signiFFcant diVTerence.... ..."

### Table 3.1: Possible outcomes of each iteration of a search algorithm. z is the current upper bound on the optimal value.

2000

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### Table 5 shows the time taken to compute optimal paths for different Manhattan distances after adding the additional heuristic to the general search algorithm.

"... In PAGE 4: ... Table5 : Time Complexity using general search algorithm after adding the additional heuristic. Comparing the values in table 5 and table 1, we see that adding this heuristic has certainly improved the performance of the general search algorithm by decreasing the time taken to compute paths.... ..."