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Table 3. Optimal design search using the new algorithm.

in Problems easy for tree-decomposable graphs
by Punit Prakash, Geng Deng, Mark C Converse, John G, David M Mahvi, Michael C Ferris 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 Pattern Formation and Optimization in Army Ant Raids
by Ricard V. Sole, Eric Bonabeau, Jordi Delgado, Pau Fernández, Jesus Marín
"... 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 Metaheuristics for the vehicle routing problem with loading constraints
by Karl F. Doerner, Guenther Fuellerer, Manfred Gronalt, Richard F. Hartl, Manuel Iori
"... 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 1. Comparison of Allocation Methods

in Cost of Precision
by K. W. Chase, W. H. Greenwood 1987

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

in Metaheuristics for the vehicle routing problem with loading constraints
by Karl F. Doerner, Guenther Fuellerer, Manfred Gronalt, Richard F. Hartl, Manuel Iori
"... 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.

in System-Level Synthesis of Low-Power Hard Real-Time Systems
by Darko Kirovski, Miodrag Potkonjak

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

in OPTIMAL PATH PLANNING USING AN IMPROVED A * ALGORITHM FOR HOMELAND SECURITY APPLICATIONS
by unknown authors
"... 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

in
by Yalin Wang A, Ihsin T. Phillips B, Robert M. Haralick C 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.

in Integration of Constraint Programming and Integer Programming for Combinatorial Optimization
by Greger Ottosson 2000
Cited by 1

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 OPTIMAL PATH PLANNING USING AN IMPROVED A * ALGORITHM FOR HOMELAND SECURITY APPLICATIONS
by unknown authors
"... 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.... ..."
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