### Table 2: Experimental results with 960.000 tour con- struction on ry48p.atsp. quality is the percentage deviation from the optimal solution. Ant Colony Sys- tem 15 runs, others 25 runs.

1998

"... In PAGE 3: ...ities become less pronounced, i.e., smoother. A major advantage of smoothing is that it makes MMAS more robust against premature conver- gence for a broader range of parameter values (es- pecially ). In Table2 we present the best aver- age tour quality obtained with several extensions of AS, including Ant Colony System (ACS) [6], the so far best improvement over AS. It can be noted that MMAS with additional trail smooth- ing (MMAS+sm) performs best.... ..."

### Table 2: Comparison with ILOG Solver and Ant-P

2001

"... In PAGE 4: ... Let us now compare with a constraint programming system (ILOG solver) and an ant colony optimization method (Ant-P solver), both timings (in seconds) are taken from [23] and divided by a factor 7 corresponding to the SPECint 95 ratio be- tween the processors. Table2 clearly show that adap- tive search is much more performant on this benchmark, which might not be very representative of real-life ap- plications but is a not-to-be-missed CSP favorite.... ..."

Cited by 25

### Table 1 shows the results for the average delay experienced per packet, overall throughput of the network during simulations and the overhead caused by the ants travelling in the network for improved antnet [9], antnet with evaporation [7] and for antnet employing multiple ant colonies. The lowest delay experienced by the data packets is for the antnet employing evaporation, but at marginally higher agent overhead and lower throughput. Higher throughput has been achieved by the multiple ant colonies since there are more than one optimal paths that can be exploited by the ant colonies at any given time. In another word, when a path become optimal for a colony it becomes congested, therefore other colony needs to find another optimal path. Thus, the algorithm can achieve better load balancing and higher throughput.

"... In PAGE 3: ... Table1 : Comparison of multiple antnet colonies, evaporation and improved antnet. ... ..."

### Table 2: Technology Mapping results

"... In PAGE 8: ... The results show that the Boolean approach reduces the number of matching algorithm calls, nd smaller area circuits in better CPU time, and reduces the initial network graph because generic 2-input base function are used. Table2 presents a comparison between SIS and Land for the library 44-2.genlib, which is distributed with the SIS package.... ..."

### Table 6. Structure of the ant colony optimization algorithm. Initialize pheromone trails While stopping criterion is not satisfied Generate a population P of p solutions For each si 2 P

2006

"... In PAGE 11: ... The di erence between the EXACT-cost, the VRPSD-cost and the TSP-cost implementations concerns only to the local search procedure adopted. Ant colony optimization The ant colony optimization algorithm considered is described in Table6 . The pheromone trail is initialized to 0 = 0:5 on every arc.... ..."

### Table 4. Comparison between different ant colony approaches.

"... In PAGE 12: ...as set equal to 0.3. Again, each test case was solved twenty times. Table4 shows the results for the thirteen test cases and compares them with the MMAS-ET approach. Surprisingly, the simple AS like approach ANTCOL- ET outperformed the MMAS-ET for some test cases.... ..."

### Table 1: Comparison Between all the Strategies Scheduling p.a. FIFS FDFS Ants

"... In PAGE 6: ... They show the number of orders with a specific delay d: if the orders were delivered earlier, d lt; 0; if they were delivered at the correct date, d = 0; and if they were delivered with some delay, d gt; 0. Table1 quantifies this analysis in number of orders and also indicates the spread for each method in number of days. What can be interpreted from the results is that the ant colonies algorithm yields the maximum number of deliveries on the correct date (#d = 0), and the minimum number of delayed orders (#d gt; 0).... ..."

### Table 1. Comparison of results for various approaches.

"... In PAGE 8: ... 4. Numerical Results Table1 compares the balance and uniformity (t,s) of (n,2) de Bruijn sequences... In PAGE 9: ... In the case of Algorithm II, the characteristics of the sequences obtained by the optimal mappings with respect to both balance and uniformity criteria are shown. ------------------------- Table1 goes here ------------------------- In Table 1, we observe that: 1. Although Algorithm I generates sequences with optimal uniformity (minimum s), the corresponding balance criterion t is rather large.... In PAGE 9: ... In the case of Algorithm II, the characteristics of the sequences obtained by the optimal mappings with respect to both balance and uniformity criteria are shown. -------------------------Table 1 goes here ------------------------- In Table1 , we observe that: 1. Although Algorithm I generates sequences with optimal uniformity (minimum s), the corresponding balance criterion t is rather large.... ..."

### Table 1. Performance Characteristics of Different AM Implementations

1997

"... In PAGE 7: ...ficient, buffered writes in the SCI DSM only. Performance measurements on the UCSB SCI cluster show competitive performance behavior of the SCI AM system ( Table1 ). Our own implementation, depicted in the first row of Table 1, adds little over- head to the raw latency of 9.... ..."

Cited by 13

### Table 1: Comparing Ant System results with the random sampling

"... In PAGE 6: ... 23000 24000 25000 26000 27000 28000 0 10 20 30 40 50 best execution time found (in # of cycles) number of iterations typical ant search result ( ant number = 5, iteration number = 50) DAG5-62 Figure 4: A typical run of ant search We also evaluate the capability of the algorithm with re- gard to discovering the optimal partition. Table1 shows a comparison between the proposed algorithm and random sampling. The first column gives the testing case index.... ..."