### Table 11. Parameters for ant colony optimization. parameter range selected value

2006

### 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.... ..."

Cited by 1

### Table 1 Results of an ant colony for TSP composed by 10, 20 and 30 ants on 50 cities for 2500 iterations

"... In PAGE 5: ...q. (5) and the starting node for each ant is selected randomly. = ) , ( 2 0 s r n d t where n is the number of nodes in G (5) Picture 6 Initial value for (r, s) In order to test the proposed system we launched a colony composed by 10, 20 and 30 ants on a graph containing 50 cities for 2500 iterations. The results we found, shown in the Table1 , matches with the ones we expected and agrees with the results presented in [1]. Table 1 Results of an ant colony for TSP composed by 10, 20 and 30 ants on 50 cities for 2500 iterations ... ..."

### TABLE I A NON-EXHAUSTIVE LIST OF SUCCESSFUL ANT COLONY OPTIMIZATION ALGORITHMS (IN CHRONOLOGICAL ORDER).

2006

Cited by 3

### 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.... ..."

Cited by 1

### Table 5: Average values per class (seven instances per line). Instance Tabu Search Ant Colony Optimization

"... In PAGE 33: ...6 1899.8 In Table5 we present the average performances for each class used for the generation of the demands. We can see how the average solution value found by the ACO always outperforms the one obtained by the Tabu Search.... ..."

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### Table 1. User specified ant colony parameters.

2004

"... In PAGE 4: ... Parameter values set for the colony apply to every ant (except the one that specifes colony size). Table1 shows all colony parameters. All but the last five listed are influential in navigation while the final five dictate how the ant draws its mark.... ..."

Cited by 3

### Table 1: Ant Problem

1997

"... In PAGE 3: ...Our GP system was set up to be the same as given in [Koza, 1992, pages 147{155] except the populations were allowed to continue to evolve even after an ant succeeded in traversing the whole trail, programs are restricted to a maximum length of 500 rather than to a maximum depth of 17, each crossover produces one child rather than two, tournament selection was used and the ants were allowed 600 operations (Move, Left, Right) to complete the trail. The details are given in Table1 , parameters not shown are as [Koza, 1994, page 655]. On each version of the problem 50 independent runs were conducted.... In PAGE 11: ... While the expected size of crossover fragments depends in detail upon the trees selected as parents and the relative weighting applied to functions and terminals cf. Table1 , typically both the inserted subtree and the subtree it replaces consist of a function and its leafs. Since these subtrees are short together they produce a small change in total size.... ..."

Cited by 52

### 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 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.... ..."