2.1 Synthetic test results. Average of 100 runs. n: size of the problem, i.e. number of locations; m: number of salesmen;
k: minimum tour length; p: population size; Opt.: best found solution (overall distance); It.: iteration number when the best solution found;t: running time in seconds. . . 26 2.2 Test results using complex operators and initialization.
Aver-age of 100 runs. n: size of the problem, i.e. number of lo-cations; m: number of salesmen; k: minimum tour length; p: population size; Opt.: best found solution (overall distance);
It.: iteration number when the best solution found;t: running time in seconds. . . 26 2.3 Test results using complex operators and initialization.
Aver-age of 10 runs. n: size of the problem, i.e. number of locations;
m: number of salesmen; l: maximum tour length; Best: best found solution (overall distance); Avg: Average of 10 runs. . . 27 2.4 Test results using complex operators and initialization.
Aver-age of 10 runs. n: size of the problem, i.e. number of locations;
m: number of salesmen; Best: best found solution (overall dis-tance); Avg: Average of 10 runs. . . 28 2.5 Part of the industrial problem's distance table - kilometers. . . 33 3.1 The eect of the introduced parameter c3 on the convergence
of PSO . . . 53 3.2 Mathematical equations of the analyzed functions. . . 59 3.3 Test results performing 500 MC simulation, modifying the
weight for the gradient part. Best results are highlighted in each row for the objective values. . . 61 3.4 The parameters (mean and deviation) of the example
distri-bution functions . . . 69 3.5 The analytically calculated example sensitivity matrix . . . 70 3.6 The investigated demand function parameters. . . 73 3.7 The resulted sensitivity matrix in the two-echelon problem. . . 74
4.1 Computational results using synthetic data sets. . . 94
4.2 Comparison to DCI_Closed. . . 95
4.3 Comparing FCPMiner with other relevant methods. . . 98
4.4 Performance test using synthetic data. . . 105
4.5 Test runs using biological data. . . 106
4.6 Number of biclusters showing signicant enrichment in GO categories for our method and the three compared biclustering algorithms. . . 107
4.7 Number of biclusters showing signicant enrichment in KEGG pathways for our method and other biclustering algorithms. . 108
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