Improved ant colony optimization algorithms for continuous ... - CoDE

3.4 Conclusions 25
G−CMA−ES (5 optima)
1e−14 1e−04 1e+06
Tuned IPSO−Powell (10 optima)
1e−14 1e−04 1e+06
Tuned IPSO−mtsls1 (15 optima)
1e−14 1e−04 1e+06
Median Errors
IACOr−mtsls1
Win 21
Draw 3
Lose 16
1e−14 1e−04 1e+06
IACOr−mtsls1 (16 optima)
Median Errors
IACOr−mtsls1
Win 22
Draw 11
Lose 7
1e−14 1e−04 1e+06
IACOr−mtsls1 (16 optima)
Median Errors
IACOr−mtsls1
Win 16
Draw 17
Lose 7
1e−14 1e−04 1e+06
IACOr−mtsls1 (16 optima)
G−CMA−ES (3 optima)
1e−14 1e−04 1e+06
Tuned IPSO−Powell (6 optima)
1e−14 1e−04 1e+06
Tuned IPSO−mtsls1 (8 optima)
1e−14 1e−04 1e+06
Average Errors
IACOr−mtsls1
Win 24
Draw 1
Lose 15
1e−14 1e−04 1e+06
IACOr−mtsls1 (14 optima)
Average Errors
IACOr−mtsls1
Win 27
Draw 6
Lose 7
1e−14 1e−04 1e+06
IACOr−mtsls1 (14 optima)
Average Errors
IACOr−mtsls1
Win 24
Draw 9
Lose 7
1e−14 1e−04 1e+06
IACOr−mtsls1 (14 optima)
Figure 3.2: The correlation plot between IACOR-Mtsls1 and G-CMA-ES,
IPSO-Powell and IPSO-Mtsls1 over 40 functions. Each point represents a
function. The points on the left part of correlation plot illustrate that on
those represented functions, IACOR-Mtsls1 obtains better results than the
other algorithm.