You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
Copy file name to clipboardExpand all lines: pymoors/docs/index.md
+10-10Lines changed: 10 additions & 10 deletions
Display the source diff
Display the rich diff
Original file line number
Diff line number
Diff line change
@@ -34,14 +34,14 @@ A concise index of the currently available algorithms.
34
34
35
35
| Algorithm | Description |
36
36
|---|---|
37
-
|[NSGA-II](../user_guide/algorithms/nsga2.md)| Baseline Pareto-based MOEA with fast non-dominated sorting and crowding distance. Robust, widely used for 2–3 objectives. |
38
-
|[NSGA-III](../user_guide/algorithms/nsga3.md)| Many-objective extension of NSGA-II using reference points to maintain diversity and guide convergence. |
39
-
|[IBEA](../user_guide/algorithms/ibea.md)| Indicator-Based EA that optimizes a quality indicator (e.g., hypervolume/ε-indicator) to drive selection. |
40
-
|[SPEA-II](../user_guide/algorithms/spea2.md)| Strength Pareto EA with enhanced fitness assignment, density estimation (k-NN), and external archive. |
41
-
|[AGEMOEA](../user_guide/algorithms/agemoea.md)| Approximation-guided MOEA that directly improves the Pareto-front approximation via set-level indicators. |
42
-
|[RNSGA-II](../user_guide/algorithms/rnsga2.md)| Reference-point oriented NSGA-II variant; biases the search toward regions of interest while preserving diversity. |
43
-
|[REVEA](../user_guide/algorithms/revea.md)| Reference vector/region–guided evolutionary algorithm using directional vectors to balance diversity and convergence. |
44
-
|[Custom Defined Algorithms](../user_guide/algorithms/custom/custom.md)| User defined algorithms by defining selection and survival operators|
37
+
|[NSGA-II](user_guide/algorithms/nsga2.md)| Baseline Pareto-based MOEA with fast non-dominated sorting and crowding distance. Robust, widely used for 2–3 objectives. |
38
+
|[NSGA-III](user_guide/algorithms/nsga3.md)| Many-objective extension of NSGA-II using reference points to maintain diversity and guide convergence. |
39
+
|[IBEA](user_guide/algorithms/ibea.md)| Indicator-Based EA that optimizes a quality indicator (e.g., hypervolume/ε-indicator) to drive selection. |
40
+
|[SPEA-II](user_guide/algorithms/spea2.md)| Strength Pareto EA with enhanced fitness assignment, density estimation (k-NN), and external archive. |
41
+
|[AGEMOEA](user_guide/algorithms/agemoea.md)| Approximation-guided MOEA that directly improves the Pareto-front approximation via set-level indicators. |
42
+
|[RNSGA-II](user_guide/algorithms/rnsga2.md)| Reference-point oriented NSGA-II variant; biases the search toward regions of interest while preserving diversity. |
43
+
|[REVEA](user_guide/algorithms/revea.md)| Reference vector/region–guided evolutionary algorithm using directional vectors to balance diversity and convergence. |
44
+
|[Custom Defined Algorithms](user_guide/algorithms/custom/custom.md)| User defined algorithms by defining selection and survival operators|
45
45
46
46
## Introduction to Multi-Objective Optimization
47
47
@@ -69,7 +69,7 @@ Unlike single-objective optimization, here we seek to optimize *all* objectives
69
69
70
70
## Quickstart
71
71
72
-
The well known [ZTD3](../user_guide/algorithms/nsga2.md#zdt3-problem) problem solved with the [NSGA-II](../user_guide/algorithms/nsga2.md) algorithm!
72
+
The well known [ZTD3](user_guide/algorithms/nsga2.md#zdt3-problem) problem solved with the [NSGA-II](user_guide/algorithms/nsga2.md) algorithm!
Arya not only captivates with her beauty, but she also misbehaves in the most adorable way possible. This problem serves as a reminder that sometimes the optimal solution is as heartwarming as it is delightfully mischievous.
0 commit comments