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Fix docs for optimizers.md

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NanuSai
2021-06-10 09:53:25 +05:30
parent 5adfba0bbd
commit a068a6e40d
1 changed files with 6 additions and 6 deletions
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doc/optimizers.md
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@@ -1568,8 +1568,8 @@ optimizer.Optimize(f, coordinates);
MOEA/D-DE (Multi Objective Evolutionary Algorithm based on Decomposition - Differential Evolution) is a multi
objective optimization algorithm. It works by decomposing the problem into a number of scalar optimization
subproblems which are solved simultaneously per generation. MOEA/D in itself is a framework, this particular
algorithm uses Differential Crossover followed by polynomial mutation to create offsprings which are then
decomposed using Tchebycheff's approach. A diversity preserving mechanism is also employed which produces
algorithm uses Differential Crossover followed by Polynomial Mutation to create offsprings which are then
decomposed to form a Single Objective Problem. A diversity preserving mechanism is also employed which encourages
a varied set of solution.
#### Constructors
@@ -1583,7 +1583,7 @@ a varied set of solution.
| `size_t` | **`populationSize`** | The number of candidates in the population. | `150` |
| `size_t` | **`maxGenerations`** | The maximum number of generations allowed. | `1000` |
| `double` | **`crossoverProb`** | Probability that a crossover will occur. | `1.0` |
| `size_t` | **`neighborSize`** | The number of nearest-neighbours to consider for each weight. | `20` |
| `size_t` | **`neighborSize`** | The number of nearest-neighbours to consider per weight vector. | `20` |
| `double` | **`distributionIndex`** | The crowding degree of the mutation. | `20` |
| `double` | **`neighborProb`** | The probability of sampling from neighbor. | `0.9` |
| `double` | **`differentialWeight`** | Amplification factor of the differentiation. | `0.5` |
@@ -1605,7 +1605,7 @@ Attributes of the optimizer may also be changed via the member methods
SchafferFunctionN1<arma::mat> SCH;
arma::vec lowerBound("-10 -10");
arma::vec upperBound("10 10");
MOEAD opt(150, 1000, 1.0, 20, 20, 0.9, 0.5, 2, lowerBound, upperBound);
MOEAD opt(150, 300, 1.0, 20, 20, 0.9, 0.5, 2, lowerBound, upperBound);
typedef decltype(SCH.objectiveA) ObjectiveTypeA;
typedef decltype(SCH.objectiveB) ObjectiveTypeB;
arma::mat coords = SCH.GetInitialPoint();
@@ -1613,7 +1613,7 @@ std::tuple<ObjectiveTypeA, ObjectiveTypeB> objectives = SCH.GetObjectives();
// obj will contain the minimum sum of objectiveA and objectiveB found on the best front.
double obj = opt.Optimize(objectives, coords);
// Now obtain the best front.
std::vector<arma::mat> bestFront = opt.Front();
arma::cube bestFront = opt.ParetoFront();
```
</details>
@@ -1678,7 +1678,7 @@ std::tuple<ObjectiveTypeA, ObjectiveTypeB> objectives = SCH.GetObjectives();
// obj will contain the minimum sum of objectiveA and objectiveB found on the best front.
double obj = opt.Optimize(objectives, coords);
// Now obtain the best front.
std::vector<arma::mat> bestFront = opt.Front();
arma::cube bestFront = opt.Front();
```
</details>