Random generation and optimal solution

Hello all

I used the random generation equation to generate a random solution for a meta-heuristic algorithm. Now I try to choose the best optimal solution among the solutions I get and use this optimal solution for another equation. But to do it? Thank you for your help

Answer 1

[Harsh]

Hi @Hazha Yahia,

To select the best optimal solution from randomly generated candidates in a meta-heuristic algorithm:

1. Define the Objective Function: Clearly specify what you're optimizing (minimize or maximize). Incorporate any constraints directly into this function or apply penalties for violations.
2. Generate Diverse Solutions: Use your random generation method to create a wide range of initial solutions. Diversity helps explore the solution space effectively and avoids premature convergence.
3. Evaluate Each Solution: Apply the objective function to each candidate to compute its fitness.
4. Select the Best Solution: Track the solution with the best fitness (lowest for minimization, highest for maximization). Optionally, repeat the process over several iterations using meta-heuristics like Genetic Algorithms or Simulated Annealing for better results.
5. Use the Optimal Solution: Once identified, use this best solution in the next equation or application step.

function findBest(solutions, mode):  // mode = "min" or "max"
    best = None
    bestFitness = +∞ if mode == "min" else -∞

    for s in solutions:
        f = evaluate(s)
        if (mode == "min" and f < bestFitness) or (mode == "max" and f > bestFitness):
            bestFitness = f
            best = s
    return best

    

I hope this resolves your query!