To select the best optimal solution from randomly generated candidates in a meta-heuristic algorithm:
- 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.
- 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.
- Evaluate Each Solution: Apply the objective function to each candidate to compute its fitness.
- 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.
- 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!
