Devasena-Valli Dual-Phase Optimization (DVDPO)

This approach alternates between logical refinement (Devasena phase) and exploratory search (Valli phase) which is inspired by kanda puranam

Pseudocode for Devasena-Valli Dual-Phase Optimization (DVDPO).

1. Initialize population randomly within the given bounds.

2. Evaluate fitness of the initial population.

3. Identify the best solution and its fitness.

4. Set parameters:

- alpha (logical reduction factor for Devasena phase)

- beta (exploratory enhancement factor for Valli phase)

- epsilon (minimum step size threshold)

- logical_step (initial logical refinement step size)

- exploratory_step (initial exploratory step size)

5. Repeat for a maximum number of iterations or until convergence:

a. If the current iteration is odd (Devasena phase):

i. Reduce logical_step by alpha.

ii. Break loop if logical_step < epsilon.

iii. Generate new solutions by refining around the best solution:

new_solution = best_solution + logical_step * (random values between -1 and 1).

b. If the current iteration is even (Valli phase):

i. Increase exploratory_step by beta.

ii. Generate new solutions randomly within the entire search space.

c. Apply boundary constraints to the new solutions.

d. Evaluate the fitness of new solutions.

e. Combine old and new populations.

i. Sort the combined population based on fitness.

ii. Retain the top individuals as the new population.

f. Update the global best solution if a better solution is found.

g. Display iteration progress (current iteration and best fitness).

6. Output the best solution and its fitness.