This project is an implementation of the Cuckoo Optimization Algorithm (COA) in MATLAB. The COA is a type of nature-inspired algorithm that is used for solving optimization problems, based on the brood parasitism of some cuckoo species.
In this implementation, the algorithm simulates a cuckoo population where each cuckoo lays eggs in a random place in the solution space (the habitat). The value of the objective function for each egg (solution) determines its fitness to survive. The algorithm includes steps to adjust the solutions and search space based on cuckoo behaviors such as egg laying and flight toward better solutions.
The main script runs the COA algorithm, which includes:
- Initialization of parameters such as the cuckoo population, egg numbers, accuracy, and others.
- Initialization of the cuckoo population.
- Iteration through the main loop of the COA, which includes steps for egg laying, evaluation of new positions, survival of the fittest eggs, and movement of cuckoos towards the best solutions.
- The loop continues until a stopping criteria (such as a maximum number of iterations or minimum variance) is reached.
- The cost function used for the optimization process is a separate function (
model.mdl) that can be replaced to fit your specific needs. This function is called in each iteration to evaluate the current solutions (cuckoos).