Pixelatory/Metaheuristic-Implementations

Metaheuristic Implementations

Implementation of various metaheuristics using Python. Not all may be completed, so double check before using.

Requirements are listed in the requirements.txt file.

CMODE

A cooperative multi-objective differential evolution algorithm, as explained in:

Wang, Jiahai, Weiwei Zhang, and Jun Zhang. "Cooperative differential evolution with multiple populations for multiobjective optimization." IEEE Transactions on Cybernetics 46.12 (2015): 2848-2861.

Note: improvements might still be made around the storage of solutions within the archive.

MOPSO

A multi-objective particle swarm optimizer, as explained in:

Coello, CA Coello, and Maximino Salazar Lechuga. "MOPSO: A proposal for multiple objective particle swarm optimization." Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No. 02TH8600). Vol. 2. IEEE, 2002.

NSCCDE

A multi-objective differential evolution algorithm, as explained in:

Zhang, Zi-Hui, et al. "A non-dominated sorting cooperative co-evolutionary differential evolution algorithm for multi-objective layout optimization." IEEE Access 5 (2017): 14468-14477.

CMOPSO (INCOMPLETE)

An attempted implementation of a competitive multi-objective particle swarm optimizer, as explained in:

Zhang, Xingyi, et al. "A competitive mechanism based multi-objective particle swarm optimizer with fast convergence." Information Sciences 427 (2018): 63-76.

The paper itself is vague on the implementation of the polynomial mutation operator. Several papers are listed, each of which has a different mutation, making it difficult to determine the correct one. After testing with PyMoo it doesn't appear like I've done this correctly. Results express the population has a similar shape of pareto front to the known optimal, but is far from it.

PlatEMO has their CMOPSO working, and I attempted to copy it, but cannot receive the results that they get.