others.rst 1.2 KB

1234567891011121314151617181920
  1. Implementation examples
  2. =======================
  3. Within the API of **Macop**, you can find an implementation of The Multi-objective evolutionary algorithm based on decomposition (MOEA/D) is a general-purpose algorithm for approximating the Pareto set of multi-objective optimization problems.
  4. It decomposes the original multi-objective problem into a number of single-objective optimization sub-problems and then uses an evolutionary process to optimize these sub-problems simultaneously and cooperatively.
  5. MOEA/D is a state-of-art algorithm in aggregation-based approaches for multi-objective optimization.
  6. .. image:: ../_static/documentation/search_space_moead.png
  7. :width: 45 %
  8. :align: center
  9. As illustrated below, the two main objectives are sub-divised into 5 single-objective optimization sub-problems in order to find the Pareto front.
  10. - ``macop.algorithms.multi.MOSubProblem`` class defines each sub-problem of MOEA/D.
  11. - ``macop.algorithms.multi.MOEAD`` class exploits ``MOSubProblem`` and implements MOEA/D using weighted-sum of objectives method.
  12. An example with MOEAD for knapsack problem is available in knapsackMultiExample.py_.
  13. .. _knapsackMultiExample.py: https://github.com/jbuisine/macop/blob/master/examples/knapsackMultiExample.py