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10. Implementation examples

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. 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. MOEA/D is a state-of-art algorithm in aggregation-based approaches for multi-objective optimization.

../_images/search_space_moead1.png

As illustrated below, the two main objectives are sub-divised into 5 single-objective optimization sub-problems in order to find the Pareto front.

  • macop.algorithms.multi.MOSubProblem class defines each sub-problem of MOEA/D.

  • macop.algorithms.multi.MOEAD class exploits MOSubProblem and implements MOEA/D using weighted-sum of objectives method.

An example with MOEAD for knapsack problem is available in knapsackMultiExample.py.