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A DEA-based MOEA/D algorithm for portfolio optimization
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DOI：10.1007/s10586-018-2316-7.png)
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In this paper, we present a multi-objective genetic algorithm DEA-MOEA/D by integrating decomposition method and DEA (Data Envelopment Analysis) approach. The initial solutions are generated by the DEA approach. Difference operators are adopted as the crossover operator of the parent. We adopt the test functions and portfolio optimization problems to compare the performance of DEA-MOEA/D, FDH-MOGA, MOEA/D and NSGA II. The results show that DEA-MOEA/D performs better than other three algorithms, not only for test functions, but for the portfolio optimization.
Keyword:
Portfolio optimization
Data envelopment analysis
Multi-objective evolutionary algorithm
Cardinality constraints
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