Multiobjective optimization using differential evolution for real‐world portfolio optimization

Portfolio optimization is an important aspect of decision‐support in investment management. Realistic portfolio optimization, in contrast to simplistic mean‐variance optimization, is a challenging problem, because it requires to determine a set of optimal solutions with respect to multiple objectives, where the objective functions are often multimodal and non‐smooth. Moreover, the objectives are subject to various constraints of which many are typically non‐linear and discontinuous. Conventional optimization methods, such as quadratic programming, cannot cope with these realistic problem properties. A valuable alternative are stochastic search heuristics, such as simulated annealing or evolutionary algorithms. We propose a new multiobjective evolutionary algorithm for portfolio optimization, which we call DEMPO–Differential Evolution for Multiobjective Portfolio Optimization. In our experimentation, we compare DEMPO with quadratic programming and another well‐known evolutionary algorithm for multiobjective optimization called NSGA‐II. The main advantage of DEMPO is its ability to tackle a portfolio optimization task without simplifications, while obtaining very satisfying results in reasonable runtime.