Portfolio selection is one of the most vital financial problems in
literature. The studied problem is a nonlinear multi-objective problem
which has been solved by a variety of heuristic and metaheuristic
techniques. In this article, a metaheuristic optimiser, the multiobjective
water cycle algorithm (MOWCA), is represented to find
efficient frontiers associated with the standard mean-variance (MV)
portfolio optimisation model. The inspired concept of WCA
is based on the simulation of water cycle process in the nature.
Computational results are obtained for analyses of daily data for
the period January 2012 to December 2014, including S&P100 in
the US, Hang Seng in Hong Kong, FTSE100 in the UK, and DAX100
in Germany. The performance of the MOWCA for solving portfolio
optimisation problems has been evaluated in comparison with
other multi-objective optimisers including the NSGA-II and multiobjective
particle swarm optimisation (MOPSO). Four well-known
performance metrics are used to compare the reported optimisers.
Statistical optimisation results indicate that the applied MOWCA is an
efficient and practical optimiser compared with the other methods
for handling portfolio optimisation problems
