Multi-objective portfolio optimisation is a critical problem researched
across various fields of study as it achieves the objective of maximising the
expected return while minimising the risk of a given portfolio at the same
time. However, many studies fail to include realistic constraints in the model,
which limits practical trading strategies. This study introduces realistic
constraints, such as transaction and holding costs, into an optimisation model.
Due to the non-convex nature of this problem, metaheuristic algorithms, such as
NSGA-II, R-NSGA-II, NSGA-III and U-NSGA-III, will play a vital role in solving
the problem. Furthermore, a learnheuristic approach is taken as surrogate
models enhance the metaheuristics employed. These algorithms are then compared
to the baseline metaheuristic algorithms, which solve a constrained,
multi-objective optimisation problem without using learnheuristics. The results
of this study show that, despite taking significantly longer to run to
completion, the learnheuristic algorithms outperform the baseline algorithms in
terms of hypervolume and rate of convergence. Furthermore, the backtesting
results indicate that utilising learnheuristics to generate weights for asset
allocation leads to a lower risk percentage, higher expected return and higher
Sharpe ratio than backtesting without using learnheuristics. This leads us to
conclude that using learnheuristics to solve a constrained, multi-objective
portfolio optimisation problem produces superior and preferable results than
solving the problem without using learnheuristics