A ε-indicator-based shuffled frog leaping algorithm for many-objective optimization problems

Many-objective optimization problems take challenges to multi-objective evolutionary algorithms. A number of nondominated solutions in population cause a difficult selection towards the Pareto front. To tackle this issue, a series of indicator-based multi-objective evolutionary algorithms (MOEAs) have been proposed to guide the evolution progress and shown promising performance. This paper proposes an indicator-based many-objective evolutionary algorithm called e-indicator-based shuffled frog leaping algorithm (epsilon-MaOSFLA), which adopts the shuffled frog leaping algorithm as an evolutionary strategy and a simple and effective e-indicator as a fitness assignment scheme to press the population towards the Pareto front. Compared with four state-of-the-art MOEAs on several standard test problems with up to 50 objectives, the experimental results show that epsilon-MaOSFLA outperforms the competitors.