A ∊-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 indicatorbased multi-objective evolutionary algorithms (MOEAs) have been proposed to guide the evolution progress and shown promising performance. This paper proposes an indicator-based manyobjective evolutionary algorithm called ε-indicator-based shuffled frog leaping algorithm (ε-MaOSFLA), which adopts the shuffled frog leaping algorithm as an evolutionary strategy and a simple and effective ε-indicator as a fitness assignment scheme to press the population towards the Pareto front. Compared with four stateof-the-art MOEAs on several standard test problems with up to 50 objectives, the experimental results show that ε-MaOSFLA outperforms the competitors.