Handling multiobjectives with adaptive mutation based ε-dominance differential evolution

Differential evolution (DE) is well known as a powerful and efficient population-based stochastic real-parameter optimization algorithms over continuous space. DE is recently shown to outperform several well-known stochastic optimization methods in solving multi-objective problems. Nevertheless, its performance is still limited in finding a uniformly distributed and near optimal Pareto fronts. To alleviate such limitations, this paper introduces an adaptive mutation operator to avoid premature of convergence by adaptively tuning the mutation scale factor F, and adopts ε-dominance strategy to update the archive that stores the nondominated solutions. Experiments based on five widely used multiple objective functions are conducted. Simulation results demonstrate the effectiveness of our proposed approach with respect to the quality of solutions in terms of the convergence and diversity of the Pareto fronts.