A two-archive algorithm with decomposition and fitness allocation for multi-modal multi-objective optimization

This paper proposes a two-archive algorithm with decomposition and fitness allocation for multi-modal multi-objective optimization problems which have more than one Pareto-optimal solution set corresponding to the same objective vector. The general framework of proposed method uses two archives, convergence archive (CA) diversity (DA), focus on population, respectively. Both archives are based decomposition-based framework. In CA, population update strategy adopts scheme, is designed according change state during evolution, combining space decision space. DA, we use crowding distance ensure Moreover, different neighborhood criteria used archives. shown not only locate maintain larger number sets, but also obtain good in both spaces. addition, empirically compared five state-of-the-art evolutionary algorithms series test functions. Comparison results show that has better performance competing algorithms.