A new metaheuristic algorithm for real-parameter optimization: Natural aggregation algorithm

This paper proposes a new evolutionary algorithm (EA), which is called the natural aggregation algorithm (NAA). NAA is inspired by the collective decision making intelligence of the group-living animals. Distinguished from other EAs, NAA distributes individuals to several sub-populations (called `shelters'), and uses a stochastic migration model to dynamically mitigate the individuals among the shelters. The inter-individual attraction effect and crowding effect are considered in the migration model to balance the exploration and exploitation. In each generation, both of the located search and generalized search are performed simultaneously, and the distributions of the individuals are self-adaptively updated. 7 benchmark functions with different dimensionality settings are used to validate the efficiency of NAA, and the results clearly show that NAA has strong performance for solving the real-parameter optimization problems.