ASCHEA: new results using adaptive segregational constraint handling

Some new results obtained using ASCHEA are presented after extending the penalty function and introducing a niching technique with adaptive radius to handle multimodal functions and a new equality constraint handling strategy is proposed.

Abstract

ASCHEA is an adaptive algorithm for constrained optimization problem based on a population level adaptive penalty function to handle constraints, a constraint-driven mate selection for recombination, and a segregational selection that favors a given number of feasible individuals. In this paper, we present some new results obtained using ASCHEA after extending the penalty function and introducing a niching technique with adaptive radius to handle multimodal functions. Furthermore, we propose a new equality constraint handling strategy. The idea is to start, for each equality, with a large feasible domain and to reduce it progressively along generations, in order to bring it as close as possible to null measure domain. Two approaches are proposed and experimented, the first based on dynamic adjustment and the second based on adaptive adjustment.