Evolutionary algorithm using surrogate models for solving bilevel multiobjective programming problems.

ABSTRACT: A bilevel programming problem with multiple objectives at the leader's and/or follower's levels, known as a bilevel multiobjective programming problem (BMPP), is extraordinarily hard as this problem accumulates the computational complexity of both hierarchical structures and multiobjective optimisation. As a strongly NP-hard problem, the BMPP incurs a significant computational cost in obtaining non-dominated solutions at both levels, and few studies have addressed this issue. In this study, an evolutionary algorithm is developed using surrogate optimisation models to solve such problems. First, a dynamic weighted sum method is adopted to address the follower's multiple objective cases, in which the follower's problem is categorised into several single-objective ones. Next, for each the lea

SUBMITTER: Liu Y 

PROVIDER: S-EPMC7746194 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature