1. A Multimodel Prediction Method for Dynamic Multiobjective Evolutionary Optimization
- Author
-
Miao Rong, Ling Wang, Witold Pedrycz, and Dunwei Gong
- Subjects
Mathematical optimization ,education.field_of_study ,Optimization problem ,Population ,Evolutionary algorithm ,Pareto principle ,02 engineering and technology ,Theoretical Computer Science ,Set (abstract data type) ,Computational Theory and Mathematics ,Convergence (routing) ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,education ,Software ,Selection (genetic algorithm) ,Dynamic testing - Abstract
A large number of prediction strategies are specific to a dynamic multiobjective optimization problem (DMOP) with only one type of the Pareto set (PS) change. However, a continuous DMOP with more than one type of the unknown PS change has been seldom investigated. We present a multimodel prediction approach (MMP) realized in the framework of evolutionary algorithms (EAs) to tackle the problem. In this paper, we first detect the type of the PS change, followed by the selection of an appropriate prediction model to provide an initial population for the subsequent evolution. To observe the influence of MMP on EAs, optimal solutions obtained by three classical dynamic multiobjective EAs with and without MMP are investigated. Furthermore, to investigate the performance of MMP, three state-of-the-art prediction strategies are compared on a large number of dynamic test instances under the same particle swarm optimizer. The experimental results demonstrate that the proposed approach outperforms its counterparts under comparison on most optimization problems.
- Published
- 2020