Modal Detection Informed Classification Evaluation via Ensemble Networks for Expensive Constrained Multimodal Optimization.

Abstract

The evaluation of objective and constraint involving expensive simulations or physical experiments with multiple optimal solutions is referred to as expensive constrained multimodal optimization problems (ECMMOPs). Under limited real function evaluations (FEs), it is challenging to find multiple optimal solutions accurately while satisfying constraints. To address these issues, this article studies a self-clustering particle swarm optimization algorithm with modal detection informed classification evaluation (MDICE) to solve ECMMOPs. To deal with multimodality, a surrogate-assisted self-clustering update mechanism is first designed to update individuals in each modality. Following that, a novel modal detection strategy is proposed based on the awareness of fitness landscapes to identify all potential modal seeds. For better utilization of FEs, a modality-guided classification evaluation strategy is designed to efficiently generate infilling samples for each constraint and modality. Moreover, to address the complex constraints, a surrogate-assisted feasibility search strategy is developed to quickly search for feasible solutions at a lower evaluation cost. Experimental results on 33 benchmark functions with various characteristics indicate that MDICE outperforms four state-of-the-art surrogate-assisted evolutionary algorithms.