Heterogeneous operator mechanism and stochastic configuration network-enhanced large-scale multi-objective evolutionary algorithm

Abstract Large-scale multi-objective optimization problems are prevalent in practical applications. Consequently, researchers have proposed numerous large-scale multi-objective evolutionary algorithms (LMOEAs) to address these problems. Among them, neural network (NN)-based LMOEAs have garnered significant attention owing to their superior search efficiency. Regrettably, existing NN-based LMOEAs are limited by a single source of training data and pre-fixed network topology, resulting in insufficient generalization and weak adaptability. We propose a heterogeneous operator mechanism and stochastic configuration network-enhanced LMOEA (HMSCN-LMOEA) to overcome these issues. Specifically, a heterogeneous operator mechanism is introduced to address the limited data sources in NN model training, thereby effectively enhancing the quality of the training set. A stochastic configuration network (SCN) model with dynamic structural adaptability is designed to replace the NN model with a pre-fixed network topology, thereby enhancing the algorithm’s adaptability. Finally, to evaluate the algorithm’s performance, four large-scale multi-objective optimization problems—UF (Unconstrained Problem), WFG (Walking Fish Group), LSMOP (Large-Scale Multi-Objective Test Problem), and ZCAT (Zapotecas, Coello, Aguirre, and Tanaka)—are adopted. Experimental results based on Inverted generational distance (IGD), Inverted generational distance plus (IGD+), Spacing, and hypervolume (HV) metrics demonstrate the significant advantages and competitiveness of HMSCN-LMOEA over state-of-the-art LMOEAs. Furthermore, HMSCN-LMOEA is employed to solve the cloud task scheduling problem across different task scales, and the experimental results show that it achieves the top rank in 75% of the evaluated scenarios, further demonstrating its promising potential.