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A Reference Vector-Guided and Generative Adversarial Network-Driven Competitive Swarm Optimizer for Large-Scale Multi-Objective Optimization
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DOI：10.1109/jas.2026.125849.png)
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Abstract
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Competitive swarm optimizers (CSOs) have demonstrated promising performance in addressing large-scale optimization challenges. However., they are often criticized for inefficient convergence when solving large-scale multi-objective optimization problems (LSMOPs) due to their competition and learning mechanisms being originally designed for single-objective scenarios. To overcome this limitation, this paper proposes a reference vector-guided and generative adversarial network (GAN)-driven CSO variant for LSMOPs. The algorithm introduces a dual-criteria competition mechanism to replace the conventional random pairwise competition, enabling more effective swarm division into winner and loser groups while preserving diverse search directions. Furthermore, it enhances particle update through two comprehensive learning strategies: a reference vector-guided update strategy that directs loser particles toward promising search directions in a self-supervised manner, coupled with a GAN-driven reproduction strategy where a GAN model is trained to capture the distribution patterns of high-performing winner particles, subsequently generating refined candidate solutions with improved quality and diversity. By synergizing the strong exploration of CSO with the efficient exploitation of GAN-based reproduction, the proposed algorithm achieves promising optimization performance. Experimental results demonstrate its superiority over seven state-of-the-art large-scale multi-objective optimization algorithms on both LSMOP benchmark problems and real-world applications.
Keywords:
Competitive swarm optimizer (CSO)
generative adversarial network (GAN)
large-scale optimization
multi-objective optimization
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