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Adversarial autoencoder enhanced evolutionary guidance for multi-objective multi-task optimization
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AI
DOI：10.1016/j.swevo.2026.102335.png)
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Abstract
En 中文
Multi-objective multitasking optimization (MOMT) problems are of significant importance in practical applications, particularly in complex engineering fields. These problems require simultaneously optimizing multiple objectives while effectively managing the interactions and information sharing between tasks. However, existing MOMT evolutionary algorithms often face challenges such as insufficient knowledge transfer, negative transfer issues, and difficulties in maintaining the quality and diversity of solutions. To address these challenges, this paper proposes an adversarial autoencoder (AAE) enhanced evolutionary guidance algorithm for multi-objective multi-task optimization (EMT-AAE). First, a new adversarial autoencoder model is proposed to learn the promising evolutionary information and achieve positive transfer. The evolutionary dynamics from source to target task are dynamically implemented by periodically training the model structure. Next, a new AAE-guided offspring reproduction strategy is proposed to improve the quality of generated solutions. Finally, an adaptive environmental selection strategy is proposed to balance solution diversity and convergence. To validate the effectiveness of the proposed algorithm, experiments are conducted on three multi-objective multi-task benchmark test suites. Compared with other state-of-the-art algorithms, EMT-AAE performs excellently in addressing both classic and complex MOMT optimization problems. Additionally, it shows scalability and practicality in three real-world problems.
Keywords:
Multi-objective multi-task optimization
Adversarial autoencoder
Evolutionary algorithm
Knowledge transfer
Solution diversity
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