Ensemble of Transfer Techniques via Adaptive Resource Allocation for Multiobjective Multitask Optimization

Abstract

Multiobjective multitask optimization (MMTO) has obtained increasing attention recently, where effective knowledge transfer (KT) is essential for improving optimization effectiveness. Existing KT techniques primarily focus on transferring knowledge derived from individuals or overall population distributions, but have not fully exploited manifold-based information. The effectiveness of using manifold information to guide the search has been demonstrated in single-task multiobjective optimization. To address these challenges, we propose an ensemble-based transfer adaptation framework, named MMTO-ensemble-based transfer adaptation (ETA), which integrates multiple transfer techniques via dynamic resource allocation. Specifically, MMTO-ETA estimates the manifold of the current population for each task and extracts two types of knowledge: structure knowledge (SK) and location knowledge (LK), and incorporates corresponding transfer techniques. The SK-based and LK-based transfer techniques are inherently complementary, each leveraging its distinct strengths to enhance the transfer process. MMTO-ETA continuously evaluates online performance and allocates computational resources among self-evolution, SK-based transfer, and LK-based transfer. This ensures that the most effective approach is prioritized at each iteration, reducing the risk of negative transfer. Extensive experiments on two benchmark suites and a real-world application demonstrate that MMTO-ETA consistently outperforms eleven state-of-the-art algorithms.