New Algorithm Tames Negative Transfer in Multi-Objective Multitasking Optimization

Optimization problems rarely exist in isolation. In engineering design, logistics, scheduling, and machine learning, teams of related problems often need to be solved at the same time, and the solutions to one can hold valuable clues for another. A research team at Yanshan University in Qinhuangdao, China, has now introduced a new algorithm that exploits those clues more intelligently than before, using ideas borrowed from manifold learning to decide exactly which knowledge should travel between tasks and which should stay home. The work, published in the International Journal of Machine Learning and Cybernetics, addresses one of the most persistent obstacles in evolutionary multitasking: the risk that sharing information between problems does more harm than good.

The field of evolutionary multitasking grew out of the recognition that many real-world optimization tasks are interrelated. Rather than running a separate evolutionary algorithm for every problem, multitasking algorithms solve several tasks simultaneously within a single framework, allowing candidate solutions to migrate between task-specific populations. When the tasks are similar, this exchange can dramatically accelerate convergence, because a solution that performs well on one problem may already be halfway to a good solution on another. The foundational work on multifactorial evolution by Gupta, Ong, and Feng in 2016 demonstrated the promise of this approach, and a growing body of research has since refined how knowledge moves between tasks.

The catch is negative transfer. When two tasks differ substantially in the structure of their search spaces or the shape of their objective functions, blindly importing solutions from a source task can pull a target population away from its own promising regions. The result is wasted computational effort and, in the worst cases, worse final solutions than a task would achieve on its own. Researchers have proposed a variety of remedies, from adaptive transfer probabilities based on population distribution statistics to explicit mapping techniques that translate solutions between task domains. The new algorithm, called MOMFEA-MTL, combines two complementary mechanisms to attack the problem from both directions: deciding when to transfer, and deciding how to transfer.

The first mechanism is an adaptive knowledge transfer strategy that continuously monitors the historical evolution of each population. Instead of fixing the probability that a solution crosses from one task to another, the algorithm adjusts that probability dynamically based on how well past transfers have served the target task. If incoming solutions have recently improved the target population’s performance, the transfer probability rises; if they have degraded it, the probability falls. This feedback loop suppresses negative transfer without requiring any prior knowledge of how similar the tasks are, which is precisely the information that is hardest to obtain in practical settings where the geometry of the search landscape is unknown.

The second mechanism tackles the question of how solutions should be transformed when they do cross between tasks. Naive approaches simply copy decision variables from one task’s representation to another, an operation that only makes sense when the tasks share a common search space structure. MOMFEA-MTL instead constructs a mapping matrix using a manifold transfer method, an approach rooted in the observation that high-dimensional data often lie on or near a low-dimensional manifold. By learning a transformation that preserves the intrinsic geometric structure of the source population while aligning it with the target task’s distribution, the algorithm can move solutions across task boundaries in a way that respects the underlying shape of each problem’s search space.

Selecting which solutions deserve the cost of this transformation is handled by a K-means solution selection strategy. Clustering the population into groups and choosing representative solutions from those clusters ensures that the transferred knowledge captures the diversity of the source task rather than a narrow sample of its best-performing region. This matters because multi-objective optimization does not seek a single best solution but an entire Pareto front of trade-off solutions, and preserving spread across the front is as important as pushing toward it. Once selected, the solutions are mapped from the source task to the target task, where they join the target population and accelerate its evolution.

The combination is designed for multi-objective multitasking problems, where each task involves optimizing several conflicting objectives simultaneously. This setting compounds the difficulty of knowledge transfer: not only must solutions be useful, but the distribution of trade-offs must also remain balanced. The authors position their approach within the broader lineage of multiobjective multifactorial optimization, building on the original MO-MFEA framework and its successors such as MO-MFEA-II, which introduced cognizant multitasking, and on explicit transfer methods like those based on autoencoding and transfer component analysis. Manifold transfer learning itself has precedent in dynamic multiobjective optimization, where it was used to predict how Pareto sets shift as problems change over time; the new work adapts the idea to the multitasking setting, where the shift is between tasks rather than between time steps.

To evaluate the method, the team ran experiments on nine classical multi-objective multitasking test functions, comparing MOMFEA-MTL against established baselines from the literature. The results showed that the proposed algorithm achieved competitive performance across the benchmark suite, with the adaptive transfer strategy and manifold-based mapping working together to deliver gains where task relatedness could be exploited while limiting damage where it could not. The authors report that the algorithm demonstrates good competitiveness on the test problems, supporting the central claim that combining adaptive transfer control with structure-preserving mapping is an effective recipe for multitasking optimization.

The practical implications extend beyond benchmarks. Evolutionary multitasking has already been applied to problems such as vehicle routing with occasional drivers, sparse reconstruction, multi-task learning for modular learning machines, and, notably by members of the same group, the optimization of steel rolling schedules in industrial production. In each of these domains, multiple related optimization problems arise naturally, and the cost of solving them one at a time is substantial. An algorithm that can reliably harvest the similarities between tasks while shielding itself from their differences could translate into measurable savings in computation time and solution quality, particularly for expensive simulations where each function evaluation carries real cost.

The research also contributes to a conceptual shift in how the field thinks about transfer itself. Early multitasking algorithms treated knowledge transfer as a fixed structural feature, with a constant probability of inter-task mating or a static mapping between search spaces. The trend, exemplified by self-regulated multitasking, adaptive transfer based on population distributions, and transfer rank methods, is toward algorithms that learn from their own experience which transfers help. MOMFEA-MTL fits squarely in this tradition, adding the geometric perspective of manifold learning to the toolkit. As optimization problems in industry and science grow larger and more entangled, the ability to solve many tasks at once, safely and efficiently, may prove to be one of evolutionary computation’s most valuable exports, and this work offers a carefully engineered step in that direction.

Subject of Research: A multi-objective multitasking evolutionary optimization algorithm using adaptive knowledge transfer and manifold transfer learning to mitigate negative transfer.

Article Title: Multi-objective multitasking optimization based on manifold transfer learning

Article References: Zhang, K., Cheng, Y., Wang, S., Sun, H., Wei, L., & Hu, Z. (2026). Multi-objective multitasking optimization based on manifold transfer learning. International Journal of Machine Learning and Cybernetics, 17(9), Article 458. https://doi.org/10.1007/s13042-026-03300-4

Image Credits: AI Generated

DOI: 10.1007/s13042-026-03300-4

Keywords: evolutionary computation, multitasking optimization, multi-objective optimization, knowledge transfer, manifold learning, negative transfer, Pareto front, K-means clustering, evolutionary algorithms, transfer learning, optimization algorithms, machine learning