2 research outputs found
Scalable Transfer Evolutionary Optimization: Coping with Big Task Instances
In today's digital world, we are confronted with an explosion of data and
models produced and manipulated by numerous large-scale IoT/cloud-based
applications. Under such settings, existing transfer evolutionary optimization
frameworks grapple with satisfying two important quality attributes, namely
scalability against a growing number of source tasks and online learning
agility against sparsity of relevant sources to the target task of interest.
Satisfying these attributes shall facilitate practical deployment of transfer
optimization to big source instances as well as simultaneously curbing the
threat of negative transfer. While applications of existing algorithms are
limited to tens of source tasks, in this paper, we take a quantum leap forward
in enabling two orders of magnitude scale-up in the number of tasks; i.e., we
efficiently handle scenarios with up to thousands of source problem instances.
We devise a novel transfer evolutionary optimization framework comprising two
co-evolving species for joint evolutions in the space of source knowledge and
in the search space of solutions to the target problem. In particular,
co-evolution enables the learned knowledge to be orchestrated on the fly,
expediting convergence in the target optimization task. We have conducted an
extensive series of experiments across a set of practically motivated discrete
and continuous optimization examples comprising a large number of source
problem instances, of which only a small fraction show source-target
relatedness. The experimental results strongly validate the efficacy of our
proposed framework with two salient features of scalability and online learning
agility.Comment: 12 pages, 5 figures, 2 tables, 2 algorithm pseudocode
Multitasking Evolutionary Algorithm Based on Adaptive Seed Transfer for Combinatorial Problem
Evolutionary computing (EC) is widely used in dealing with combinatorial
optimization problems (COP). Traditional EC methods can only solve a single
task in a single run, while real-life scenarios often need to solve multiple
COPs simultaneously. In recent years, evolutionary multitasking optimization
(EMTO) has become an emerging topic in the EC community. And many methods have
been designed to deal with multiple COPs concurrently through exchanging
knowledge. However, many-task optimization, cross-domain knowledge transfer,
and negative transfer are still significant challenges in this field. A new
evolutionary multitasking algorithm based on adaptive seed transfer (MTEA-AST)
is developed for multitasking COPs in this work. First, a dimension unification
strategy is proposed to unify the dimensions of different tasks. And then, an
adaptive task selection strategy is designed to capture the similarity between
the target task and other online optimization tasks. The calculated similarity
is exploited to select suitable source tasks for the target one and determine
the transfer strength. Next, a task transfer strategy is established to select
seeds from source tasks and correct unsuitable knowledge in seeds to suppress
negative transfer. Finally, the experimental results indicate that MTEA-AST can
adaptively transfer knowledge in both same-domain and cross-domain many-task
environments. And the proposed method shows competitive performance compared to
other state-of-the-art EMTOs in experiments consisting of four COPs