A Bi-Objective Knowledge Transfer Framework for Evolutionary Many-Task Optimization

Jiang, Yi; Zhan, Zhi‐Hui; Tan, Kay Chen; Zhang, Jun

doi:10.1109/tevc.2022.3210783

Cited by 32 publications

(20 citation statements)

References 56 publications

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“…Therefore, in the future, we will not only enhance the LEO-based algorithms to well address the above issues, but also hope to extend the idea of knowledge learning to more research aspects of EC and real-world application problems, such as large-scale problems [25]- [63], multimodal problems [64][65], and multi-task problems [66] [67]. Moreover, the automatic optimization of the network architecture and parameter of the LEO learning system for different scenarios will be worthy of study [68].…”

Section: Discussionmentioning

confidence: 99%

Learning-Aided Evolution for Optimization

Zhan

Zhang

2023

IEEE Trans. Evol. Computat.

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Abstract-Learning and optimization are the two essential abilities of human beings for problem solving. Similarly, computer scientists have made great efforts to design artificial neural network (ANN) and evolutionary computation (EC) to simulate the learning ability and the optimization ability for solving real-world problems, respectively. These have been two essential branches in artificial intelligence (AI) and computer science. However, in humans, learning and optimization are usually integrated together for problem solving. Therefore, how to efficiently integrate these two abilities together to develop powerful AI remains a significant but challenging issue. Motivated by this, this paper proposes a novel learning-aided evolutionary optimization (LEO) framework that plus learning and evolution for solving optimization problems. The LEO is integrated with the evolution knowledge learned by ANN from the evolution process of EC to promote optimization efficiency. The LEO framework is applied to both classical EC algorithms and some state-of-the-art EC algorithms including a champion algorithm, with benchmarking against the IEEE Congress on Evolutionary Computation competition data. The experimental results show that the LEO can significantly enhance the existing EC algorithms to better solve both single-objective and multi-/many-objective global optimization problems, suggesting that learning plus evolution is more intelligent for problem solving. Moreover, the experimental results have also validated the time efficiency of the LEO, where the additional time cost for using LEO is greatly deserved. Therefore, the promising LEO can lead to a new and more efficient paradigm for EC algorithms to solve global optimization problems by plus learning and evolution.

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Section: Discussionmentioning

confidence: 99%

Learning-Aided Evolution for Optimization

Zhan

Zhang

2023

IEEE Trans. Evol. Computat.

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“…For example, the TRADE algorithm consumes a certain amount of extra fitness evaluations to identify similar tasks and the TRS may not be accurate enough to distinguish similar tasks when a MaTOP contains many types of heterogeneous tasks. Therefore, for future work, researchers can consider the following two aspects: 1) discovering a more efficient and accurate TRS for identifying and capturing shift invariance between tasks and 2) extending the scope of similarity between tasks such as rotated invariance between function landscapes of the tasks and biobjective similarity (i.e., shape and domain) of the tasks [53].…”

Section: Discussionmentioning

confidence: 99%

Transferable Adaptive Differential Evolution for Many-Task Optimization

Zhan

Tan

et al. 2023

IEEE Trans. Cybern.

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The evolutionary multitask optimization (EMTO) algorithm is a promising approach to solve many-task optimization problems (MaTOPs), in which similarity measurement and knowledge transfer (KT) are two key issues. Many existing EMTO algorithms estimate the similarity of population distribution to select a set of similar tasks and then perform KT by simply mixing individuals among the selected tasks. However, these methods may be less effective when the global optima of the tasks greatly differ from each other. Therefore, this article proposes to consider a new kind of similarity, namely, shift invariance, between tasks. The shift invariance is defined that the two tasks are similar after linear shift transformation on both the search space and the objective space. To identify and utilize the shift invariance between tasks, a two-stage transferable adaptive differential evolution (TRADE) algorithm is proposed. In the first evolution stage, a task representation strategy is proposed to represent each task by a vector that embeds the evolution information. Then, a task grouping strategy is proposed to group the similar (i.e., shift invariant) tasks into the same group while the dissimilar tasks into different groups. In the second evolution stage, a novel successful evolution experience transfer method is proposed to adaptively utilize the suitable parameters by transferring successful parameters among similar tasks within the same group. Comprehensive experiments are carried out on two representative MaTOP benchmarks with a total of 16 instances and a real-world application. The comparative results show that the proposed TRADE is superior to some state-of-the-art EMTO algorithms and single-task optimization algorithms.

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“…Tang et al [40] devised a level-based inter-task learning strategy to transfer sharing information among the cross-task neighborhood for multi-task particle swarm optimization with a dynamic local topology. Jiang et al [41] proposed Block-Level Knowledge Transfer based on Differential Evolution (BLKT-DE) for EMT. BLKT divides each individual into multiple gene blocks of equal size and then groups the blocks using K-means clustering.…”

Section: Emt Algorithmsmentioning

confidence: 99%

Multifactorial Memetic Algorithm with Adaptive Auxiliary Tasks for Service Migration Optimization in Mobile Edge Computing

Li,

Liu,

Liu

et al. 2024

Preprint

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In high-speed mobile networks, mobile edge computing is tasked with service migration optimization, i.e., assigning mobile users to the right servers to minimize the response time. Service migration optimization is a complex problem posing significant challenges to conventional optimization methods. To tackle this problem , we develop a multifactorial memetic algorithm with adaptive auxiliary tasks or MFMA-AAT for short. MFMA-AAT solves the target service migration optimization problem and an adaptively selected auxiliary task simultaneously, where the auxiliary task is a simplified version of the target problem to guide the search towards promising regions faster via knowledge transfer. Multiple auxiliary tasks are pre-constructed based on the distribution of the mobile users and the one with best improvement at each generation is selected for knowledge transfer. A community detection based memetic operator is also introduced to accelerate the local convergence of the proposed algorithm. Experimental results on test problems demonstrate that MFMA-AAT is more efficient than traditional service migration approaches and other state-of-the-art multifactorial evolutionary algorithms.

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A Bi-Objective Knowledge Transfer Framework for Evolutionary Many-Task Optimization

Cited by 32 publications

References 56 publications

Learning-Aided Evolution for Optimization

Learning-Aided Evolution for Optimization

Transferable Adaptive Differential Evolution for Many-Task Optimization

Multifactorial Memetic Algorithm with Adaptive Auxiliary Tasks for Service Migration Optimization in Mobile Edge Computing

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