A Meta-Knowledge Transfer-Based Differential Evolution for Multitask Optimization

Li, Jianyu; Zhan, Zhi-Hui; Tan, Kay Chen; Zhang, Mengjie

doi:10.1109/tevc.2021.3131236

Cited by 105 publications

(29 citation statements)

References 66 publications

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“…Zhou et al [34] found that nonlinear mapping can more accurately reflect the relationship among tasks than linear mapping and proposed a novel and effective kernelized autoencoding strategy to achieve nonlinear mapping. Li et al [35] proposed a meta-knowledge transfer method to share meta-knowledge. Meta-knowledge is a kind of "knowledge of knowledge" that can be transferred more generally among different populations for different tasks.…”

Section: B Related Workmentioning

confidence: 99%

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

Jiang

Zhan

Tan

et al. 2023

IEEE Trans. Evol. Computat.

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Many-task optimization problem is a kind of challenging multi-task optimization problem with more than three tasks. Two significant issues in solving many-task optimization problems are measuring inter-task similarity and transferring knowledge among similar tasks. However, most existing algorithms only use a single similarity measurement, which cannot accurately measure the inter-task similarity because the inter-task similarity is a concept with multiple different aspects. To address this limitation, this paper proposes a bi-objective knowledge transfer framework, which aims firstly to accurately measure different types of inter-task similarity using two different measurements and secondly to effectively transfer knowledge with different types of similarity via specific strategies. To achieve the first goal, a bi-objective measurement is designed to measure inter-task similarity from two different aspects, including shape similarity and domain similarity. To achieve the second goal, a similarity-based adaptive knowledge transfer strategy is designed to choose the suitable knowledge transfer strategy based on the type of inter-task similarity. We compare the bi-objective knowledge transfer framework-based algorithms with several state-of-the-art algorithms on two challenging many-task optimization test suites with 16 instances and on real-world many-task optimization problems with up to 500 tasks. The experimental results show that the proposed algorithms generally outperform the compared algorithms.

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Section: B Related Workmentioning

confidence: 99%

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

Jiang

Zhan

Tan

et al. 2023

IEEE Trans. Evol. Computat.

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“…, is a kind of evolutionary algorithm (EA) that solves optimization problems by iterations of mutation, crossover, and selection [3]- [8]. So far, DE has been successfully applied to a wide range of complex optimization problems [9]- [11], such as multi-objective optimization [12], many-objective optimization [13], constrained optimization [14] [15], multimodal optimization [16]- [19], dynamic optimization [20][21], expensive optimization [22] [23], and multitask optimization [24] [25].…”

Section: Introduction Ifferential Evolution (De) Proposed By Storn An...mentioning

confidence: 99%

Gene Targeting Differential Evolution: A Simple and Efficient Method for Large-Scale Optimization

Wang

Jian

Zhan

et al. 2023

IEEE Trans. Evol. Computat.

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Large-scale optimization problems (LSOPs) are challenging because the algorithm is difficult in balancing too many dimensions and in escaping from trapped bottleneck dimensions. To improve solutions, this paper introduces targeted modification to the certain values in the bottleneck dimensions. Analogous to gene targeting (GT) in biotechnology, we experiment on targeting the specific genes in candidate solution to improve its trait in differential evolution (DE). We propose a simple and efficient method, called GT-based DE (GTDE), to solve LSOPs. In the algorithm design, a simple GT-based modification is developed to perform on the best individual, comprising probabilistically targeting the location of bottleneck dimensions, constructing a homologous targeting vector, and inserting the targeting vector into the best individual. In this way, all the bottleneck dimensions of the best individual can be probabilistically targeted and modified to break the bottleneck and to provide global guidance for more optimal evolution. Note that the GT is only performed on the globally best individual and is just carried out as a simple operator that is added to the standard DE. Experimental studies compare the GTDE with some other state-of-the-art large-scale optimization algorithms, including the winners of CEC2010, CEC2012, CEC2013, and CEC2018 competitions on large-scale optimization. The results show that the GTDE is efficient and performs better than or at least comparable to the others in solving LSOPs.Manuscript received….revised….accepted….

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“…Evolutionary computation algorithms have been one of the most efficient methods to solve NP-hard optimization problems [22]. In recent decades, various types of evolutionary computation algorithms have been developed, mainly including genetic algorithm (GA) [23,24], ant colony optimization (ACO) [25,26], particle swarm optimization [27][28][29], and differential evolution [30][31][32].…”

Section: Introductionmentioning

confidence: 99%

A Scheme Library-Based Ant Colony Optimization with 2-Opt Local Search for Dynamic Traveling Salesman Problem

Wang¹,

Zhu²,

Jiang³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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The dynamic traveling salesman problem (DTSP) is significant in logistics distribution in real-world applications in smart cities, but it is uncertain and difficult to solve. This paper proposes a scheme library-based ant colony optimization (ACO) with a two-optimization (2-opt) strategy to solve the DTSP efficiently. The work is novel and contributes to three aspects: problem model, optimization framework, and algorithm design. Firstly, in the problem model, traditional DTSP models often consider the change of travel distance between two nodes over time, while this paper focuses on a special DTSP model in that the node locations change dynamically over time. Secondly, in the optimization framework, the ACO algorithm is carried out in an offline optimization and online application framework to efficiently reuse the historical information to help fast respond to the dynamic environment. The framework of offline optimization and online application is proposed due to the fact that the environmental change in DTSP is caused by the change of node location, and therefore the new environment is somehow similar to certain previous environments. This way, in the offline optimization, the solutions for possible environmental changes are optimized in advance, and are stored in a mode scheme library. In the online application, when an environmental change is detected, the candidate solutions stored in the mode scheme library are reused via ACO to improve search efficiency and reduce computational complexity. Thirdly, in the algorithm design, the ACO cooperates with the 2-opt strategy to enhance search efficiency. To evaluate the performance of ACO with 2-opt, we design two challenging DTSP cases with up to 200 and 1379 nodes and compare them with other ACO and genetic algorithms. The experimental results show that ACO with 2-opt can solve the DTSPs effectively.

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A Meta-Knowledge Transfer-Based Differential Evolution for Multitask Optimization

Cited by 105 publications

References 66 publications

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

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

Gene Targeting Differential Evolution: A Simple and Efficient Method for Large-Scale Optimization

A Scheme Library-Based Ant Colony Optimization with 2-Opt Local Search for Dynamic Traveling Salesman Problem

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