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evolutionary multitasking with evolutionary trend alignment in subdomains

EXPERT SYSTEMS WITH APPLICATIONS 2025年 269卷

作者： Du, Wenhao Ren, Zhigang Cole, Jack Zou, Xiaotian Wang, Chaowen Xi An Jiao Tong Univ Sch Automat Sci & Engn Xian Peoples R China Univ Exeter Dept Comp Sci Exeter England

evolutionary multitasking (EMT) seeks to address multiple tasks by leveraging inter-task knowledge transfer for an enhanced comprehensive performance. However, its effectiveness may be severely diminished when the tasks involved share little similarity. Some domain adaptation (DA) methods are developed to augment inter-task similarity through learning and applying transformation mappings between tasks. Nevertheless, they generally treat each task as an indivisible domain, which prevents them from fully and precisely leveraging task-specific characteristics. To overcome this limitation, this study proposes a multifactorial evolutionary algorithm based on subdomain evolutionary trend alignment (SETA-MFEA). SETA-MFEA first adaptively decomposes each task into several subdomains, each of which exhibits a relatively simple fitness landscape. A novel DA technique named SETA is then designed and utilized to establish precise inter-subdomain mappings by determining and aligning the evolutionary trends of the corresponding subpopulations. The derived mappings enable various subpopulations, whether within the same task or different tasks, to share a consistent evolutionary trend, so that intra- and inter-task complementary evolutionary information can be transferred through SETA-based intersubdomain crossovers. Extensive testing on several commonly used multitasking/many-tasking benchmark suites, as well as in a real-world application, demonstrates that SETA-MFEA shows competitive performance compared to two single-task evolutionary algorithms and six classic or state-of-the-art EMT algorithms.

关键词： evolutionary multitasking Domain adaptation Domain decomposition evolutionary trend alignment

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evolutionary multitasking with Adaptive Tradeoff Selection Strategy

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COGNITIVE COMPUTATION 2025年第1期17卷 1-25页

作者： Li, Wei Zhou, Yinhui Wang, Lei Xian Univ Technol Sch Comp Sci & Engn Xian 710048 Peoples R China Shaanxi Key Lab Network Comp & Secur Technol Xian 710048 Peoples R China

As a new emerging evolutionary framework, evolutionary multitasking aims to optimize multiple tasks simultaneously. Knowledge transfer is an important component of evolutionary multitasking. How to extract and transfer knowledge significantly affects the performance of the algorithm. A serious challenge for evolutionary multitasking is the inappropriate knowledge transfer or insufficient exploration and exploitation. To address this challenge, an evolutionary multitasking with adaptive tradeoff selection strategy (EMT-ATS) is proposed. To enhance global exploration and local exploitation during the evolution, an adaptive tradeoff selection mechanism is developed to select promising offspring during different stages to guide the population toward more promising solution regions. In addition, a Cohen's d indicator-based is used to adjust knowledge transfer. To verify the effectiveness of the proposed EMT-ATS, a series of experiments are conducted with several popular evolutionary multitasking algorithms on multitasking benchmark problems. In addition, a multitask optimization problem involving two real-world problems is used to validate the practicability of the proposed EMT-ATS. Experimental results demonstrate the effectiveness of the proposed EMT-ATS.

关键词： evolutionary multitasking Knowledge transfer Adaptive tradeoff selection Multifactorial optimization

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evolutionary multitasking for solving nonlinear equation systems

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INFORMATION SCIENCES 2024年 660卷

作者： Li, Shuijia Gong, Wenyin Lim, Ray Liao, Zuowen Gu, Qiong Natl Univ Def Technol Coll Syst Engn Changsha 410073 Peoples R China China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China Nanyang Technol Univ Sch Comp Sci & Engn Singapore 639798 Singapore Huazhong Univ Sci & Technol State Key Lab Intelligent Mfg Equipment & Technol Wuhan 430074 Peoples R China Beibu Gulf Univ Beibu Gulf Ocean Dev Res Ctr Qinzhou 535000 Peoples R China Hubei Univ Arts & Sci Sch Comp Engn Xiangyang 441053 Peoples R China Hubei Univ Arts & Sci Hubei Key Lab Power Syst Design & Test Elect Vehic Xiangyang 441053 Peoples R China

Over the past few years, many evolutionary algorithms have been developed to find multiple roots of the nonlinear equation system (NES). However, they can only solve one NES in a single run, ignoring the potentially useful information and solving experience derived from different NESs. To this end, an evolutionary multitasking NES optimization framework called MTNES, is proposed for the first time in this paper for solving multiple NESs simultaneously. Specifically, we first initialize multiple NES tasks in a 0-1 unified search space to establish an implicit relationship between NESs to facilitate knowledge transfer. Then, combining differential evolution and neighborhood technique, a neighborhood knowledge transfer is presented to reduce negative knowledge transfer and thus help find more roots. In addition, a novel resource reallocation mechanism is developed to release the found roots, thereby improving population diversity as well as aiding the search for more promising areas. Numerous empirical results reveal that the proposed approach can achieve a higher root rate and success rate when compared with several well -established algorithms on eighteen complex NESs. Moreover, experimental results on two real -world applications further show the potential practicability of the proposed multitasking NES -solving framework.

关键词： Nonlinear equation system evolutionary multitasking Knowledge transfer Differential evolution Neighborhood

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evolutionary multitasking for bidirectional adaptive codec: A case study on vehicle routing problem with time windows

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APPLIED SOFT COMPUTING 2023年 145卷

作者： Wu, Yanlin Cai, Yanguang Fang, Chuncheng Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China

The vehicle routing problem (VRP), an NP-hard problem of considerable complexity, pertains to the determination of optimal sequences of customer visits in an effort to minimize the total operational cost of vehicles. Conventional approaches to tackling VRP have primarily focused on optimizing each task independently, utilizing mathematical methodologies or evolutionary algorithms. Nevertheless, research exploring the application of the advanced concept of evolutionary multitasking (EMT) to address VRP remains limited. To enrich the body of knowledge in this field, our study presents an innovative EMT algorithm, dubbed EMT-RD. This algorithm employs a bidirectional adaptive codec to facilitate the transfer of information between real and discrete solutions. The EMT-RD algorithm permits the transfer of real and discrete solutions across varying tasks, utilizing an encoding rule that transforms information from real to discrete solutions for each task. Further, a decoding rule from discrete to real solutions generates search experiences, facilitating the transfer of knowledge between real solutions. The mechanism is capped off with an adaptive transfer strategy that instigates transfer operations across different tasks. Each task is tackled using a single-task optimization algorithm that employs an artificial bee colony algorithm. Through comprehensive experimentation, we found that our proposed EMT-RD algorithm outperforms competitors on 168 VRP with time windows multitasking instance pairs and four real-world problem pairs that were developed for this study. These results underscore the efficacy of the adaptive transfer strategy and bidirectional adaptive codec in addressing inter-task correlations when solving the VRP. & COPY;2023 Elsevier B.V. All rights reserved.

关键词： Adaptive transfer strategy Artificial bee colony Bidirectional adaptive codec evolutionary multitasking Vehicle routing problem with time windows

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evolutionary multitasking with Two -level Knowledge Transfer for Multi -view Point Cloud Registration

Evolutionary Multitasking with Two -level Knowledge Transfer...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Ding, Hangqi Xu, Haoran Wu, Yue Li, Hao Gong, Maoguo Ma, Wenping Miao, Qiguang Shi, Jiao Lei, Yu Xidian Univ Sch Comp Sci & Technol Xian Peoples R China Xidian Univ Sch Elect Engn Xian Peoples R China Xidian Univ Sch Artificial Intelligence Xian Peoples R China

ISBN: (纸本)9798400704949

Point cloud registration is a hot research topic in the field of computer vision. In recent years, the registration method based on evolutionary computation has attracted more and more attention because of its robustness to initial pose and flexibility of objective function design. However, most of the current evolutionary computation-based point cloud registration methods do not take into account the multi-view problem, that is, to capture the close relationship between point clouds from different perspectives. We fully realize that if these relations are used correctly, the registration performance can be improved. Therefore, this paper proposes an evolutionary multitasking multi -view point cloud registration method, which solves the problem of multi-view error accumulation. To ensure the unity of global and local, a two -level knowledge transfer strategy is proposed, which divides the multi-view cloud registration task into two levels. This strategy unifies the search space of two registration tasks, solves the negative transfer phenomenon, and avoids the problem of falling into the local optimum. Finally, the effectiveness of the method is verified by sufficient experiments. This method has strong robustness to noise and outliers, and can be effectively implemented in various registration scenarios.

关键词： Point cloud registration evolutionary multitasking knowledge transfer multi-task optimization

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evolutionary multitasking for Feature Selection in High-Dimensional Classification via Particle Swarm Optimization

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IEEE TRANSACTIONS ON evolutionary COMPUTATION 2022年第3期26卷 446-460页

作者： Chen, Ke Xue, Bing Zhang, Mengjie Zhou, Fengyu Zhengzhou Univ Sch Elect Engn Zhengzhou 450001 Peoples R China Victoria Univ Wellington Sch Engn & Comp Sci Wellington 6140 New Zealand Shandong Univ Sch Control Sci & Engn Jinan 250061 Peoples R China

Feature selection (FS) is an important preprocessing technique for improving the quality of feature sets in many practical applications. Particle swarm optimization (PSO) has been widely used for FS due to being efficient and easy to implement. However, when dealing with high-dimensional data, most of the existing PSO-based FS approaches face the problems of falling into local optima and high-computational cost. evolutionary multitasking is an effective paradigm to enhance global search capability and accelerate convergence by knowledge transfer among related tasks. Inspired by evolutionary multitasking, this article proposes a multitasking PSO approach for high-dimensional FS. The approach converts a high-dimensional FS task into several related low-dimensional FS tasks, then finds an optimal feature subset by knowledge transfer between these low-dimensional FS tasks. Specifically, a novel task generation strategy based on the importance of features is developed, which can generate highly related tasks from a dataset adaptively. In addition, a new knowledge transfer mechanism is presented, which can effectively implement positive knowledge transfer among related tasks. The results demonstrate that the proposed method can evolve a feature subset with higher classification accuracy in a shorter time than other state-of-the-art FS methods on high-dimensional classification.

关键词： Task analysis Feature extraction multitasking Optimization Search problems Knowledge transfer Particle swarm optimization evolutionary multitasking feature selection (FS) high-dimensional classification knowledge transfer particle swarm optimization (PSO)

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Constraints Separation Based evolutionary multitasking for Constrained Multi-Objective Optimization Problems

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IEEE/CAA Journal of Automatica Sinica 2024年第8期11卷 1819-1835页

作者： Kangjia Qiao Jing Liang Kunjie Yu Xuanxuan Ban Caitong Yue Boyang Qu Ponnuthurai Nagaratnam Suganthan School of Electrical and Information Engineering Zhengzhou UniversityZhengzhou 450001China School of Electrical Engineering and Automation Henan Institute of TechnologyXinxiang 453003China School of Electronic and Information Zhongyuan University of TechnologyZhengzhou 450007China KINDI Center for Computing Research College of EngineeringQatar UniversityDoha 999043Qatar

Constrained multi-objective optimization problems(CMOPs)generally contain multiple constraints,which not only form multiple discrete feasible regions but also reduce the size of optimal feasible regions,thus they propose serious challenges for *** all constraints,some constraints are highly correlated with optimal feasible regions;thus they can provide effective help to find feasible Pareto ***,most of the existing constrained multi-objective evolutionary algorithms tackle constraints by regarding all constraints as a whole or directly ignoring all constraints,and do not consider judging the relations among constraints and do not utilize the information from promising single ***,this paper attempts to identify promising single constraints and utilize them to help solve *** be specific,a CMOP is transformed into a multitasking optimization problem,where multiple auxiliary tasks are created to search for the Pareto fronts that only consider a single constraint ***,an auxiliary task priority method is designed to identify and retain some high-related auxiliary tasks according to the information of relative positions and dominance ***,an improved tentative method is designed to find and transfer useful knowledge among *** results on three benchmark test suites and 11 realworld problems with different numbers of constraints show better or competitive performance of the proposed method when compared with eight state-of-the-art peer methods.

关键词： Constrained multi-objective optimization(CMOPs) evolutionary multitasking knowledge transfer single constraint.

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A feedback matrix based evolutionary multitasking algorithm for high-dimensional ROC convex hull maximization

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INFORMATION SCIENCES 2025年 690卷

作者： Qiu, Jianfeng Wang, Ning Shu, Shengda Li, Kaixuan Xie, Juan Chen, Chunhui Cheng, Fan Anhui Univ Informat Mat & Intelligent Sensing Lab Anhui Prov Hefei Peoples R China Anhui Univ Engn Res Ctr Autonomous Unmanned Syst Technol Minist Educ Hefei Peoples R China Anhui Univ Sch Artificial Intelligence Hefei Peoples R China Anhui Univ Sch Comp Sci & Technol Hefei Peoples R China Anhui Jianzhu Univ Sch Math & Phys Hefei Peoples R China Anhui Prov Geomat Ctr Hefei Peoples R China Key Lab JiangHuai Arable Land Resources Protect & Hefei Peoples R China Anhui Land Satellite Remote Sensing Applicat Tech Hefei Peoples R China

Multi-objective evolutionary algorithms have shown their competitiveness in solving ROC convex hull maximization. However, due to "the curse of dimensionality", few of them focus on high-dimensional ROCCH maximization. Therefore, in this paper, a feedback matrix (FM)-based evolutionary multitasking algorithm, termed as FM-EMTA, is proposed. In FM-EMTA, to tackle "the curse of dimensionality", a feature importance based low-dimensional task construction strategy is designed to transform the high-dimensional ROCCH maximization task into several low-dimensional tasks. Then, each low-dimensional task evolves with a population. To ensure that the low-dimensional task achieves a better ROCCH, an FM-based evolutionary multitasking operator is proposed. Specifically, for each low-dimensional task i, the element FM(i,j) in feedback matrix is defined to measure the degree that the low-dimensional task j could assist task i. Based on it, an FM-based assisted task selection operator and an FM-based knowledge transfer operator are developed to constitute the evolutionary multitasking operator, with which the useful knowledge is transferred among the low-dimensional tasks. After the evolution, the best ROCCHs obtained by the low-dimensional tasks are combined together to achieve the final ROCCH on the original high-dimensional task. Experiments on twelve high-dimensional datasets with different characteristics demonstrate the superiority of the proposed FM-EMTA over the state-of-the-arts in terms of the area under ROCCH, the hypervolume indicator and the running time.

关键词： High-dimensional ROCCH Feedback matrix evolutionary computation evolutionary multitasking

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An evolutionary multitasking Method for Multi-Objective Critical Node Detection on Interdependent Networks

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IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING 2025年第1期11卷 607-620页

作者： Zhang, Lei Zhang, Huaijin Feng, Xinyi Yang, Haipeng Cheng, Fan Anhui Univ Sch Comp Sci & Technol Informat Mat & Intelligent Sensing Lab Anhui Prov Minist Educ Hefei 230039 Peoples R China Anhui Univ Sch Comp Sci & Technol Key Lab Intelligent Comp & Signal Proc Minist Educ Hefei 230039 Peoples R China

The critical node detection problem (CNDP) on interdependent networks has attracted the attention of researchers recently. However, as the number of network layers increases leading to the expansion of the search space, it prevents the existing work from obtaining high-quality solutions with limited computational resources. In fact, there is connection information between each single-layer network and the interdependence network. In this regard, this paper proposes an evolutionary multitasking method in which knowledge transfer between single-layer and multi-layer network tasks is fully utilized to better solve the CNDP on interdependent networks. Specifically, the CNDP on interdependent networks is firstly transformed into a bi-objective problem (BICND). Then, the critical node detection (CND) task for multi-layer network and the CND task for single-layer networks are established in the proposed method to solve the BICND problem. Meanwhile, this paper proposes a new knowledge transfer mechanism based on the node coupling relationship of interdependent networks. This knowledge transfer mechanism not only ensures the transfer of useful information between multiple tasks, but also helps them to obtain higher quality solutions. The experimental results show that the proposed method can effectively search for high-quality solutions and has better performance than other single-objective and multi-objective methods.

关键词： interdependent network evolutionary multitasking Critical node detection multi-objective optimization multi-objective optimization knowledge transfer knowledge transfer multi-objective optimization knowledge transfer

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evolutionary multitasking with Search Direction Alignment

Evolutionary Multitasking with Search Direction Alignment

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International Joint Conference on Neural Networks (IJCNN)

作者： Du, Wenhao Ren, Zhigang Wang, Muju Chen, An Liu, Hanqing Su, Shenyu Xi An Jiao Tong Univ Sch Automat Sci & Engn Xian Peoples R China JiangSu Prov Special Equipment Safety Supervis In Branch Wuxi Wuxi Peoples R China

ISBN: (纸本)9781665488679

evolutionary multitasking (EMT) aims to tackle multiple tasks simultaneously with knowledge transfer for an improved overall performance. Unfortunately, the performance is vulnerable to negative transfer, when the involved tasks share a low similarity. Some domain adaptation (DA) techniques have been designed to enhance the similarity between tasks by the means of inter-task individual pairing or distribution alignment, but they do not explicitly exploit the relation between the search directions of different task populations. To this end, this study proposes a novel DA technique based on search direction alignment (SDA), which derives the inter-task transformation by determining and aligning the evolutionary directions of different task populations. After that, SDA is embedded into a pioneer EMT algorithm, i.e., MFEA, forming the SDA-MFEA. In SDA-MFEA, with the help of SDA, different task populations can possess the same evolutionary trend and thus positive knowledge transfer is more likely to occur during the optimization process. Extensive experiments conducted on a widely-used multitasking benchmark suite show that SDA-MFEA outperforms the canonical MFEA and four state-of-the-art DA-based MFEA variants, indicating the effectiveness of SDA.

关键词： evolutionary multitasking domain adaptation inter-task transformation search direction alignment

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