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evolutionary multi-task optimization with hybrid knowledge transfer strategy

INFORMATION SCIENCES 2021年 580卷 874-895页

作者： Cai, Yiqiao Peng, Deming Liu, Peizhong Guo, Jing-Ming Huaqiao Univ Coll Comp Sci & Technol Xiamen 361021 Peoples R China Xiamen Key Lab Data Secur & Blockchain Technol Fujian Peoples R China Huaqiao Univ Coll Engn Quanzhou 362000 Peoples R China Natl Taiwan Univ Sci & Technol Dept Elect Engn Taipei 10607 Taiwan

As an emerging research paradigm in the field of evolutionary computation, evolutionary multi-task optimization (EMTO) has received an increasing amount of attention due to its capability in concurrently solving multiple related optimization tasks. In EMTO, without any prior knowledge about the complementarity between different tasks, the task relatedness is mainly captured dynamically through the evolving population. However, how to transfer the knowledge across tasks in accordance with different degrees of relatedness as the search proceeds has received little visibility. To address this issue and further enhance the performance of EMTOs, this paper proposes a hybrid knowledge transfer (HKT) strategy. In HKT, a population distribution-based measurement (PDM) technique is designed to evaluate the task relatedness based on the distribution characteristics of the evolving population, and then a multi-knowledge transfer (MKT) mechanism is employed to conduct multiple strategies of knowledge transfer according to the degree of relatedness between tasks. By incorporating the HKT strategy into EMTO, the resultant algorithmic framework, termed EMTO-HKT, is presented. The experimental results on the single-objective multi-task optimization test suite demonstrate the superiority of EMTO- HKT compared with other state-of-the-art EMTO algorithms. (c) 2021 Elsevier Inc. All rights reserved.

关键词： evolutionary multi-task optimization Hybrid knowledge transfer strategy Population distribution-based measurement multi-knowledge transfer

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evolutionary multi-task optimization for parameters extraction of photovoltaic models

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ENERGY CONVERSION AND MANAGEMENT 2020年 207卷 112509-112509页

作者： Liang, Jing Qiao, Kangjia Yuan, Minghua Yu, Kunjie Qu, Boyang Ge, Shilei Li, Yaxin Chen, Guanlin Zhengzhou Univ Sch Elect Engn Zhengzhou 450001 Peoples R China Zhengzhou Univ Sch Pharmaceut Sci Zhengzhou 450001 Peoples R China Zhongyuan Univ Technol Sch Elect & Informat Engn Zhengzhou 450007 Peoples R China

As the demand for solar energy increases dramatically, the optimization and control of photovoltaic systems become increasingly important, accurate and reliable parameter identification of photovoltaic models is always required, which proposes an urgent need for accurate and robust algorithms. To this end, many heuristic algorithms have been proposed to extract the parameters of different photovoltaic models. However, they only extract the parameters of one model in a single run, which is inconsistent with the human ability to solve multiple tasks simultaneously and ignores the useful information derived from different models. Therefore, in this paper an evolutionary multi-task optimization algorithm is proposed to extract the parameters of multiple different photovoltaic models simultaneously. To be specific, the helpful information found by the population is transferred through the cross-task crossover to improve the performance in terms of solution quality and convergence rate of the population. The proposed algorithm is evaluated by extracting the parameters of three different models simultaneously, i.e., single diode, double diode, and photovoltaic module model. Comprehensive results demonstrate that the proposed algorithm has better performance with respect to the accuracy and robustness in comparison with other state-of-the-art algorithms.

关键词： Photovoltaic models Parameter extraction evolutionary multi-task optimization Differential evolution

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Knowledge transfer in evolutionary multi-task optimization: A survey

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APPLIED SOFT COMPUTING 2023年 138卷

作者： Tan, Ziying Luo, Linbo Zhong, Jinghui Xidian Univ Sch Cyber Engn Xian Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou Peoples R China

evolutionary multi-task optimization (EMTO) is an optimization algorithm designed to optimize multiple tasks simultaneously. In real life, different tasks often correlate to each other, and there exists implicit knowledge or skills common to these tasks. In EMTO, such common knowledge is utilized in an evolutionary optimization process, and EMTO aims to transfer knowledge across different tasks to improve performance in solving each task independently. Therefore, an effective and efficient knowledge transfer mechanism is critical to ensure the success of EMTO. To this end, this survey focuses on the current research progress of knowledge transfer methods in EMTO and proposes a systematic multi-level taxonomy to categorize the existing work. The conduct of categorization is based on two key problems: when and how to perform knowledge transfer. Based on the proposed taxonomy, we also discuss the possibility of integrating various approaches under different categories and applying transfer learning approaches to EMTO. Throughout these discussions, this survey paper aims to identify the potential research directions for improving knowledge transfer performance in EMTO.& COPY;2023 Published by Elsevier B.V.

关键词： Knowledge transfer evolutionary multi-task optimization Transfer learning

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evolutionary Constrained multi-task optimization: Benchmark Problems and Preliminary Results 22

Evolutionary Constrained Multi-task Optimization: Benchmark ...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Li, Yanchi Gong, Wenyin Li, Shuijia Univ Geosci Sch Comp Sci China Wuhan Peoples R China

ISBN: (纸本)9781450392686

multi-task optimization (MTO) aims to solve multiple tasks simultaneously. However, multi-task evolutionary algorithms (MTEAs) hardly consider the problem with constraints, while most optimization problems, in reality, are with constraints. This study presents a benchmark of constrained multi-task optimization problems (CMTOPs), modified from the CEC2017 competition on evolutionary multi-task optimization and CEC2017 competition on constrained real-parameter optimization. Moreover, this study attempts to solve CMTOPs by incorporating constraint handling techniques into MTEAs. Experimental results demonstrate the complexity of the proposed benchmark in the context of CMTOPs and present the requirements for handling constraints in MTEAs. Our preliminary exploration also reveals prospects for the development of evolutionary algorithms in the area of constrained multi-task optimization. The Matlab source code can be obtained from https://***/intLyc/CMTO-Benchmark.

关键词： Constrained multi-task optimization evolutionary multi-task optimization constrained optimization evolutionary transfer optimization multifactorial evolutionary algorithm

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multi-task optimization in reliability redundancy allocation problem: A multifactorial evolutionary-based approach

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2024年 244卷

作者： Chowdury, Md. Abdul Malek Nath, Rahul Shukla, Amit K. Rauniyar, Amit Muhuri, Pranab K. South Asian Univ Dept Comp Sci Rajpur Rd New Delhi 110068 India Univ Bergen Dept Informat Thormohlens Gate 55 N-5008 Bergen Norway Univ Vaasa Sch Technol & Innovat Wolffintie 34 FI-65200 Vaasa Finland Univ Coll Dublin Sch Comp Sci Dublin Ireland

evolutionary multi-task optimization attempts to solve multiple optimization problems simultaneously by modeling the solution structures of two or more problems within a single encoding. In this paper, we report a novel way for evolutionary multi-task optimization in the reliability redundancy allocation problem exploiting the concepts of the popular multifactorial evolutionary algorithm (MFEA). We demonstrate the working of the proposed method considering two test sets and show how they can be concurrently solved using the MFEA. In the first test set, we consider two optimization tasks (case studies): the complex (bridge) system and the seriesparallel system. In the second test set, there are two optimization tasks: the over-speed protection system for the gas turbine and the life support system in a space capsule. The common attributes between the two systems, within a set, complement each other to enhance the evolution process through implicit knowledge transfer. We present the comparative results considering existing evolutionary methods such as particle swarm optimization, genetic algorithm, simulated annealing, differential evolution, and ant colony optimization. Results are analyzed and compared using the average reliability, best reliability, computation time, performance ranking, and the popular statistical significance test of analysis of variance. The outcome shows that our proposed approach can solve the multiple case studies of RRAP simultaneously without compromising the solution quality. Moreover, our MFEA based solution method tops the rank among all approaches and provides significant improvement in computation time where it gains 28.02% and 14.43% of improvement in computation time for first and second test set, respectively, when compared with genetic algorithm. The percentage improvements in the computational time of the MFEA significantly increases when it is compared with other approaches.

关键词： evolutionary multi-task optimization multifactorial optimization Reliability-redundancy allocation problem (RRAP) Complex (bridge) system Series-parallel system Over-speed protection system Life support system in a space capsule

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Semantics-guided multi-task genetic programming for multi-output regression

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PATTERN RECOGNITION 2025年 161卷

作者： Wang, Chunyu Chen, Qi Xue, Bing Zhang, Mengjie Victoria Univ Wellington Ctr Data Sci & Artificial Intelligence Wellington 6140 New Zealand Victoria Univ Wellington Sch Engn & Comp Sci Wellington 6140 New Zealand

multi-output regression entails the simultaneous prediction of two or more output variables, presenting greater complexities than single-output regression due to the frequent interdependent relationships of these variables. Such dependencies mean that accurately predicting one variable typically requires careful analysis of its relationships with others. In this paper, multi-output regression problems are treated as multi-task problems, with a prediction of one output variable as a distinct task. A new multi-task multi-population genetic programming method is proposed to solve the problem. The method incorporates a semantics based crossover operator to identify the most informative subtree from a similar task that facilitates positive knowledge transfer. Empirical results indicate that our method significantly improves the training and testing performances of other multi-task GP methods, surpassing standard GP and GP with regressor chain on most examined regression datasets. Further analysis reveals that our proposed method can generate high-quality solutions by knowledge transfer and efficiently evolves similar GP models for analogous output variables, significantly enhancing positive knowledge transfer.

关键词： multi-output regression Genetic programming evolutionary multi-task optimization

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Knee-oriented expensive many-objective optimization via aggregation-dominance: A multi-task perspective

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SWARM AND evolutionary COMPUTATION 2025年 92卷

作者： Tang, Junfeng Wang, Handing Jin, Yaochu Xidian Univ Sch Artificial Intelligence Xian 710071 Peoples R China Westlake Univ Sch Engn Hangzhou 310030 Peoples R China

Given the costs to implement whole Pareto optimal solutions, users often prefer solutions of interest, like knee points, which represent naturally preferred solutions without a specific bias. Recent surrogate-assisted multi-objective evolutionary algorithms (SAMOEAs) incorporating knee identification techniques have been suggested, but most of them cannot find knee solutions for expensive many-objective optimization problems. This work proposes a Kriging-assisted evolutionary multi-task algorithm with aggregation-dominance. The aggregation-dominance approach identifies knee points on an estimated Pareto front, from which subproblems are created and solved in parallel via Kriging-assisted multi-task optimization for guiding search knee solutions. Additionally, our proposed infill solutions selection strategy focuses on re-evaluating solutions converging in regions of interest. Experimental results on knee-oriented benchmark problems show that our algorithm outperforms state-of-the-art methods, with aggregation-dominance surpassing five existing knee identification techniques. We also validate the algorithm's performance on the portfolio allocation problem.

关键词： Expensive many-objective optimization Knee solution Pareto front estimation Kriging model evolutionary multi-task optimization

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Parameter adaptation in multifactorial evolutionary algorithm for many-task optimization

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MEMETIC COMPUTING 2021年第4期13卷 433-446页

作者： Thang, Ta Bao Dao, Tran Cong Long, Nguyen Hoang Binh, Huynh Thi Thanh Hanoi Univ Sci & Technol Sch Informat & Commun Technol Hanoi Vietnam

The advent of multifactorial optimization (MFO) has made a wind of change in intelligence computation in general and specifically in evolutionary computing. Based on the implicit parallelism of population-based search, MFO optimizes different problems simultaneously and entirely. However, the randomness of knowledge transfers raises the question of how to diminish harmful interactions among tasks for more effective transfers. In recent years, many proposals have been devised to handle this paradigm and improve existing algorithms. Notwithstanding the diversity in their concept, there are few efforts to solve many-task optimization (MaTO) that contains beyond three tasks. In light of this reason, this paper proposes two algorithms named SA-MFEA and LSA-MFEA for MaTO. Instead of utilizing fixed parameters, SA-MFEA and LSA-MFEA adapt the probability of random mating parameter to reduce negative transfers based on the historical memory of successful rmp. Besides, LSA-MFEA is capable of enhancing the exploitation by linear population size reduction. To examine the efficiency of the two proposed algorithms, experiments on various many-task benchmark problems and comparison with several state-of-the-art algorithms have been conducted. The results demonstrated that SA-MFEA and LSA-MFEA are competitive in terms of quality of solutions, convergence trend, and computation time.

关键词： evolutionary multi-task optimization multifactorial evolutionary algorithm Parameters adaptation strategy Many-task optimization

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A Self-Adaptive evolutionary multi-task Based Constrained multi-Objective evolutionary Algorithm

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2023年第4期7卷 1098-1112页

作者： Qiao, Kangjia Liang, Jing Yu, Kunjie Wang, Minghui Qu, Boyang Yue, Caitong Guo, Yinan Zhengzhou Univ Sch Elect Engn Zhengzhou 450001 Peoples R China Zhengzhou Univ Sch Informat Engn Zhengzhou 450001 Peoples R China Zhongyuan Univ Technol Sch Elect & Informat Engn Zhengzhou 450007 Peoples R China China Univ Min & Technol Beijing 100084 Peoples R China

Constrained multi-objective optimization problems (CMOPs) are difficult to solve since they involve the optimization of multiple objectives and the satisfaction of various constraints. Most constrained multi-objective evolutionary algorithms (CMOEAs) are prone to fall into the local optima due to the imbalance between objectives and constraints as well as the poor search ability of the population. To better solve CMOPs, this paper proposes a double-balanced evolutionary multi-task optimization (DBEMTO) algorithm, which evolves two populations to respectively solve the main task (CMOP) and the auxiliary task (MOP extracted from the CMOP). In DBEMTO, three evolutionary strategies are assigned to each population for offspring generation. The three evolutionary strategies include an individual transfer-based inter-task strategy and two intra-task strategies, not only utilizing the information of inter-task but also providing diverse search abilities of intra-task. Moreover, a self-adaptive scheme is developed to self-adaptively employ three strategies, so that the population can balance the information utilization of both intra-task and inter-task. Then, in the environmental selection, the performance of the three strategies is adopted to guide the sharing of the two offspring populations. Compared with several other state-of-the-art CMOEAs, DBEMTO has performed more competitively according to the final results.

关键词： task analysis optimization Statistics Sociology Knowledge transfer multitasking Genetic algorithms Constrained multi-objective optimization evolutionary multi-task optimization knowledge transfer self-adaptive intra-task inter-task

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evolutionary multitasking With Solution Space Cutting for Point Cloud Registration

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2024年第1期8卷 110-125页

作者： Wu, Yue Gong, Peiran Gong, Maoguo Ding, Hangqi Tang, Zedong Liu, Yibo Ma, Wenping Miao, Qiguang Xidian Univ Sch Comp Sci & Technol Key Lab Collaborat Intelligence Syst Minist Educ Xian 710071 Peoples R China Xidian Univ Sch Elect Engn Key Lab Collaborat Intelligence Syst Minist Educ Xian 710071 Peoples R China Xidian Univ Sch Artificial Intelligence Key Lab Intelligent Percept & Image Understanding Minist Educ Xian 710071 Peoples R China

Point cloud registration (PCR) is a popular research topic in computer vision. Recently, the registration method in an evolutionary way has received continuous attention because of its robustness to the initial pose and flexibility in objective function design. However, most evolving registration methods cannot tackle the local optimum well and they have rarely investigated the success ratio, which implies the probability of not falling into local optima and is closely related to the practicality of the algorithm. evolutionary multi-task optimization (EMTO) is a widely used paradigm, which can boost exploration capability through knowledge transfer among related tasks. Inspired by this concept, this study proposes a novel evolving registration algorithm via EMTO, where the multi-task configuration is based on the idea of solution space cutting. Concretely, one task searching in cut space assists another task with complex function landscape in escaping from local optima and enhancing successful registration ratio. To reduce unnecessary computational cost, a sparse-to-dense strategy is proposed. In addition, a novel fitness function robust to various overlap rates as well as a problem-specific metric of computational cost is introduced. Compared with 8 evolving approaches, 4 traditional approaches and 3 deep learning approaches on the object-scale and scene-scale registration datasets, experimental results demonstrate that the proposed method has superior performances in terms of precision and tackling local optima.

关键词： evolutionary multi-task optimization point cloud registration particle swarm optimization

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