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6th International Conference on Data-Driven optimization of Complex Systems (DOCS)

作者： Qian, Yongkang Hao, Xingxing Chen, Li Zhao, Shulin Northwest Univ Sch Informat Sci & Technol Xian Peoples R China

ISBN: (纸本)9798350377859;9798350377842

evolutionary multitask optimization (EMTO) aims to utilize useful information between tasks and optimize multiple tasks at the same time. This is an emerging research direction in evolutionary computation. In recent years, many studies have promoted the progress of EMTO. Nevertheless, most algorithms are only designed for only one similar situation or choose a single transfer strategy in the algorithm, which limits the performance of the algorithm in problems with multiple similar situations. To solve this problem, this paper proposes an algorithm that transfers three evolutionary elements, i.e., the direction, distance, and position, via differential vectors, evolutionary paths, and the distribution of optimal solutions, respectively. Generally, these three elements control the generation of offspring in most cases of evolutionary optimization. Therefore, the combination of these three evolutionary elements is expected to solve multitask optimization problems with multiple similar situations more efficiently. At the same time, this paper designs an adaptive strategy to control the number of transferred offspring for each transfer strategy to avoid negative transfer. In order to verify the effectiveness of the proposed algorithm, comparisons on a single-objective multi-task benchmark with classic and recently advanced algorithms are conducted, and the results verify the effectiveness of the designed algorithm.

关键词： evolutionary algorithm evolutionary multitask optimization knowledge transfer evolutionary element

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Distributed Knowledge Transfer for evolutionary multitask Multimodal optimization

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IEEE TRANSACTIONS ON evolutionary COMPUTATION 2024年第4期28卷 1141-1155页

作者： Gao, Kailai Yang, Cuie Ding, Jinliang Tan, Kay Chen Chai, Tianyou Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China

evolutionary multitasking optimization (EMTO) is a paradigm that optimizes multiple tasks simultaneously to improve the overall performance of all tasks by seamlessly transferring useful knowledge among them. Although EMTO has received significant interest, rare studies consider handling tasks that are multimodal optimization problems (MMOPs) with multiple global optimal solutions. Due to the multiple different modalities of each task, a major challenge of solving multiple MMOPs is how to extract and transfer knowledge across modalities of different tasks. To this end, this article designs a distributed knowledge transfer-based evolutionary multitask multimodal optimization (EMTMO-DKT) approach for solving multiple MMOPs simultaneously by discovering and utilizing local knowledge across modalities of different tasks. Specifically, we first divide the population of each task into multiple subpopulations, where each subpopulation explores a modality. Then, we propose an evolution path-based similarity measurement to measure the local similarities between subpopulations of different tasks. Since the modalities can be locally similar across tasks, we develop a subpopulation cross matching strategy according to the obtained similarities to pair subpopulations of different tasks. In this stage, the successfully paired subpopulations are allowed to transfer knowledge. Finally, the knowledge transfer probability self-adjusting strategy is applied to each subpopulation to balance knowledge transfer and self-evolution, so as to improve search efficiency. In this article, a set of multitask multimodal optimization test problems are constructed to assess the efficacy of compared algorithms. Experimental results on both the benchmark functions and the real-world optimization problem demonstrate that the proposed algorithm can quickly locate more global optima in comparison with state-of-the-art EMTO and multimodal optimization algorithms.

关键词： evolutionary multitask optimization knowledge transfer local similarity multimodal optimization evolutionary multitask optimization knowledge transfer local similarity multimodal optimization

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GrpAvoid: Multigroup Collision-Avoidance Control and optimization for UAV Swarm

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IEEE TRANSACTIONS ON CYBERNETICS 2023年第3期53卷 1776-1789页

作者： Luo, Linbo Wang, Xinyu Ma, Jianfeng Ong, Yew-Soon Xidian Univ Sch Cyber Engn Xian 710071 Peoples R China Xidian Univ Sch Comp Sci & Technol Xian 710071 Peoples R China ASTAR Singapore Singapore

Collision-avoidance control for UAV swarm has recently drawn great attention due to its significant implications in many industrial and commercial applications. However, traditional collision-avoidance models for UAV swarm tend to focus on avoidance at individual UAV level, and no explicit strategy is designed for avoidance among multiple UAV groups. When directly applying these models for multigroup UAV scenarios, the deadlock situation may happen. A group of UAVs may be temporally blocked by other groups in a narrow space and cannot progress toward achieving its goal. To this end, this article proposes a modeling and optimization approach to multigroup UAV collision avoidance. Specifically, group level collision detection and adaption mechanism are introduced, efficiently detecting potential collisions among different UAV groups and restructuring a group into subgroups for better collision and deadlock avoidance. A two-level control model is then designed for realizing collision avoidance among UAV groups and of UAVs within each group. Finally, an evolutionary multitask optimization method is introduced to effectively calibrate the parameters that exist in different levels of our control model, and an adaptive fitness evaluation strategy is proposed to reduce computation overhead in simulation-based optimization. The simulation results show that our model has superior performances in deadlock resolution, motion stability, and distance maintenance in multigroup UAV scenarios compared to the state-of-the-art collision-avoidance models. The model optimization results also show that our model optimization method can largely reduce execution time for computationally-intensive optimization process that involves UAV swarm simulation.

关键词： optimization Adaptation models Collision avoidance Computational modeling Planning System recovery Task analysis evolutionary multitask optimization UAV simulation UAV swarm control

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Adaptive Aggregative multitask Competitive Particle Swarm optimization with Bi-Directional Asymmetric Flip Strategy for High-Dimensional Feature Selection

Adaptive Aggregative Multitask Competitive Particle Swarm Op...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Zhang, Yong Du, Ke-Jing Jiang, Yi Wang, Li-Min Wang, Hua Zhan, Zhi-Hui South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Victoria Univ Inst Sustainable Ind & Liveable Cities Melbourne Vic 8001 Australia Guangdong Univ Finance & Econ Sch Informat Sci Guangzhou 510320 Peoples R China Nankai Univ Coll Artificial Intelligence Tianjin 300350 Peoples R China

ISBN: (纸本)9798400704949

evolutionary multitask optimization (EMTO) has been increasingly employed in addressing high-dimensional feature selection challenges, but current EMTO algorithms still have three deficiencies: First, in task generation, they just consider the linear correlation among features but ignore the nonlinear correlation. Second, they commonly encounter negative knowledge transfer. Third, they are hard to strike an optimal balance between global search capability and computational efficiency. This paper proposes an adaptive aggregative multitask competitive swarm optimization (AAMCSO) for high-dimensional feature selection, which contains three novel and effective strategies to address the above deficiencies. Firstly, AAMCSO proposes a linear-and-nonlinear-correlation-based task generation strategy to generate multiple tasks while considering both linear and nonlinear correlation between features and labels. Secondly, AAMCSO proposes an adaptive aggregative knowledge transfer strategy to adaptively transfer positive knowledge among related tasks. Thirdly, AAMCSO proposes a bi-directional asymmetric flip strategy to guide the population to search for a smaller feature subset with better classification performance. We have conducted extensive comparative experiments on AAMCSO and multiple state-of-the-art feature selection algorithms in high-dimensional feature selection problems with up to 10000 dimensions. The results show that AAMCSO achieves significantly superior performance to the state-of-the-art comparison algorithms in terms of both classification accuracy and feature number.

关键词： evolutionary multitask optimization High-dimensional feature selection Particle swarm optimization Competitive swarm optimization evolutionary computation

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multitask Shape optimization Using a 3-D Point Cloud Autoencoder as Unified Representation

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IEEE TRANSACTIONS ON evolutionary COMPUTATION 2022年第2期26卷 206-217页

作者： Rios, Thiago van Stein, Bas Back, Thomas Sendhoff, Bernhard Menzel, Stefan Honda Res Inst Europe D-63073 Offenbach Germany Leiden Inst Adv Comp Sci Dept Comp Sci NL-2333 CA Leiden Netherlands

The choice of design representations, as of search operators, is central to the performance of evolutionary optimization algorithms, in particular, for multitask problems. The multitask approach pushes further the parallelization aspect of these algorithms by solving simultaneously multiple optimization tasks using a single population. During the search, the operators implicitly transfer knowledge between solutions to the offspring, taking advantage of potential synergies between problems to drive the solutions to optimality. Nevertheless, in order to operate on the individuals, the design space of each task has to be mapped to a common search space, which is challenging in engineering cases without clear semantic overlap between parameters. Here, we apply a 3-D point cloud autoencoder to map the representations from the Cartesian to a unified design representation: the latent space of the autoencoder. The transfer of latent space features between design representations allows the reconstruction of shapes with interpolated characteristics and maintenance of common parts, which potentially improves the performance of the designs in one or more tasks during the optimization. Compared to traditional representations for shape optimization, such as free-form deformation, the latent representation enables more representative design modifications, while keeping the baseline characteristics of the learned classes of objects. We demonstrate the efficiency of our approach in an optimization scenario where we minimize the aerodynamic drag of two different car shapes with common underbodies for cost-efficient vehicle platform design.

关键词： Task analysis optimization Three-dimensional displays Shape Statistics Sociology Knowledge transfer Automotive engineering commonality evolutionary multitask optimization point cloud autoencoder

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A hybrid evolutionary multitask algorithm for the multiobjective vehicle routing problem with time windows

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INFORMATION SCIENCES 2022年 612卷 168-187页

作者： Cai, Yiqiao Cheng, Meiqin Zhou, Ying Liu, Peizhong Guo, Jing-Ming Huaqiao Univ Coll Comp Sci & Technol Xiamen 361021 Peoples R China Xiamen Key Lab Data Secur & Blockchain Technol Xiamen Fujian Peoples R China Shenzhen Inst Informat Technol Sch Comp Sci Shenzhen 518000 Peoples R China Huaqiao Univ Coll Engn Quanzhou 362000 Peoples R China Natl Taiwan Univ Sci & Technol Dept Elect Engn Taipei 10607 Taiwan

The vehicle routing problem with time windows (VRPTW), as an important logistic problem, has been widely investigated in recent decades. However, it is still a great challenge for most existing approaches to solve multiobjective VRPTWs (MOVRPTWs) with many conflicting objectives. In this study, a hybrid evolutionary multitask algorithm, termed HEMT, is proposed to address MOVRPTWs under the framework of evolutionary multitasking, where multiple MOVRPTWs are optimized simultaneously by leveraging the similarity between them. In particular, HEMT is characterized by three aspects: (1) an exploration stage with knowledge transfer (ESKT) is designed to globally explore the search space by transferring knowledge across different MOVRPTWs;(2) an exploitation stage with knowledge reuse (ESKR) is employed to further promote the quality of solutions by conducting local searches and reusing problem-specific knowledge;and (3) a tradeoff mechanism (TOM) is suggested to adaptively switch the search between ESKT and ESKR. Furthermore, to verify the efficacy of the proposed HEMT, four suites of new multitasking instances are generated based on 45 real-world MOVRPTW benchmarks under different multitask environments. The experimental results show that HEMT not only effectively solves multiple MOVRPTWs simultaneously but also achieves better performances than the traditional single-task counterparts.

关键词： Vehicle routing problem with time windows evolutionary multitask optimization Multiobjective optimization Knowledge transfer

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Point cloud registration via sampling-based evolutionary multitasking

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SWARM AND evolutionary COMPUTATION 2024年 89卷

作者： Ding, Hangqi Wu, Yue Gong, Maoguo Li, Hao Gong, Peiran Miao, Qiguang Ma, Wenping Duan, Yiping Tao, Xiaoming Xidian Univ Sch Comp Sci & Technol Xian 710071 Peoples R China Xidian Univ Sch Elect Engn Xian 710071 Peoples R China Xidian Univ Sch Artificial Intelligence Xian 710071 Peoples R China Minist Educ Key Lab Collaborat Intelligence Syst Xian 710071 Peoples R China Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Inner Mongolia Normal Univ Coll Math Sci Hohhot 010022 Peoples R China

Point cloud registration (PCR) is an important task in both computer vision and pattern recognition. However, since the success of most existing two -stage PCR methods largely depends on the implicit assumption that the first coarse registration stage can provide an approximately optimal initial transformation for the second fine stage, these methods suffer from low success rate when the assumption is not satisfied. evolutionary multitasking can find better solutions for multiple tasks simultaneously by transferring knowledge among multiple related tasks. Inspired by evolutionary multitasking, this study proposes a sampling -based multitask optimization method for PCR, which solves the problem of low registration success rate by sharing knowledge between two related tasks generated by a dataset. Specifically, one task is for roughly aligning the sampled point cloud, and the other is for the original unsampled point cloud. The main purpose of the first task is to continuously provide useful knowledge to the second task so that it can achieve precise alignment. Then, a new knowledge transfer mechanism guided by historical information is proposed, which can effectively adjust the search behavior of the original unsampled task. In addition, we develop a dynamic task complement strategy to complement the accuracy that may be lost by downsampling. The results on five different datasets show that the proposed method can achieve higher registration accuracy with higher success rate compared to other state-of-the-art PCR methods.

关键词： evolutionary multitasking evolutionary multitask optimization Knowledge transfer Point cloud registration

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AT-MFCGA: An Adaptive Transfer-guided Multifactorial Cellular Genetic Algorithm for evolutionary multitasking

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INFORMATION SCIENCES 2021年 570卷 577-598页

作者： Osaba, Eneko Del Ser, Javier Martinez, Aritz D. Lobo, Jesus L. Herrera, Francisco TECNALIA Basque Res & Technol Alliance BRTA Derio 48160 Bizkaia Spain Basque Ctr Appl Math BCAM Bilbao 48009 Spain Univ Granada DaSCI Andalusian Inst Data Sci & Computat Intelli Granada Spain

Transfer optimization is an incipient research area dedicated to solving multiple optimization tasks simultaneously. Among the different approaches that can address this problem effectively, evolutionary multitasking resorts to concepts from evolutionary Computation to solve multiple problems within a single search process. In this paper we introduce a novel adaptive metaheuristic algorithm to deal with evolutionary multitasking environments coined as Adaptive Transfer-guided Multifactorial Cellular Genetic Algorithm (ATMFCGA). AT-MFCGA relies on cellular automata to implement mechanisms in order to exchange knowledge among the optimization problems under consideration. Furthermore, our approach is able to explain by itself the synergies among tasks that were encountered and exploited during the search, which helps us to understand interactions between related optimization tasks. A comprehensive experimental setup is designed to assess and compare the performance of AT-MFCGA to that of other renowned evolutionary multitasking alternatives (MFEA and MFEA-II). Experiments comprise 11 multitasking scenarios composed of 20 instances of 4 combinatorial optimization problems, yielding the largest discrete multitasking environment solved to date. Results are conclusive in regard to the superior quality of solutions provided by AT-MFCGA with respect to the rest of the methods, which are complemented by a quantitative examination of the genetic transferability among tasks throughout the search process. (c) 2021 Elsevier Inc. All rights reserved.

关键词： multitasking Transfer optimization evolutionary multitask optimization Multifactorial evolutionary algorithm

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Many-task evolutionary algorithm with adaptive knowledge transfer via density-based clustering

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KNOWLEDGE-BASED SYSTEMS 2023年第1期278卷

作者： Bao, Chunteng Gao, Diju Ding, Yi Xu, Lihong Goodman, Erik D. Shanghai Maritime Univ Key Lab Marine Technol & Control Engn Minist Transport Shanghai 201306 Peoples R China Shanghai Maritime Univ Logist Res Ctr Shanghai 201306 Peoples R China Tongji Univ Coll Elect & Informat Engn Shanghai 201804 Peoples R China Michigan State Univ BEACON Ctr Study Evolut Act E Lansing MI 48824 USA

evolutionary multitask optimization (EMTO) explores how to solve two or more related optimization tasks simultaneously by exploiting useful knowledge of other related tasks while solving one task, with the goal of enhancing the performance of the algorithm in solving each task. There has been a large body of work in the literature on knowledge transfer and reuse. However, when the number of tasks increases to a certain extent, the potential negative knowledge transfer between tasks becomes a great challenge for most multitask evolutionary algorithms. To allay this threat, this study proposes an adaptive knowledge transfer strategy for evolutionary many-task optimization via density-based clustering (AEMaTO-DC). AEMaTO-DC contains an adaptive mating selection mechanism, a correlation task evaluation and selection mechanism and a cluster-based knowledge interaction mechanism between tasks. Specifically, AEMaTO-DC regulates the probability of knowledge transfer by comparing the relative intensity of the intertask evolution rate and intratask evolution rate. In knowledge transfer, AEMaTO-DC selects related tasks by a maximum mean discrepancy metric, and it determines the intensity of intertask interaction by a density-based clustering method. We compare the performance of AEMaTO-DC with several state-of-the-art multitask evolutionary algorithms on a variety of synthetic benchmarks and a real-world multitask optimization problem. The experimental results demonstrate that AEMaTO-DC shows competitive success rates in solving the many-task problems used, and it takes advantage of synergistic relationships among solution processes for the tasks addressed.(c) 2023 Elsevier B.V. All rights reserved.

关键词： Many-task optimization Intertask synergies Knowledge transfer evolutionary multitask optimization Density-based clustering

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Hetero-Dimensional multitask Neuroevolution for Chaotic Time Series Prediction

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IEEE ACCESS 2020年 8卷 123135-123150页

作者： Zhang, Daoqing Jiang, Mingyan Shandong Univ Sch Informat Sci & Engn Qingdao 266237 Peoples R China

Chaotic time series prediction has important research and application value, and neural network-based prediction methods have problems such as low accuracy and difficulty in determining the number of nodes in the hidden layer. In recent years, as a representative of evolutionary multitask optimization algorithms(EMTO), multifactorial evolutionary algorithms (MFEA) have achieved excellent results in multitask optimization (MTO) problems, but it does not work well in solving hetero-dimensional multitask problem. We analyzed this phenomenon and proposed the concept of harmful transfer. Then to solve these problems, in this paper, we propose the hetero-dimensional multitask neuroevolution algorithms (HD-MFEA neuroevolution) for chaotic time series prediction. First, we propose hetero-dimensional assortative mating algorithm and self-adaption elite replacement algorithm, so that the hetero-dimensional multitask evolutionary algorithm (HD-MFEA) can overcome the problems caused by harmful transfer on hetero-dimensional multitask problem. Second, in order to adapt to the hierarchical characteristics of neural networks, we propose the neural level segmental coding strategy. Third, we consider the optimization of neural networks with different hidden layer nodes as hetero-dimensional multitask problem and use the HD-MFEA neuroevolution to solve this problem. We verify the effectiveness of the HD-MFEA on six hetero-dimensional multitask benchmark problems. The Mackey-Glass, Lorenz and Sunspot time series are used to demonstrate the performance of the HD-MFEA neuroevolution. Experimental results have showed that the HD-MFEA neuroevolution performs well in predicting the chaotic time series problems.

关键词： Chaotic time series prediction evolutionary algorithm evolutionary multitask optimization multifactorial evolutionary algorithm neuroevolution

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