Subjects -> ENGINEERING (Total: 2688 journals)
    - CHEMICAL ENGINEERING (229 journals)
    - CIVIL ENGINEERING (237 journals)
    - ELECTRICAL ENGINEERING (176 journals)
    - ENGINEERING (1325 journals)
    - ENGINEERING MECHANICS AND MATERIALS (452 journals)
    - HYDRAULIC ENGINEERING (56 journals)
    - INDUSTRIAL ENGINEERING (98 journals)
    - MECHANICAL ENGINEERING (115 journals)

ENGINEERING (1325 journals)                  1 2 3 4 5 6 7 | Last

Showing 1 - 200 of 1205 Journals sorted by number of followers
Composite Structures     Hybrid Journal   (Followers: 247)
Composites Part B : Engineering     Hybrid Journal   (Followers: 221)
IEEE Spectrum     Full-text available via subscription   (Followers: 219)
ACS Nano     Hybrid Journal   (Followers: 183)
Composites Part A : Applied Science and Manufacturing     Hybrid Journal   (Followers: 175)
IEEE Geoscience and Remote Sensing Letters     Hybrid Journal   (Followers: 151)
Composites Science and Technology     Hybrid Journal   (Followers: 150)
IEEE Instrumentation & Measurement Magazine     Hybrid Journal   (Followers: 148)
IEEE Communications Magazine     Full-text available via subscription   (Followers: 140)
IEEE Engineering Management Review     Full-text available via subscription   (Followers: 117)
IEEE Antennas and Propagation Magazine     Hybrid Journal   (Followers: 112)
IEEE Transactions on Control Systems Technology     Hybrid Journal   (Followers: 111)
IEEE Transactions on Instrumentation and Measurement     Hybrid Journal   (Followers: 106)
IEEE Transactions on Signal Processing     Hybrid Journal   (Followers: 92)
IEEE Antennas and Wireless Propagation Letters     Hybrid Journal   (Followers: 88)
IEEE Industry Applications Magazine     Full-text available via subscription   (Followers: 82)
IEEE Transactions on Antennas and Propagation     Full-text available via subscription   (Followers: 79)
IEEE Transactions on Engineering Management     Hybrid Journal   (Followers: 74)
Engineering Failure Analysis     Hybrid Journal   (Followers: 68)
IEEE Microwave Magazine     Full-text available via subscription   (Followers: 63)
IEEE Signal Processing Letters     Hybrid Journal   (Followers: 60)
IEEE Transactions on Reliability     Hybrid Journal   (Followers: 53)
Experimental Techniques     Hybrid Journal   (Followers: 51)
IET Radar, Sonar & Navigation     Open Access   (Followers: 50)
IEEE Transactions on Microwave Theory and Techniques     Hybrid Journal   (Followers: 49)
Control Engineering Practice     Hybrid Journal   (Followers: 46)
IEEE Journal of Selected Topics in Signal Processing     Hybrid Journal   (Followers: 43)
Biotechnology Progress     Hybrid Journal   (Followers: 42)
IEEE Potentials     Full-text available via subscription   (Followers: 42)
IEEE Journal on Selected Areas in Communications     Hybrid Journal   (Followers: 39)
Heat Transfer Engineering     Hybrid Journal   (Followers: 36)
IET Microwaves, Antennas & Propagation     Open Access   (Followers: 35)
International Journal for Numerical Methods in Engineering     Hybrid Journal   (Followers: 35)
IEEE Microwave and Wireless Components Letters     Hybrid Journal   (Followers: 35)
Digital Signal Processing     Hybrid Journal   (Followers: 34)
IEEE Transactions on Knowledge and Data Engineering     Hybrid Journal   (Followers: 32)
AIChE Journal     Hybrid Journal   (Followers: 31)
Computing in Science & Engineering     Full-text available via subscription   (Followers: 31)
Computers & Geosciences     Hybrid Journal   (Followers: 30)
Flow, Turbulence and Combustion     Hybrid Journal   (Followers: 30)
Coastal Management     Hybrid Journal   (Followers: 29)
Canadian Geotechnical Journal     Hybrid Journal   (Followers: 28)
GPS Solutions     Hybrid Journal   (Followers: 28)
Fluid Dynamics     Hybrid Journal   (Followers: 27)
Bell Labs Technical Journal     Hybrid Journal   (Followers: 27)
Géotechnique     Hybrid Journal   (Followers: 27)
IEEE Transactions on Information Theory     Hybrid Journal   (Followers: 27)
IEEE Transactions on Power Delivery     Hybrid Journal   (Followers: 26)
Applied Energy     Partially Free   (Followers: 26)
Advances in Engineering Software     Hybrid Journal   (Followers: 26)
IEEE Journal of Solid-State Circuits     Full-text available via subscription   (Followers: 24)
Corrosion Science     Hybrid Journal   (Followers: 23)
Engineering & Technology     Hybrid Journal   (Followers: 22)
IET Image Processing     Open Access   (Followers: 22)
Intermetallics     Hybrid Journal   (Followers: 21)
Combustion, Explosion, and Shock Waves     Hybrid Journal   (Followers: 21)
IEEE Transactions on Electronics Packaging Manufacturing     Hybrid Journal   (Followers: 21)
IET Signal Processing     Open Access   (Followers: 21)
IEEE Transactions on Circuits and Systems II: Express Briefs     Hybrid Journal   (Followers: 20)
Advanced Synthesis & Catalysis     Hybrid Journal   (Followers: 20)
Implementation Science     Open Access   (Followers: 20)
International Journal for Numerical Methods in Fluids     Hybrid Journal   (Followers: 19)
Engineering Optimization     Hybrid Journal   (Followers: 19)
International Communications in Heat and Mass Transfer     Hybrid Journal   (Followers: 19)
Electrophoresis     Hybrid Journal   (Followers: 18)
IET Circuits, Devices & Systems     Open Access   (Followers: 18)
IEEE/ACM Transactions on Computational Biology and Bioinformatics     Hybrid Journal   (Followers: 18)
International Journal of Adhesion and Adhesives     Hybrid Journal   (Followers: 18)
IEEE Transactions on Intelligent Transportation Systems     Hybrid Journal   (Followers: 17)
Experiments in Fluids     Hybrid Journal   (Followers: 17)
Computational Geosciences     Hybrid Journal   (Followers: 17)
Integration     Hybrid Journal   (Followers: 16)
IEEE Transactions on Energy Conversion     Hybrid Journal   (Followers: 16)
Engineering Geology     Hybrid Journal   (Followers: 16)
European Journal of Mass Spectrometry     Hybrid Journal   (Followers: 16)
Energy Conversion and Management     Hybrid Journal   (Followers: 15)
Bulletin of Engineering Geology and the Environment     Hybrid Journal   (Followers: 15)
Coastal Engineering     Hybrid Journal   (Followers: 15)
IEEE Transactions on Magnetics     Hybrid Journal   (Followers: 14)
IEEE Journal of Biomedical and Health Informatics     Hybrid Journal   (Followers: 14)
IEEE Transactions on Automation Science and Engineering     Full-text available via subscription   (Followers: 13)
IEEE Transactions on Evolutionary Computation     Hybrid Journal   (Followers: 13)
Electromagnetics     Hybrid Journal   (Followers: 13)
Computers and Geotechnics     Hybrid Journal   (Followers: 12)
IEEE Transactions on Semiconductor Manufacturing     Hybrid Journal   (Followers: 12)
IET Renewable Power Generation     Open Access   (Followers: 12)
Human Factors in Ergonomics & Manufacturing     Hybrid Journal   (Followers: 12)
IEEE Transactions on Professional Communication     Hybrid Journal   (Followers: 11)
Biomedical Engineering     Hybrid Journal   (Followers: 11)
IEEE Transactions on Education     Hybrid Journal   (Followers: 11)
CIRP Annals - Manufacturing Technology     Hybrid Journal   (Followers: 11)
Heat Transfer - Asian Research     Hybrid Journal   (Followers: 11)
IEEE Journal of Oceanic Engineering     Hybrid Journal   (Followers: 11)
International Journal of Antennas and Propagation     Open Access   (Followers: 10)
Proceedings of the Institution of Civil Engineers - Geotechnical Engineering     Hybrid Journal   (Followers: 10)
IEEE Transactions on Nuclear Science     Hybrid Journal   (Followers: 10)
IEEE Transactions on Plasma Science     Hybrid Journal   (Followers: 10)
Computers & Mathematics with Applications     Full-text available via subscription   (Followers: 9)
Fuel Cells Bulletin     Full-text available via subscription   (Followers: 9)
Computational Optimization and Applications     Hybrid Journal   (Followers: 9)
Annals of Science     Hybrid Journal   (Followers: 9)
European Journal of Engineering Education     Hybrid Journal   (Followers: 9)
Applied Catalysis B: Environmental     Hybrid Journal   (Followers: 9)
Biomedical Microdevices     Hybrid Journal   (Followers: 8)
IEEE Technology and Society Magazine     Full-text available via subscription   (Followers: 8)
Fuel Cells     Hybrid Journal   (Followers: 8)
Adaptive Behavior     Hybrid Journal   (Followers: 8)
Proceedings of the Institution of Civil Engineers - Bridge Engineering     Hybrid Journal   (Followers: 8)
Energy Engineering     Full-text available via subscription   (Followers: 8)
IEEE Transactions on Advanced Packaging     Full-text available via subscription   (Followers: 8)
Clay Minerals     Hybrid Journal   (Followers: 8)
Continuum Mechanics and Thermodynamics     Hybrid Journal   (Followers: 8)
Applied Catalysis A: General     Hybrid Journal   (Followers: 7)
International Journal of Applied Ceramic Technology     Hybrid Journal   (Followers: 7)
Basin Research     Hybrid Journal   (Followers: 7)
Discrete Optimization     Full-text available via subscription   (Followers: 7)
Designs, Codes and Cryptography     Hybrid Journal   (Followers: 7)
IEEE Journal of Selected Topics in Quantum Electronics     Hybrid Journal   (Followers: 7)
Environmental and Ecological Statistics     Hybrid Journal   (Followers: 7)
Biomicrofluidics     Open Access   (Followers: 7)
Geothermics     Hybrid Journal   (Followers: 7)
Fuel and Energy Abstracts     Full-text available via subscription   (Followers: 7)
IEEE Vehicular Technology Magazine     Full-text available via subscription   (Followers: 7)
Catalysis Communications     Hybrid Journal   (Followers: 7)
Computers and Electronics in Agriculture     Hybrid Journal   (Followers: 7)
Computer Applications in Engineering Education     Hybrid Journal   (Followers: 6)
Computing and Visualization in Science     Hybrid Journal   (Followers: 6)
Fusion Engineering and Design     Hybrid Journal   (Followers: 6)
Applied Clay Science     Hybrid Journal   (Followers: 6)
Composite Interfaces     Hybrid Journal   (Followers: 6)
Formal Methods in System Design     Hybrid Journal   (Followers: 6)
Acta Geotechnica     Hybrid Journal   (Followers: 6)
Advances in OptoElectronics     Open Access   (Followers: 6)
International Journal of Adaptive Control and Signal Processing     Hybrid Journal   (Followers: 5)
IEEE Transactions on Vehicular Technology     Hybrid Journal   (Followers: 5)
IET Science, Measurement & Technology     Open Access   (Followers: 5)
IEEE Transactions on Applied Superconductivity     Hybrid Journal   (Followers: 5)
International Journal of Architectural Computing     Full-text available via subscription   (Followers: 5)
Finite Fields and Their Applications     Full-text available via subscription   (Followers: 5)
Focus on Powder Coatings     Full-text available via subscription   (Followers: 5)
Engineering With Computers     Hybrid Journal   (Followers: 5)
Proceedings of the Institution of Civil Engineers - Engineering Sustainability     Hybrid Journal   (Followers: 5)
Archives of Computational Methods in Engineering     Hybrid Journal   (Followers: 5)
Active and Passive Electronic Components     Open Access   (Followers: 5)
Proceedings of the Institution of Civil Engineers - Ground Improvement     Hybrid Journal   (Followers: 4)
Frontiers in Energy     Hybrid Journal   (Followers: 4)
Adsorption     Hybrid Journal   (Followers: 4)
Catalysis Today     Hybrid Journal   (Followers: 4)
Applied Numerical Mathematics     Hybrid Journal   (Followers: 4)
Current Applied Physics     Full-text available via subscription   (Followers: 4)
Fluid Phase Equilibria     Hybrid Journal   (Followers: 4)
Graphs and Combinatorics     Hybrid Journal   (Followers: 4)
Filtration & Separation     Full-text available via subscription   (Followers: 4)
Annals of Pure and Applied Logic     Open Access   (Followers: 4)
Grass and Forage Science     Hybrid Journal   (Followers: 4)
Catalysis Surveys from Asia     Hybrid Journal   (Followers: 4)
Informatik-Spektrum     Hybrid Journal   (Followers: 3)
Engineering Computations     Hybrid Journal   (Followers: 3)
European Journal of Combinatorics     Full-text available via subscription   (Followers: 3)
Applicable Algebra in Engineering, Communication and Computing     Hybrid Journal   (Followers: 3)
Chaos : An Interdisciplinary Journal of Nonlinear Science     Hybrid Journal   (Followers: 3)
Concurrent Engineering     Hybrid Journal   (Followers: 3)
Focus on Pigments     Full-text available via subscription   (Followers: 3)
Annals of Combinatorics     Hybrid Journal   (Followers: 3)
Frontiers of Environmental Science & Engineering     Hybrid Journal   (Followers: 3)
Fuzzy Sets and Systems     Hybrid Journal   (Followers: 3)
Catalysis Letters     Hybrid Journal   (Followers: 3)
IET Generation, Transmission & Distribution     Open Access   (Followers: 2)
Historical Records of Australian Science     Hybrid Journal   (Followers: 2)
IET Optoelectronics     Open Access   (Followers: 2)
Assembly Automation     Hybrid Journal   (Followers: 2)
International Journal of Abrasive Technology     Hybrid Journal   (Followers: 2)
Aerobiologia     Hybrid Journal   (Followers: 2)
Cellular and Molecular Neurobiology     Hybrid Journal   (Followers: 2)
Comptes Rendus : Mécanique     Open Access   (Followers: 2)
Chinese Journal of Catalysis     Full-text available via subscription   (Followers: 2)
IEEE Latin America Transactions     Full-text available via subscription   (Followers: 2)
Communications in Numerical Methods in Engineering     Hybrid Journal   (Followers: 2)
ESAIM: Control Optimisation and Calculus of Variations     Open Access   (Followers: 2)
Focus on Surfactants     Full-text available via subscription   (Followers: 2)
Engineering Analysis with Boundary Elements     Hybrid Journal   (Followers: 2)
Chaos, Solitons & Fractals     Hybrid Journal   (Followers: 1)
Foundations of Science     Hybrid Journal   (Followers: 1)
Forschung     Hybrid Journal   (Followers: 1)
European Journal of Lipid Science and Technology     Hybrid Journal   (Followers: 1)
Antarctic Science     Hybrid Journal   (Followers: 1)
Épités - Épitészettudomány     Full-text available via subscription   (Followers: 1)
Dyes and Pigments     Hybrid Journal   (Followers: 1)
Bautechnik     Hybrid Journal   (Followers: 1)
Biointerphases     Open Access   (Followers: 1)
Designed Monomers and Polymers     Open Access   (Followers: 1)
Color Research & Application     Hybrid Journal   (Followers: 1)
Abstract and Applied Analysis     Open Access   (Followers: 1)
Focus on Catalysts     Full-text available via subscription  
ESAIM: Proceedings     Open Access  
Environmetrics     Hybrid Journal  
COMBINATORICA     Hybrid Journal  
Chinese Science Bulletin     Open Access  
Calphad     Hybrid Journal  
Boundary Value Problems     Open Access  

        1 2 3 4 5 6 7 | Last

Similar Journals
Journal Cover
IEEE Transactions on Evolutionary Computation
Journal Prestige (SJR): 3.493
Citation Impact (citeScore): 12
Number of Followers: 13  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1089-778X
Published by IEEE Homepage  [228 journals]
  • IEEE Transactions on Evolutionary Computation Publication Information

    • Free pre-print version: Loading...

      Abstract: Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.
      PubDate: April 2022
      Issue No: Vol. 26, No. 2 (2022)
       
  • IEEE Transactions on Evolutionary Computation Society Information

    • Free pre-print version: Loading...

      Abstract: Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.
      PubDate: April 2022
      Issue No: Vol. 26, No. 2 (2022)
       
  • IEEE Transactions on Evolutionary Computation Information for Authors

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      Abstract: These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal.
      PubDate: April 2022
      Issue No: Vol. 26, No. 2 (2022)
       
  • Guest Editorial Special Issue on Multitask Evolutionary Computation

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      Authors: Abhishek Gupta;Yew-Soon Ong;Kenneth A. De Jong;Mengjie Zhang;
      Pages: 202 - 205
      Abstract: It is our pleasure to introduce this special issue on multitask evolutionary computation (MTEC), focusing on novel methodologies and applications of evolutionary algorithms (EAs) crafted to perform multiple search and optimization tasks jointly. EAs are population-based methods inspired by principles of natural evolution that have provided a gradient-free path to solving complex learning and optimization problems. However, unlike the natural world where evolution has engendered diverse species and produced differently skilled subpopulations, in silico EAs are typically designed to evolve a set of solutions specialized for just a single target task. This convention of problem solving in isolation tends to curtail the power of implicit parallelism of a population. Skills evolved for a given problem instance do not naturally transfer to populations tasked to solve another. Hence, convergence rates remain restrained, even in settings where related tasks with overlapping search spaces, similar optimal solutions, or with other forms of reusable information, are routinely recurring.
      PubDate: April 2022
      Issue No: Vol. 26, No. 2 (2022)
       
  • Multitask Shape Optimization Using a 3-D Point Cloud Autoencoder as
           Unified Representation

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      Authors: Thiago Rios;Bas van Stein;Thomas Bäck;Bernhard Sendhoff;Stefan Menzel;
      Pages: 206 - 217
      Abstract: 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.
      PubDate: April 2022
      Issue No: Vol. 26, No. 2 (2022)
       
  • Learning and Sharing: A Multitask Genetic Programming Approach to Image
           Feature Learning

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      Authors: Ying Bi;Bing Xue;Mengjie Zhang;
      Pages: 218 - 232
      Abstract: Using evolutionary computation algorithms to solve multiple tasks with knowledge sharing is a promising approach. Image feature learning can be considered as a multitask learning problem because different tasks may have a similar feature space. Genetic programming (GP) has been successfully applied to image feature learning for classification. However, most of the existing GP methods solve one task, independently, using sufficient training data. No multitask GP method has been developed for image feature learning. Therefore, this article develops a multitask GP approach to image feature learning for classification with limited training data. Owing to the flexible representation of GP, a new knowledge sharing mechanism based on a new individual representation is developed to allow GP to automatically learn what to share across two tasks and to improve its learning performance. The shared knowledge is encoded as a common tree, which can represent the common/general features of two tasks. With the new individual representation, each task is solved using the features extracted from a common tree and a task-specific tree representing task-specific features. To find the best common and task-specific trees, a new evolutionary search process and fitness functions are developed. The performance of the new approach is examined on six multitask learning problems of 12 image classification datasets with limited training data and compared with 17 competitive methods. The experimental results show that the new approach outperforms these comparison methods in almost all the comparisons. Further analysis reveals that the new approach learns simple yet effective common trees with high effectiveness and transferability.
      PubDate: April 2022
      Issue No: Vol. 26, No. 2 (2022)
       
  • Adaptive Multifactorial Evolutionary Optimization for Multitask
           Reinforcement Learning

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      Authors: Aritz D. Martinez;Javier Del Ser;Eneko Osaba;Francisco Herrera;
      Pages: 233 - 247
      Abstract: Evolutionary computation has largely exhibited its potential to complement conventional learning algorithms in a variety of machine learning tasks, especially those related to unsupervised (clustering) and supervised learning. It has not been until lately when the computational efficiency of evolutionary solvers has been put in prospective for training reinforcement learning models. However, most studies framed so far within this context have considered environments and tasks conceived in isolation, without any exchange of knowledge among related tasks. In this manuscript we present A-MFEA-RL, an adaptive version of the well-known MFEA algorithm whose search and inheritance operators are tailored for multitask reinforcement learning environments. Specifically, our approach includes crossover and inheritance mechanisms for refining the exchange of genetic material, which rely on the multilayered structure of modern deep-learning-based reinforcement learning models. In order to assess the performance of the proposed approach, we design an extensive experimental setup comprising multiple reinforcement learning environments of varying levels of complexity, over which the performance of A-MFEA-RL is compared to that furnished by alternative nonevolutionary multitask reinforcement learning approaches. As concluded from the discussion of the obtained results, A-MFEA-RL not only achieves competitive success rates over the simultaneously addressed tasks, but also fosters the exchange of knowledge among tasks that could be intuitively expected to keep a degree of synergistic relationship.
      PubDate: April 2022
      Issue No: Vol. 26, No. 2 (2022)
       
  • A Multivariation Multifactorial Evolutionary Algorithm for Large-Scale
           Multiobjective Optimization

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      Authors: Yinglan Feng;Liang Feng;Sam Kwong;Kay Chen Tan;
      Pages: 248 - 262
      Abstract: For solving large-scale multiobjective problems (LSMOPs), the transformation-based methods have shown promising search efficiency, which varies the original problem as a new simplified problem and performs the optimization in simplified spaces instead of the original problem space. Owing to the useful information provided by the simplified searching space, the performance of LSMOPs has been improved to some extent. However, it is worth noting that the original problem has changed after the variation, and there is thus no guarantee of the preservation of the original global or near-global optimum in the newly generated space. In this article, we propose to solve LSMOPs via a multivariation multifactorial evolutionary algorithm. In contrast to existing transformation-based methods, the proposed approach intends to conduct an evolutionary search on both the original space of the LSMOP and multiple simplified spaces constructed in a multivariation manner concurrently. In this way, useful traits found along the search can be seamlessly transferred from the simplified problem spaces to the original problem space toward efficient problem solving. Besides, since the evolutionary search is also performed in the original problem space, preserving the original global optimal solution can be guaranteed. To evaluate the performance of the proposed framework, comprehensive empirical studies are carried out on a set of LSMOPs with two to three objectives and 500–5000 variables. The experimental results highlight the efficiency and effectiveness of the proposed method compared to the state-of-the-art methods for large-scale multiobjective optimization.
      PubDate: April 2022
      Issue No: Vol. 26, No. 2 (2022)
       
  • An Evolutionary Multitasking Optimization Framework for Constrained
           Multiobjective Optimization Problems

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      Authors: Kangjia Qiao;Kunjie Yu;Boyang Qu;Jing Liang;Hui Song;Caitong Yue;
      Pages: 263 - 277
      Abstract: When addressing constrained multiobjective optimization problems (CMOPs) via evolutionary algorithms, various constraints and multiple objectives need to be satisfied and optimized simultaneously, which causes difficulties for the solver. In this article, an evolutionary multitasking (EMT)-based constrained multiobjective optimization (EMCMO) framework is developed to solve CMOPs. In EMCMO, the optimization of a CMOP is transformed into two related tasks: one task is for the original CMOP, and the other task is only for the objectives by ignoring all constraints. The main purpose of the second task is to continuously provide useful knowledge of objectives to the first task, thus facilitating solving the CMOP. Specially, the genes carried by parent individuals or offspring individuals are dynamically regarded as useful knowledge due to the different complementarities of the two tasks. Moreover, the useful knowledge is found by the designed tentative method and transferred to improve the performance of the two tasks. To the best of our knowledge, this is the first attempt to use EMT to solve CMOPs. To verify the performance of EMCMO, an instance of EMCMO is obtained by employing a genetic algorithm as the optimizer. Comprehensive experiments are conducted on four benchmark test suites to verify the effectiveness of knowledge transfer. Furthermore, compared with other state-of-the-art constrained multiobjective optimization algorithms, EMCMO can produce better or at least comparable performance.
      PubDate: April 2022
      Issue No: Vol. 26, No. 2 (2022)
       
  • Evolutionary Competitive Multitasking Optimization

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      Authors: Genghui Li;Qingfu Zhang;Zhenkun Wang;
      Pages: 278 - 289
      Abstract: This article introduces a special multitasking optimization problem (MTOP) called the competitive MTOP (CMTOP). Its distinctive characteristics are that all tasks’ objectives are comparable, and its optimal solution is the best one among the optimal solutions of all the individual problems. This article proposes an evolutionary algorithm with an online resource allocation strategy and an adaptive information transfer mechanism to solve the CMTOP. The experimental results on benchmark and real-world problems show that our proposed algorithm is effective and efficient.
      PubDate: April 2022
      Issue No: Vol. 26, No. 2 (2022)
       
  • Evolutionary Multitask Optimization With Adaptive Knowledge Transfer

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      Authors: Hao Xu;A. K. Qin;Siyu Xia;
      Pages: 290 - 303
      Abstract: Evolutionary multitask optimization (EMTO) studies how to simultaneously solve multiple optimization tasks via evolutionary algorithms (EAs) while making the useful knowledge acquired from solving one task to assist solving other tasks, aiming to improve the overall performance of solving each individual task. Recent years have seen a large body of EMTO works based on different kinds of EAs and studying one or more aspects in how to represent, extract, transfer, and reuse knowledge. A key challenge to EMTO is the occurrence of negative knowledge transfer between tasks, which becomes severer when the total number of tasks increases. To address this issue, we propose an adaptive EMTO (AEMTO) framework. This framework can adapt knowledge transfer frequency, knowledge source selection, and knowledge transfer intensity in a synergistic way to make the best use of knowledge transfer, especially when facing many tasks. We implement the proposed AEMTO framework and evaluate our implementation on three suites of MTO problems with 2, 10, and 50 tasks and one real-world MTO problem with 2000 tasks in comparison to several state-of-the-art EMTO methods with certain adaptation strategies regarding knowledge transfer and the single-task optimization counterpart of the proposed method. Experimental results have demonstrated the effectiveness of the adaptive knowledge transfer strategies used in AEMTO and the overall performance superiority of AEMTO.
      PubDate: April 2022
      Issue No: Vol. 26, No. 2 (2022)
       
  • Solving Multitask Optimization Problems With Adaptive Knowledge Transfer
           via Anomaly Detection

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      Authors: Chao Wang;Jing Liu;Kai Wu;Zhaoyang Wu;
      Pages: 304 - 318
      Abstract: Evolutionary multitask optimization (EMTO) has recently attracted widespread attention in the evolutionary computation community, which solves two or more tasks simultaneously to improve the convergence characteristics of tasks when individually optimized. Effective knowledge between tasks is transferred by taking advantage of the parallelism of population-based search. Without any prior knowledge about tasks, it is a challenging problem of how to adaptively transfer effective knowledge between tasks and reduce the impact of negative transfer in EMTO. However, these two issues are rarely studied simultaneously in the existing literature. Besides, in complex many-task environments, the potential relationships among individuals from highly diverse populations associated with tasks directly determine the effectiveness of cross-task knowledge transfer. Keeping those in mind, we propose a multitask evolutionary algorithm based on anomaly detection (MTEA-AD). Specifically, each task is assigned a population and an anomaly detection model. Each anomaly detection model is used to learn the relationship among individuals between the current task and the other tasks online. Individuals that may carry negative knowledge are identified as outliers, and candidate transferred individuals identified by the anomaly detection model are selected to assist the current task, which may carry common knowledge across the current task and other tasks. Furthermore, to realize the adaptive control of the degree of knowledge transfer, the successfully transferred individuals that survive to the next generation through the elitism are used to update the anomaly detection parameter. The fair competition between offspring and candidate transferred individuals can effectively reduce the risk of negative transfer. Finally, the empirical studies on a series of synthetic benchmarks and a practical study are conducted to verify the effectiveness of MTEA-AD. The experimental results demonstrate that -ur proposal can adaptively adjust the degree of knowledge transfer through the anomaly detection model to achieve highly competitive performance compared to several state-of-the-art EMTO methods.
      PubDate: April 2022
      Issue No: Vol. 26, No. 2 (2022)
       
  • Evolutionary Many-Task Optimization Based on Multisource Knowledge
           Transfer

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      Authors: Zhengping Liang;Xiuju Xu;Ling Liu;Yaofeng Tu;Zexuan Zhu;
      Pages: 319 - 333
      Abstract: Multitask optimization aims to solve two or more optimization tasks simultaneously by leveraging intertask knowledge transfer. However, as the number of tasks increases to the extent of many-task optimization, the knowledge transfer between tasks encounters more uncertainty and challenges, thereby resulting in degradation of optimization performance. To give full play to the many-task optimization framework and minimize the potential negative transfer, this article proposes an evolutionary many-task optimization algorithm based on a multisource knowledge transfer mechanism, namely, EMaTO-MKT. Particularly, in each iteration, EMaTO-MKT determines the probability of using knowledge transfer adaptively according to the evolution experience, and balances the self-evolution within each task and the knowledge transfer among tasks. To perform knowledge transfer, EMaTO-MKT selects multiple highly similar tasks in terms of maximum mean discrepancy as the learning sources for each task. Afterward, a knowledge transfer strategy based on local distribution estimation is applied to enable the learning from multiple sources. Compared with the other state-of-the-art evolutionary many-task algorithms on benchmark test suites, EMaTO-MKT shows competitiveness in solving many-task optimization problems.
      PubDate: April 2022
      Issue No: Vol. 26, No. 2 (2022)
       
  • Multifactorial Evolutionary Algorithm Based on Improved Dynamical
           Decomposition for Many-Objective Optimization Problems

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      Authors: Jun Yi;Wei Zhang;Junren Bai;Wei Zhou;Lizhong Yao;
      Pages: 334 - 348
      Abstract: In multiobjective optimization, it is generally known that the boom in computational complexity and search spaces came with a rise in the number of objectives, and this leads to a decrease in selection pressure and the deterioration of the evolutionary process. It follows then that the many-objective optimization problem (MaOP) has become one of the most challenging topics in the field of intelligent optimization. Recently, the multifactorial evolutionary algorithm (MFEA) and its variations, which have shown excellent performance in knowledge transfer across related problems, may provide a new and effective way for solving MaOPs. In this article, a novel MFEA based on improved dynamical decomposition (MFEA/IDD), which integrates the advantages of multitasking optimization and decomposition-based evolutionary algorithms, is proposed. Specifically, in the improved dynamical decomposition strategy (IDD) method, the bi-pivot strategy is designed to provide a good mechanism for balancing between convergence and diversity instead of the single-pivot strategy. Furthermore, a novel MFEA-based approach embedding the IDD strategy is developed to reduce the total running time for solving multiple MaOPs simultaneously. Compared with seven state-of-the-art algorithms, the efficacy of our proposed method is validated experimentally on the benchmarks WFG, DTLZ, and MAF with three to ten objectives, along with a series of real-world cases. The results reveal that the MFEA/IDD is well placed in balancing convergence and diversity while reducing the total number of function evaluations for solving MaOPs.
      PubDate: April 2022
      Issue No: Vol. 26, No. 2 (2022)
       
  • Hypervolume-Optimal μ-Distributions on Line/Plane-Based Pareto Fronts in
           Three Dimensions

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      Authors: Ke Shang;Hisao Ishibuchi;Weiyu Chen;Yang Nan;Weiduo Liao;
      Pages: 349 - 363
      Abstract: Hypervolume is widely used in the evolutionary multiobjective optimization (EMO) field to evaluate the quality of a solution set. For a solution set with $mu $ solutions on a Pareto front, a larger hypervolume means a better solution set. Investigating the distribution of the solution set with the largest hypervolume is an important topic in EMO, which is the so-called hypervolume-optimal $mu $ -distribution. Theoretical results have shown that the $mu $ solutions are uniformly distributed on a linear Pareto front in two dimensions. However, the $mu $ solutions are not always uniformly distributed on a single-line Pareto front in three dimensions. They are only uniform when the single-line Pareto front has one constant objective. In this article, we further investigate the hypervolume-optimal $mu $ -distribution in three dimensions. We consider the line-based and plane-based Pareto fronts. For the line-based Pareto fronts, we extend the single-line Pareto front to two-line and three-line Pareto fronts, where each line has one constant objective. For the plane-based Pareto fronts, the linear triangular and inverted triangular Pareto fronts are considered. First, we show that the $mu $ solutions are not always uniformly distributed on the line-based Pareto fronts. The uniformity depends on how the lines are combined. Then, we show that a uniform solution set on the plane-based Pareto front is not always optimal for hypervolume maximization. It is locally optimal with respect to a $(mu +1)$- selection scheme. Our results can help researchers in the community to better understand and utilize the hypervolume indicator.
      PubDate: April 2022
      Issue No: Vol. 26, No. 2 (2022)
       
  • Real-Time Federated Evolutionary Neural Architecture Search

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      Authors: Hangyu Zhu;Yaochu Jin;
      Pages: 364 - 378
      Abstract: Federated learning is a distributed machine learning approach to privacy preservation and two major technical challenges prevent a wider application of federated learning. One is that federated learning raises high demands on communication resources, since a large number of model parameters must be transmitted between the server and clients. The other challenge is that training large machine learning models such as deep neural networks in federated learning requires a large amount of computational resources, which may be unrealistic for edge devices such as mobile phones. The problem becomes worse when deep neural architecture search (NAS) is to be carried out in federated learning. To address the above challenges, we propose an evolutionary approach to real-time federated NAS that not only optimizes the model performance but also reduces the local payload. During the search, a double-sampling technique is introduced, in which for each individual, only a randomly sampled submodel is transmitted to a number of randomly sampled clients for training. This way, we effectively reduce computational and communication costs required for evolutionary optimization, making the proposed framework well suitable for real-time federated NAS.
      PubDate: April 2022
      Issue No: Vol. 26, No. 2 (2022)
       
  • Indicator-Based Evolutionary Algorithm for Solving Constrained
           Multiobjective Optimization Problems

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      Authors: Jiawei Yuan;Hai-Lin Liu;Yew-Soon Ong;Zhaoshui He;
      Pages: 379 - 391
      Abstract: To prevent the population from getting stuck in local areas and then missing the constrained Pareto front fragments in dealing with constrained multiobjective optimization problems (CMOPs), it is important to guide the population to evenly explore the promising areas that are not dominated by all examined feasible solutions. To this end, we first introduce a cost value-based distance into the objective space, and then use this distance and the constraints to define an indicator to evaluate the contribution of each individual to exploring the promising areas. Theoretical studies show that the proposed indicator can effectively guide population to focus on exploring the promising areas without crowding in local areas. Accordingly, we propose a new constraint handling technique (CHT) based on this indicator. To further improve the diversity of population in the promising areas, the proposed indicator-based CHT divides the promising areas into multiple subregions, and then gives priority to removing the individuals with the worst fitness values in the densest subregions. We embed the indicator-based CHT in evolutionary algorithm and propose an indicator-based constrained multiobjective algorithm for solving CMOPs. Numerical experiments on several benchmark suites show the effectiveness of the proposed algorithm. Compared with six state-of-the-art constrained evolutionary multiobjective optimization algorithms, the proposed algorithm performs better in dealing with different types of CMOPs, especially in those problems that the individuals are easy to appear in the local infeasible areas that dominate the constrained Pareto front fragments.
      PubDate: April 2022
      Issue No: Vol. 26, No. 2 (2022)
       
  • A Multifidelity Approach for Bilevel Optimization With Limited Computing
           Budget

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      Authors: Mohammad Mohiuddin Mamun;Hemant Kumar Singh;Tapabrata Ray;
      Pages: 392 - 399
      Abstract: Bilevel optimization refers to a specialized class of problems where the optimum of an upper level (UL) problem is sought subject to the optimality of a nested lower level (LL) problem as a constraint. This nested structure necessitates a large number of function evaluations for the solution methods, especially population-based metaheuristics such as evolutionary algorithms (EAs). Reducing this effort remains critical for practical uptake of bilevel EAs, particularly for computationally expensive problems where each solution evaluation may involve a significant cost. This letter aims to contribute toward this field by a novel and previously unexplored proposition that bilevel optimization problems can be posed as multifidelity optimization problems. The underpinning idea is that an informed judgment of how accurate the LL optimum estimate should be to confidently determine its ranking can significantly cut down redundant evaluations during the search. Toward this end, we propose an algorithm which learns the appropriate fidelity to evaluate a solution during the search based on the seen data, instead of resorting to an exhaustive LL optimization. Numerical experiments are conducted on a range of standard as well as more complex variants of the SMD test problems to demonstrate the advantages of the proposed approach when compared to state-of-the-art surrogate-assisted algorithms.
      PubDate: April 2022
      Issue No: Vol. 26, No. 2 (2022)
       
  • TechRxiv: Share Your Preprint Research with the World!

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      Pages: 400 - 400
      Abstract: Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.
      PubDate: April 2022
      Issue No: Vol. 26, No. 2 (2022)
       
 
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