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Performance analysis of pixelated antennas employing shifted cross-shaped elements

IET MICROWAVES ANTENNAS & PROPAGATION 2023年第9期17卷 710-722页

作者： Mair, Dominik Renzler, Michael Unterladstaetter, Markus Ussmueller, Thomas Univ Innsbruck Dept Mechat Innsbruck Tyrol Austria Fohrenwald 31a A-6105 Leutasch Tyrol Austria

This article presents a detailed analysis of pixelated antennas employing shifted cross-shaped elements optimised by evolutionary algorithms. Although, the size and number of the involved pixels, as well as the overall antenna size strongly influence performance of such antennas, detailed investigations on the interplay of geometrical considerations and their influence on parameters such as gain are missing from literature. Thus, the influence of various geometrical parameters on the performance of pixelated antennas for the first time, resulting in valuable design considerations is presented in this study. A maximum cross size could be found, for which an optimisation is viable. Based on this, a dataset consisting of 400 pixelated antennas was created, for different pixel- and antenna sizes and investigated certain parameters such as bandwidth and antenna gain. Most notably, a directly proportional dependence from the pixel-on the antenna size for the optimisation of electrically large antennas was found. For small antennas, this dependency begins to deviate, leading to the fact that smaller pixels are needed to successfully optimise an antenna. Furthermore, the authors find that multi-objective optimisation is a necessity to optimise matched antennas with a defined bandwidth and gain. The antenna gain shows a dependency on the cross size for electrically small antennas. From these results, design considerations for pixelated antennas can be derived. In addition to the extensive simulations, the dataset was verified in laboratory measurements.

关键词： antennas evolutionary computation optimisation

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Meta-Heuristic Guided Feature Optimization for Enhanced Authorship Attribution in Java Source Code

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IEEE ACCESS 2023年 11卷 141657-141673页

作者： Al-Ahmad, Bilal Al-Madi, Nailah Alzaqebah, Abdullah Alkhawaldeh, Rami S. Aldebei, Khaled Kabir, Md. Faisal Altaharwa, Ismail Abu-Faraj, Mua'ad Aljarah, Ibrahim Univ Jordan Aqaba 77110 Jordan St Cloud State Univ Dept Comp Sci & Informat Technol St Cloud MN 56301 USA Princess Sumaya Univ Technol Amman 11941 Jordan Al Ahliyya Amman Univ Fac Informat Technol Comp Sci Dept Amman 19628 Jordan Penn State Univ Harrisburg Sch Sci Engn & Technol Middletown PA 17057 USA Univ Jordan Amman 11942 Jordan

Source code authorship attribution is the task of identifying who develops the code based on learning based on the programmer style. It is one of the critical activities which used extensively in different aspects such as computer security, computer law, and plagiarism. This paper attempts to investigate source code authorship attribution by capturing natural language aspects of the code rather than only using minimal set of syntactic and stylistic code features as explored in the previous literature. It proposes an evolutionary feature selection model to improve the accuracy of authorship attribution by implementing two language models (uni-gram and bi-gram). The proposed approach uses K-Nearest Neighbor as a classifier and Genetic Algorithm as a feature selection technique. Two experiments have been demonstrated on a public Authorship Attribution dataset on GitHub, the experiments include various evolutionary feature selection models. Notably, the obtained results in both experiments were compared with the related studies, and show a significant improvement in terms of accuracy.

关键词： Source coding Feature extraction Genetic algorithms Codes Task analysis Java Classification algorithms evolutionary computation data mining feature selection java source code authorship attribution

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An Interactive Co-evolutionary Framework for Multi-Objective Critical Node Detection on Large-Scale Complex Networks

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2023年第3期10卷 1722-1735页

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

Multi-objective evolutionary algorithms (MOEAs) have been proved to be competitive for solving the critical node detection problem (CNDP) on complex networks in both non-cascading scenario and cascading scenario. With the continuous expansion of network scale, the search space of the existing MOEAs will increase exponentially. To this end, we propose an interactive co-evolutionary framework (ICoEF) to solve the multi-objective critical node detection on large-scale complex networks, where a set of local populations as well as a global population are interactively evolved to detect the critical nodes with high qualities. Specifically, we first use a community detection algorithm to divide the original network into multiple local communities, where each local community generates a local population for quickly finding local solutions independently. Then, the latent good solutions can be easily obtained by combining the returned local solutions. After that, one global population generated by the original network can refine these latent good solutions by using the proposed two-stage replacement search strategy. Through the above local and global evolutionary interaction, a set of high-quality solutions can be finally obtained. Experiments on synthetic and real-world networks demonstrate the superiority of the proposed framework over several state-of-the-art baselines in both non-cascading and cascading scenarios.

关键词： Optimization Complex networks Statistics Sociology Costs Integer linear programming evolutionary computation Cooperative co-evolutionary critical node detection large-scale complex network multi-objective optimization

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A Many-Objective Optimization Based Federal Deep Generation Model for Enhancing Data Processing Capability in IoT

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2023年第1期19卷 561-569页

作者： Cai, Xingjuan Lan, Yang Zhang, Zhixia Wen, Jie Cui, Zhihua Zhang, Wensheng Taiyuan Univ Sci & Technol Sch Comp Sci & Technol Taiyuan 030024 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Intelligent Control & Management Co Beijing 100190 Peoples R China

The rapid progress of artificial intelligence expands its wide applicability in Internet of Things (IoT). Meanwhile, data insufficient and data source privacy are key supply chain challenges facing IoT especially in the healthcare industry. To address this problem in healthcare IoT, in this article, we propose a skin cancer detection model based on federated learning integrated with deep generation model. First, we employ dual generative adversarial networks to address the problem of insufficient data. In addition, to improve the quality of generated images, we synchronously optimize the sharpness of images, Frechet inception distance, image diversity, and loss using knee point-driven evolutionary algorithm (KnEA). Then, we protect patient information privacy by training federated skin cancer framework. Finally, we employ the ISIC 2018 dataset to test the performance of the proposed training model under different situations, including using identically distributed data, nonidentically distributed data, a sparse convolutional neural network, and a fully connected convolutional neural network. The experiment results demonstrate that the accuracy and area under the curve reach 91% and 88%, respectively. This model can help resolve problems of insufficient data in smart medicine of IoT and protect the privacy of user data while also providing an excellent detection rate.

关键词： Skin cancer Data models Servers Generators Privacy Generative adversarial networks evolutionary computation Deep generative models federated learning (FL) Internet of Thing (IoT) knee point-driven evolutionary algorithm (KnEA) skin cancer

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A Multi-Factorial evolutionary Algorithm With Asynchronous Optimization Processes for Solving the Robust Influence Maximization Problem

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IEEE computationAL INTELLIGENCE MAGAZINE 2023年第3期18卷 41-53页

作者： Wang, Shuai Ding, Beichen Jin, Yaochu Sun Yat Sen Univ Guangzhou Peoples R China Bielefeld Univ Bielefeld Germany

The complex network has attracted increasing attention and shown effectiveness in modeling multifarious systems. Focusing on selecting members with good spreading ability, the influence maximization problem is of great significance in network-based information diffusion tasks. Plenty of attention has been paid to simulating the diffusion process and choosing influential seeds. However, errors and attacks typically threaten the normal function of networked systems, and few studies have considered the influence maximization problem under structural failures. Therefore, a quantitative measure with a changeable parameter is first developed in this paper to tackle the unpredictable destruction percentage on networks. Further, limitations on the existing methods are shown experimentally. To address these limitations, the evolutionary multitasking paradigm is employed, and several problem-specific operators are developed. On top of these developments, a multi-factorial evolutionary algorithm is devised to find seeds with robust influence ability, termed MFEARIM, where the genetic information for both myopia and holistic areas is considered to improve the search ability. Additionally, an asynchronous strategy is designed to efficiently tackle tasks with distinct costs, and the convergence of the search process can thus be accelerated. Experiments on several synthetic and real-world networks validate the competitive performance of MFEARIM over the existing methods.

关键词： Costs Focusing Diffusion processes evolutionary computation Complex networks Multitasking Genetics

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Controlling Sequential Hybrid evolutionary Algorithm by Q-Learning

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IEEE computationAL INTELLIGENCE MAGAZINE 2023年第1期18卷 84-103页

作者： Zhang, Haotian Sun, Jianyong Back, Thomas Zhang, Qingfu Xu, Zongben Xi An Jiao Tong Univ Xian Peoples R China Leiden Univ Leiden Netherlands City Univ Hong Kong Hong Kong Peoples R China

Many state-of-the-art evolutionary algoritahms (EAs) can be categorized as sequential hybrid EAs, in which various EAs are sequentially executed. The timing to switch from one EA to another is critical to the performance of the hybrid EA because the switching time determines the allocation of computational resources and thereby it helps balance exploration and exploitation. In this article, a framework for adaptive parameter control for hybrid EAs is proposed, in which the switching time is controlled by a learned agent rather than a manually designed scheme. First the framework is applied to an adaptive differential evolution algorithm, LSHADE, to control when to use the scheme to reduce the population. Then the framework is applied to the algorithm that won the CEC 2018 competition, i.e., the hybrid sampling evolution strategy (HSES), to control when to switch from the univariate sampling phase to the Covariance Matrix Adaptation Evolution Straategy phase. The agents for parameter control in LSHADE and HSES are trained by using Q-learning and deep Q-learning to obtain the learned algorithms Q-LSHADE and DQ-HSES. The results of experiments on the CEC 2014 and 2018 test suites show that the learned algorithms significantly outperform their counterparts and some state-of-the-art EAs.

关键词： Q-learning Sociology Switches evolutionary computation Control systems Timing Resource management

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Multi-Objective Neural evolutionary Algorithm for Combinatorial Optimization Problems

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023年第4期34卷 2133-2143页

作者： Shao, Yinan Lin, Jerry Chun-Wei Srivastava, Gautam Guo, Dongdong Zhang, Hongchun Yi, Hu Jolfaei, Alireza Alibaba Inc Hangzhou 310052 Peoples R China Western Norway Univ Appl Sci N-5063 Bergen Norway Brandon Univ Dept Math & Comp Sci Brandon MB R7A 6A9 Canada China Med Univ Res Ctr Interneural Comp Taichung 40402 Taiwan Asia Univ Coll Informat & Elect Engn Taichung 413 Taiwan Macquarie Univ Dept Comp Sydney NSW 2109 Australia

There has been a recent surge of success in optimizing deep reinforcement learning (DRL) models with neural evolutionary algorithms. This type of method is inspired by biological evolution and uses different genetic operations to evolve neural networks. Previous neural evolutionary algorithms mainly focused on single-objective optimization problems (SOPs). In this article, we present an end-to-end multi-objective neural evolutionary algorithm based on decomposition and dominance (MONEADD) for combinatorial optimization problems. The proposed MONEADD is an end-to-end algorithm that utilizes genetic operations and rewards signals to evolve neural networks for different combinatorial optimization problems without further engineering. To accelerate convergence, a set of nondominated neural networks is maintained based on the notion of dominance and decomposition in each generation. In inference time, the trained model can be directly utilized to solve similar problems efficiently, while the conventional heuristic methods need to learn from scratch for every given test problem. To further enhance the model performance in inference time, three multi-objective search strategies are introduced in this work. Our experimental results clearly show that the proposed MONEADD has a competitive and robust performance on a bi-objective of the classic travel salesman problem (TSP), as well as Knapsack problem up to 200 instances. We also empirically show that the designed MONEADD has good scalability when distributed on multiple graphics processing units (GPUs).

关键词： Optimization evolutionary computation Heuristic algorithms Search problems Neural networks Genetics Urban areas Attention mechanism deep reinforcement learning (DRL) multi-objective learning neural combinatorial optimization neural evolutionary algorithm

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A penalty-based algorithm proposal for engineering optimization problems

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NEURAL COMPUTING & APPLICATIONS 2023年第10期35卷 7635-7658页

作者： Oztas, Gulin Zeynep Erdem, Sabri Pamukkale Univ Dept Business Adm TR-20160 Denizli Turkey Dokuz Eylul Univ Dept Business TR-35390 Izmir Turkey

This paper presents a population-based evolutionary computation model for solving continuous constrained nonlinear optimization problems. The primary goal is achieving better solutions in a specific problem type, regardless of metaphors and similarities. The proposed algorithm assumes that candidate solutions interact with each other to have better fitness values. The interaction between candidate solutions is limited with the closest neighbors by considering the Euclidean distance. Furthermore, Tabu Search Algorithm and Elitism selection approach inspire the memory usage of the proposed algorithm. Besides, this algorithm is structured on the principle of the multiplicative penalty approach that considers satisfaction rates, the total deviations of constraints, and the objective function value to handle continuous constrained problems very well. The performance of the algorithm is evaluated with real-world engineering design optimization benchmark problems that belong to the most used cases by evolutionary optimization researchers. Experimental results show that the proposed algorithm produces satisfactory results compared to the other algorithms published in the literature. The primary purpose of this study is to provide an algorithm that reaches the best-known solution values rather than duplicating existing algorithms through a new metaphor. We constructed the proposed algorithm with the best combination of features to achieve better solutions. Different from similar algorithms, constrained engineering problems are handled in this study. Thus, it aims to prove that the proposed algorithm gives better results than similar algorithms and other algorithms developed in the literature.

关键词： Natural facts evolutionary computation Constrained nonlinear optimization Engineering benchmark problems Metaheuristics Nature-inspired optimization algorithms

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A Macro-Micro Population-Based Co-evolutionary Multi-Objective Algorithm for Community Detection in Complex Networks

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IEEE computationAL INTELLIGENCE MAGAZINE 2023年第3期18卷 69-86页

作者： Zhang, Lei Yang, Haipeng Yang, Shangshang Zhang, Xingyi Anhui Univ Hefei Peoples R China

Recently, multi-objective evolutionary algorithms (MOEAs) have shown promising performance in terms of community detection in complex networks. However, most studies have focused on designing different strategies to achieve good community detection performance based on a single population. Unlike these studies, this study proposes a macro-micro population-based co-evolutionary multi-objective algorithm called MMCoMO for community detection in complex networks to obtain a better trade-off between exploration and exploitation. This algorithm employs two populations, i.e., macro-population and micro-population, for co-evolution to obtain better community structures. In particular, the macro-population prefers exploration and is responsible for quickly determining approximate partitions of the network to obtain good rough community structures as early as possible, whereas the micro-population favors exploitation and is responsible for searching for good fine community structures through the local search process. Thus, these two populations can be used to improve each other through interactions in the co-evolutionary process. In particular, a guiding strategy is designed using the elite (non-dominated) solutions of the macro-population to guide the micro-population, and an influencing strategy is further designed using the elite solutions of the micro-population to positively influence the macro-population. Experiments on synthetic networks and 14 real-world networks demonstrate the superiority of the proposed algorithm over several state-of-the-art community detection algorithms.

关键词： Sociology Complex networks evolutionary computation Approximation algorithms Partitioning algorithms Statistics Detection algorithms

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An ensemble knowledge transfer framework for evolutionary multi-task optimization

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SWARM AND evolutionary computation 2023年 83卷

作者： Zhou, Jiajun Rao, Shijie Gao, Liang China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China

The cognitive capability of learning from past experiences to resolve relevant tasks at hand is a hallmark of humans. The emerging evolutionary multi-task transfer optimization (EMTO) fashion pursues such intelligent behavior by exploring useful knowledge drawn from solving one task to accelerate the optimization process of other related tasks. However, in practical scenario, knowledge drawn from various task domains may not always benefit the solving process of one another, and source-target domain mismatch is likely to induce notorious negative transfer, which is a critical concern in EMTO. Domain adaption aims to narrow the gap between distinct domains so as to curb negative transfer. Generally, different strategies possess distinct advantages in different situations, no one can dominate others in all cases. Taking this cue, we present a novel bandit-mechanism-based ensemble method to determine the proper domain adaption strategy online in the context of EMTO. Besides, the intensity of cross-task knowledge transfer is adapted according to historical experiences of the population. We carry out extensive experiments to examine the performance of proposed approach, demonstrating its superiority in comparison to state-of-the-art peers in multi-task problem-solving scenario. Our work thus sheds light on a new alternative way for automatic domain adaption for knowledge transfer across problems.

关键词： evolutionary computation Multi-task transfer optimization Domain adaptation Adaptive knowledge transfer Ensemble learning

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