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EIS-OBEA: Enhanced Image Steganalysis via Opposition-Based evolutionary algorithm

IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2025年 20卷 3616-3631页

作者： Ma, Yuanyuan Xu, Lige Zhang, Qianqian Zhang, Yi Xin, Xianwei Luo, Xiangyang Henan Normal Univ Coll Comp & Informat Engn Xinxiang 453007 Peoples R China Informat Engn Univ Henan Prov Key Lab Cyberspace Situat Awareness Zhengzhou 450001 Peoples R China

Recent years have witnessed a spurt progress in steganography, which poses challenges for steganalysis. However, previous steganalysis methods attach equal attention to various feature information, while key feature information in detection is ubiquitously ignored, and the detection time-space cost is burdened consequently. To alleviate this predicament, this paper proposes an enhanced image steganalysis via opposition-based evolutionary algorithm (EIS-OBEA), which can guide steganalysis showing more solicitude for key feature information and reduce detection time overhead. Specifically, evolutionary algorithm is introduced into enhanced steganalysis. To elevate searching ability for steganalysis key feature submodels, Tent map is applied in enhanced steganalysis population initialization because of its great randomness. Secondly, considering that opposition-based learning can dynamically adjust searching space of enhanced steganalysis population, opposition-based learning via lens imaging strategy is proposed to help enhanced steganalysis escape from local optimal solutions. Then, to reasonably evaluate the detection contribution of steganalysis key feature submodels, the pearson correlation coefficient for steganalysis is designed. On this basis, fitness function is devised to select superior individuals and obtain steganalysis key feature submodels after iteration. It is noted that EIS-OBEA can optimize steganalysis training samples into quite small-size data, so that computational cost can be significantly reduced when maintaining or even improving detection accuracy. Extensive experimental results substantiate that compared with the state-of-the-art peer algorithms, EIS-OBEA not only achieves highly competitive or even better detection performance, but also meliorates steganalysis time-space cost to a large extent.

关键词： Feature extraction Optimization evolutionary computation Training Costs Steganography Accuracy Lenses Deep learning Data mining Enhanced steganalysis evolutionary algorithm opposition-based learning coefficient for steganalysis

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Many-objective evolutionary algorithm based on dynamic mating and strengthened fitness selection mechanism

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JOURNAL OF SUPERCOMPUTING 2025年第2期81卷 1-77页

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

Many-objective evolutionary algorithms have demonstrated their superiority in dealing with many-objective optimization problems. However, their performance in handling many objective optimization problems can be significantly affected due to the sensitivity on the curvature of Pareto front. This paper proposes the dynamic mating selection strategy and strengthened fitness selection mechanism to solve many objective optimization problems (MaOEA-DMSF). In MaOEA-DMSF, a dynamic mating selection strategy is proposed to select appropriate mating population, which can generate high-quality offspring with a higher probability. In addition, a strengthened fitness selection mechanism is proposed to improve convergence without deterioration in population diversity. To verify the effectiveness of the proposed MaOEA-DMSF, a series of experiments are carried out against eight state-of-the-art many-objective optimization algorithms on three widely used benchmark test suites. Experimental results demonstrate that the proposed MaOEA-DMSF has higher competitiveness compared with peer competitors.

关键词： Many-objective optimization hyperplane mapping distance strengthened fitness selection mechanism evolutionary algorithm

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An indicator-based multi-objective evolutionary algorithm assisted by improved graph convolutional networks

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SWARM AND evolutionary COMPUTATION 2025年 94卷

作者： Yan, Pengguo Tian, Ye Liu, Yu Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan 114051 Peoples R China Anhui Univ Sch Comp Sci & Technol Hefei 230601 Peoples R China

Recently, graph convolutional networks (GCN) have attracted significant attention due to their superior performance in handling non-Euclidean spaces, which enables GCN to model and analyze complex data structures that cannot be handled by traditional methods. Neural network-based multi-objective evolutionary algorithms (NNMOEAs) have made significant strides, predominantly focusing on mapping the decision space to the objective space, but may fail to focus on the interconnectedness of solutions within the decision space. To address this problem, this paper proposes a two-stage multi-objective optimization algorithm that utilizes graph convolutional networks to enhance population evolution. In the initial stage, the algorithm employs cosine similarity to represent the population as graph-structured data. A hypervolume-guided self-attention update mechanism is then introduced to balance exploration and exploitation, achieved by establishing an exploratory neighborhood population alongside an expanded neighborhood population. In the subsequent stage, a key node detection strategy is implemented, which considers both the global influence and local mediation roles of nodes. This strategy selects individuals with highly concentrated information to generate new solutions, thereby facilitating a thorough exploration of the solution space. The proposed algorithm is evaluated against five state-of-the-art MOEAs across five benchmark test suites and five real-world problems. The experimental results demonstrate its superior performance in addressing robust, variable linkages and imbalance mapping multi-objective optimization problems, as well as its feasibility in practical problems.

关键词： Graph convolutional networks evolutionary algorithm Self-attention mechanism Key node detection strategy

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Fair Influence Maximization in Social Networks: A Community-Based evolutionary algorithm

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING 2025年第1期13卷 262-275页

作者： Ma, Kaicong Xu, Xinxiang Yang, Haipeng Cao, Renzhi Zhang, Lei Anhui Univ Sch Comp Sci & Technol Informat Mat & Intelligent Sensing Lab Anhui Prov Hefei 230039 Peoples R China Pacific Lutheran Univ Sch Comp Sci Tacoma WA 98447 USA

Influence maximization (IM) has been extensively studied in network science, which attempts to find a subset of users to maximize the influence spread. A new variant of IM, fair IM (FIM), which primarily enhances the fair propagation of information, has attracted increasing attention in academia. However, existing algorithms for FIM suffer from a trade-off between fairness and running time, as it is difficult to ensure that users are fairly influenced in terms of sensitive attributes, such as race or gender, while maintaining a high influence spread. To tackle this problem, herein, we propose an effective and efficient community-based evolutionary algorithm for FIM (named CEA-FIM). In CEA-FIM, a community-based node selection strategy is proposed to identify potential nodes, which not only considers the size of the community but also the attributes of the nodes in the community. Subsequently, we designed an evolutionary algorithm based on the proposed node selection strategy to hasten the solution search, including the novel initialization, crossover, and mutation strategies. We validated the proposed algorithm by performing experiments on real-world and synthetic networks. The experimental results show that the proposed CEA-FIM achieves a better balance between effectiveness and efficiency than state-of-the-art methods do.

关键词： Influence maximization group fairness community structure evolutionary algorithm social networks Influence maximization group fairness community structure evolutionary algorithm social networks

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A soft encoding-based evolutionary algorithm for the steelmaking scheduling problem and its extension under energy thresholds

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COMPUTERS & OPERATIONS RESEARCH 2025年 174卷

作者： Jiang, Sheng-Long Chongqing Univ Coll Mat Sci & Engn Chongqing 400044 Peoples R China

Steelmaking and continuous casting scheduling problem (SCCSP) is a classic optimization problem increasingly incorporating more constraints, such as energy-related ones. However, classic evolutionary algorithms with "rigid" encoding schemes face challenges in finding optimal solutions for heavily constrained SCCSPs. Motivated by this gap, this paper first extends the mathematical model of the classic SCCSP to its variant under energy thresholds (ET-SCCSP) from both single- and multi-objective optimization perspectives, and derives several problem-specific properties. Next, this paper develops a solving algorithm named the soft encoding-based evolutionary algorithm (SoEA), which uses a real-valued vector to encode a feasible solution for SCCSPs. Furthermore, SoEA introduces the following components: (1) a peak-cutting backward list scheduling (PC-BLS) procedure to decode a real-valued vector into a feasible solution, and (2) a local search procedure to enhance the algorithm's performance. Comparative results in the computational experiment demonstrate that the SoEA with the propose encoding/decoding scheme: (1) achieves better performance than exact solver for small-scale instances under energy thresholds, (2) obtains promising results for medium-scale instances compared to other schemes, and (3) can be intensified by the tailored local search procedure. The proposed SoEA can also serve as a benchmark or tutorial for the development and evaluation of high-efficiency algorithms for other SCCSPs with heavy constraints. The source code is available on the GitHub repository: https://***/janason/Soft-Sch eduling/tree/master/SoEA.

关键词： Energy threshold Steelmaking and continuous casting Solution representation evolutionary algorithm Hybrid flow shop

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A Deep Reinforcement Learning-Based evolutionary algorithm for Distributed Heterogeneous Green Hybrid Flowshop Scheduling

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PROCESSES 2025年第3期13卷 728-728页

作者： Xu, Hua Huang, Lingxiang Tao, Juntai Zhang, Chenjie Zheng, Jianlu Jiangnan Univ Sch Artificial Intelligence & Comp Sci Li Hu Ave Wuxi 214122 Peoples R China

Due to increasing energy consumption, green scheduling in the manufacturing industry has attracted great attention. In distributed manufacturing involving heterogeneous plants, accounting for complex work sequences and energy consumption poses a major challenge. To address distributed heterogeneous green hybrid flowshop scheduling (DHGHFSP) while optimising total weighted delay (TWD) and total energy consumption (TEC), a deep reinforcement learning-based evolutionary algorithm (DRLBEA) is proposed in this article. In the DRLBEA, a problem-based hybrid heuristic initialization with random-sized population is designed to generate a desirable initial solution. A bi-population evolutionary algorithm with global search and local search is used to obtain the elite archive. Moreover, a distributional Deep Q-Network (DQN) is trained to select the best local search strategy. Experimental results on 20 instances show a 9.8% increase in HV mean value and a 35.6% increase in IGD mean value over the state-of-the-art method. The results show the effectiveness and efficiency of the DRLBEA in solving DHGHFSP.

关键词： distributed heterogeneous green hybrid flowshop scheduling deep reinforcement learning evolutionary algorithm distributional deep Q-network

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A novel interval sparse evolutionary algorithm for efficient spectral variable selection

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ANALYTICA CHIMICA ACTA 2025年 1341卷 343655页

作者： Li, Mingrui Li, Yonggang Yang, Chunhua Zhu, Hongqiu Zhou, Can Cent South Univ Sch Automat Changsha 410083 Peoples R China

Background: In spectral analysis, selecting the right spectral variables is crucial for effective modeling. It reduces data dimensionality, removes irrelevant wavelength points, and improves both the generalization ability and computational efficiency of the model. However, the number of available samples often falls short of the total possible combinations of wavelengths, making variable selection a non-deterministic polynomial-time (NP) hard optimization problem. The current dedicated variable selection and heuristic algorithms fail to balance the effectiveness and speed of variable selection. Therefore, there is a great need for a more advanced approach to address this problem. (92) Results: In this paper, we adopt a different perspective by considering variable selection as a large-scale sparse multi-objective optimization problem, modeled with fewer variables to achieve lower prediction errors. Then a novel interval sparse evolutionary algorithm (ISEA) was proposed, merging the benefits of dedicated variable selection algorithms and evolutionary algorithms. It incorporates a roulette probability mechanism and enhances the selection probability of key informative variables through a sparse population initialization strategy (SPIS) and a regional sparse evolution strategy (RSES). Specifically, the SPIS prioritizes variable regions through interval partial least squares (iPLS) and initializes the sparse population based on regional roulette probability, thereby enhancing the likelihood of selection of important regional variables in the initial sparse population. The RSES further focuses on more important regions, ensuring the variables in more important regions have a higher survival probability in subsequent generations. (138) Significance: Applied to datasets of corn oil, soil, and diesel fuels, ISEA outperforms nine state-of-the-art methods by maintaining both the effectiveness of variable selection and running speed. Additionally, unlike dedicated variable se

关键词： Spectral analysis Variable selection Multi-objective optimization evolutionary algorithm Roulette probability

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BSPKTM-SIFE-WST: bispectrum based channel selection using set-based-integer-coded fuzzy granular evolutionary algorithm and wavelet scattering transform for motor imagery EEG classification

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SIGNAL IMAGE AND VIDEO PROCESSING 2025年第4期19卷 1-16页

作者： Kardam, Vikram Singh Taran, Sachin Pandey, Anukul Delhi Technol Univ DTU Dept Elect & Commun Delhi India

A brain-computer interface (BCI) is a system that communicates between the brain and an external device. The electroencephalogram (EEG) is the most favourable tool for extracting neural signals from the brain. Motor Imagery (MI) based BCI with EEG signals is an active BCI paradigm. The performance of MI-based BCI is easily affected by noise and redundant information. To decrease noisy and redundant information and increase the spatial resolution of the EEG signals, a multichannel EEG-based BCI system is used. However, high-dimensional data from multichannel BCI systems has a serious impact on the classification performance. Therefore, for better classification performance of EEG-based BCI systems, channel selection methods are used. Generally, many traditional signal processing techniques have been used for feature-based channel selection. However, such type estimation discards the phase relationship among frequency components. To solve this problem, a bispectrum based channel selection technique is used to overcome the drawback of the power spectrum. It effectively provides the frequency domain information of MI-related brain activities. Therefore, in this study, a bispectrum-based channel selection algorithm is proposed for the MI-based BCI system. The most relevant channels from bispectrum analysis are selected from bispectrum analysis using a set-based integer-coded fuzzy granular evolutionary algorithm. The features are extracted from the selected channels using wavelet scattering transform. Finally, the experiments are tested on multiple classifiers and the best performance is obtained using the support vector machine classifier. The other classifiers also attained significant results using a minimum number of EEG channels. The proposed approach, integrating bispectrum-based channel selection, SIFE, and WST, offers a significant improvement in MI-EEG classification accuracy. By reducing irrelevant features and optimizing channel selection, the framework achiev

关键词： Brain computer interface Motor imagery Electroencephalogram evolutionary algorithm Bispectrum Machine learning

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Learning and knowledge-guided evolutionary algorithm for the large-scale buffer allocation problem in production lines

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COMPUTERS & INDUSTRIAL ENGINEERING 2025年 203卷

作者： Gao, Sixiao Zhang, Fan Shi, Shuo BYD Auto Ind Co Ltd Shenzhen 518118 Guangdong Peoples R China Cent South Univ Sch Automat Changsha 410075 Hunan Peoples R China Cent South Univ Business Sch Changsha 410075 Hunan Peoples R China

The large-scale buffer allocation problem (LBAP) in production lines represents a significant optimization challenge, centered on the efficient allocation of limited temporary storage areas. Prior research has predominantly addressed the LBAP through dynamic programming, search algorithms, and metaheuristics. However, these methodologies are often problem-specific and inefficient when applied to large-scale scenarios. Consequently, there is a pressing need to investigate innovative algorithms beyond existing approaches. This paper presents a novel learning and knowledge-guided evolutionary algorithm designed for the LBAP in production lines. The proposed algorithm develops an adaptive genetic algorithm and a variable neighborhood search algorithm, incorporating a simulated annealing-based strategy. An online Q-learning algorithm is employed to dynamically select the more effective of the two preceding algorithms for solution updates, while the simulated annealing-based strategy regulates the acceptance of these updated solutions. Furthermore, The proposed algorithm dynamically adjusts crossover, mutation, and shaking rates to adapt to the neighborhood structure. It also leverages conflict knowledge obtained from prior update experiences to inform the search process, thereby enhancing solution quality and computational efficiency. Numerical results indicate that the proposed algorithm surpasses state-of-the-art methods in addressing the LBAP. Additionally, empirical ablation studies demonstrate that the knowledge-guided approach efficiently explores promising solution regions by eliminating low-value solutions, while the learning-guided approach effectively generates improved solutions by selecting optimal strategies. This proposed algorithm significantly advances dynamic production resource allocation in large-scale systems.

关键词： Buffer allocation Large-scale evolutionary algorithm Learning-guided Knowledge-guided

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Dual-clustering-based Two-population Co-evolutionary algorithm for segmentation coding in flash memory

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2025年 147卷

作者： Luo, Jianjun Chen, Menghao Huang, Boming Liu, Hailuan Fan, Lingyan Gao, Lijuan Feng, Guorui Hangzhou Dianzi Univ Hangzhou 310018 Zhejiang Peoples R China Hangzhou Hualan Microelect CO Ltd Hangzhou 310018 Zhejiang Peoples R China China Elect Technol Grp Corp Res Inst 52 Hangzhou 310018 Zhejiang Peoples R China Shanghai Univ Sch Commun & Informat Engn Shanghai 200444 Peoples R China

To ensure the reliability of stored data, error checking and correction (ECC) module is widely used in flash memory-based storage devices. With the increase of raw bit error rate (RBER), the traditional error correction coding mode is not only hard to satisfy the requirements of storage devices, but also reduces the efficiency of data recovery when error data cannot be located. In this paper, a novel segmentation coding mode is proposed to improve the drawbacks of traditional coding mode. To our knowledge, this is an innovative study of the error correction coding mode in ECC. It can make the coding mode used in ECC to become more flexible and efficient. Since the performance of this coding mode is related to segmentation, we propose a dual-population co-evolutionary algorithm based on clustering algorithm to optimize the performance. The proposed algorithm adopts clustering algorithm to measure the diversity of the population and dynamic weight allocation strategy to regulate evolution indicators of the population. Some experiments are conducted on benchmark problems and the segmentation coding problem, respectively. Experimental results show that the proposed algorithm is superior to other state-of-the-art evolutionary algorithms.

关键词： Flash memory Error checking and correction Bose-Chaudhuri-Hocquenghem code Constrained multiobjective optimization evolutionary algorithm

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