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Preserving privacy in association rule mining using multi-threshold particle swarm optimization

INFORMATION SCIENCES 2025年 692卷

作者： Aljehani, Shahad Alotaibi, Youseef Umm Al Qura Univ Coll Comp Dept Comp Sci & Artificial Intelligence Mecca 21955 Saudi Arabia Umm Al Qura Univ Coll Comp Dept Software Engn Mecca 21955 Saudi Arabia

Healthcare data has become a powerful resource for generating insights that drive medical research. Association Rule Mining (ARM) techniques are widely used to identify relationships among diseases, treatments, and symptoms. However, sensitive information is often exposed, creating significant privacy challenges, particularly when data is integrated from multiple sources. Although Privacy-Preserving Association Rule Mining (PPARM) methods have been developed to address these issues, most rely on a single, predefined Minimum Support Threshold (MST) that is inflexible in adapting to diverse rule patterns. In this study, a Multi-Threshold Particle Swarm Optimization for Association Rule Mining (MPSO4ARM) model is introduced, integrating the Apriori and Particle Swarm Optimization (PSO) algorithms to perform data mining while protecting sensitive rules. A novel approach is employed by the proposed model to dynamically adjust the MST, allowing for more adaptive and effective privacy preservation. The MPSO4ARM model adjusts the MST on-the-fly based on rule length, improving its ability to safeguard sensitive data across various datasets. The proposed model was evaluated on the Chess, Mushroom, Retail, and Heart Disease datasets. The experimental results showed that the MPSO4ARM model outperforms traditional Apriori and conventional PSO algorithms, achieving higher fitness values and reducing side effects such as Hiding Failure (HF) and Missing Cost (MC), particularly in the Heart Disease and Mushroom datasets. Although the dynamic MST function introduces a moderate increase in computational runtime compared to Apriori and conventional PSO, this trade-off between execution time and enhanced privacy protection is considered acceptable, given the model's substantial improvements in data utility and rule sanitization.

关键词： Privacy-preserving Multi-threshold association rule mining Data sanitization evolutionary algorithms Particle swarm optimization Sensitive rule protection

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Global Optimization with Derivative-free, Derivative-based and evolutionary algorithms

Global Optimization with Derivative-free, Derivative-based a...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Bashir, Hassan A. Neville, Richard S. Univ Manchester Sch Comp Sci Manchester M13 9PL Lancs England

ISBN: (纸本)9781479938407

This paper investigates global optimization methods from the perspective of population-based and restarted point-based heuristics. We examine the performance of a standard evolutionary computation (EC) methodology, a derivative-based sequential quadratic programming (SQP) algorithm and a novel derivative-free stochastic coordinate ascent (SCA) algorithm. All methods are analyzed by random sampling of the feasible search space. A comparison was made to evaluate the three algorithms, in the light of newly updated IEEE CEC2013 benchmarks, on a set of multimodal and composite test cases. Results revealed that while the standard EC algorithm is generally more robust, on the basis of convergence efficiency both the restarted SCA and SQP algorithms have shown remarkable performance on some of these benchmarks. The results further suggest that depending on the nature of the problem landscape and dimensionality the three algorithms, chosen from different optimization frameworks, perform complementary to each other.

关键词： evolutionary computation quadratic programming sampling methods search problems stochastic programming EC methodology IEEE CEC2013 benchmarks composite test cases derivative-based SQP algorithm derivative-based algorithms derivative-based sequential quadratic programming algorithm derivative-free SCA algorithm derivative-free algorithms derivative-free stochastic coordinate ascent algorithm evolutionary algorithms evolutionary computation methodology global optimization methods multimodal test cases optimization frameworks population-based heuristics restarted point-based heuristics search space standard EC algorithm Benchmark testing evolutionary computation Genetic algorithms Optimization methods Sociology Standards

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A genetic iterated greedy algorithm for the blocking flowshop to minimize total earliness and tardiness

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JOURNAL OF INTELLIGENT MANUFACTURING 2024年第5期35卷 2161-2174页

作者： Prata, Bruno de Athayde Fuchigami, Helio Yochihiro Univ Fed Ceara Dept Ind Engn Fortaleza CE Brazil Univ Fed Sao Carlos Prod Engn Dept Sao Carlos Brazil

An important and realistic class of scheduling problems is considered in this paper: the total earliness and tardiness minimization in the blocking flowshop, where there is no intermediate buffer between machines. Blocking occurs when a completed item or product remains on the machine until the next machine is available. We proposed a new hybrid evolutionary algorithm: the Genetic Iterated Greedy Algorithm (GIGA). In our innovative solution approach, a genetic algorithm presents a hybrid crossover based on the Iterated Greedy metaheuristic. The hybrid crossover considers the Hamming distance as an indicator of the diversity of the current population. In the first generations, the crossover will adopt larger values for the destruction parameter, and this value is gradually reduced throughout the search process. Our proposal is compared to four competitive metaheuristics reported for earliness and tardiness flowshop. Two performance indicators are considered: the Average Relative Percentage Deviation (ARPD) and the Success Rate (SR). Based on the statistical analysis of the computational experimentation, our GIGA outperformed all the implemented algorithms of the literature with statistical significance. Concerning the performance indicators, GIGA achieved ARPD = 0.02% and SR = 83.5%, pointing to the superiority of the proposed solution approach.

关键词： Production sequencing Just-in-time Metaheuristics evolutionary algorithms

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Suitability Assessment of Cage Fish Farming Location in Reservoirs through Neural Networks-Based Remote Sensing Analysis

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REMOTE SENSING 2024年第2期16卷 236页

作者： Sedighkia, Mahdi Datta, Bithin Australian Natl Univ ICEDS Canberra 2601 Australia Australian Natl Univ MSI Canberra 2601 Australia James Cook Univ Coll Sci Townsville 4811 Australia

The present study evaluates the application of different artificial intelligence methods associated with remote sensing data processing for assessing water quality parameters, with a focus on fish cage farming in the reservoirs. Three AI methods were utilized including 1-optimal artificial neural network (ONN), 2-adaptive neuro fuzzy inference system in which a hybrid algorithm was used for the training process (ANFIS) and 3-coupled evolutionary algorithm-adaptive neuro fuzzy inference system in which particle swarm optimization was utilized in the training process (EA-ANFIS). Three critical water quality parameters for cage fish farming were selected consisting of water temperature, dissolved oxygen (DO) and total dissolved solids (TDS). Moreover, two measurement indices, the Nash-Sutcliffe model efficiency coefficient (NSE) and root mean square error (RMSE), were utilized to assess the predictive skills of the data driven models. Based on the results in the case study, EA-ANFIS is the best method to simulate water temperature and DO in the reservoir by the remote sensing technique. Furthermore, the ANFIS-based model is the best method to simulate TDS. According to the results in the case study, utilizing the spectral images might not be reliable to simulate DO concentration in the reservoirs. However, the images are robust to simulate water temperature as well as TDS concentration.

关键词： remote sensing water quality artificial neural network neuro fuzzy inference system evolutionary algorithms

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A Novel Hybrid Method Using Grey Wolf Algorithm and Genetic Algorithm for IoT Botnet DDoS Attacks Detection

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2025年第1期18卷 1-61页

作者： Maazalahi, Mahdieh Hosseini, Soodeh Shahid Bahonar Univ Kerman Fac Math & Comp Dept Comp Sci Kerman Iran

The Internet of Things (IoT) is a vast network of interconnected physical objects that has improved the conditions for a computer-based physical world and improved efficiency. With the increase in communication in an IoT system, Internet security has decreased, and the most dangerous and sophisticated attacks in the IoT have emerged, i.e., DDoS and Botnet attacks. DDoS attacks are a serious threat to the availability of Internet services, especially since botnets can now be launched by almost anyone. In this situation, the use of an intrusion detection system (IDS) is essential to detect intruders and maintain the security of IoT networks. In this paper, a new IDS is proposed to detect IoT-Botnet DDoS attacks. This IDS is a new three-phase system, the first phase is related to preprocessing on the dataset and the second phase includes a new hybrid method for feature selection using filter and wrapper methods based on the Grey Wolf (GW) algorithm and genetics called GW-GA. In this method, the initial population is randomly selected and then at each stage, feature selection is done by both algorithms simultaneously and the final answer is compared and the best solutions are given as a new population to both algorithms and the third phase includes the use of machine learning and metaheuristic algorithms as classifiers. In the proposed method and to verify the performance, it is evaluated using the large BOT-IoT dataset. The results show that the proposed method significantly reduces the feature and also increases the classification accuracy compared to other methods, and the RF and Bagging algorithms have achieved a maximum recognition accuracy of 0.999. The dimensions of BOT-IoT have been reduced from 46 features to 12.

关键词： evolutionary algorithms Genetics algorithm Grey Wolf algorithm Botnet attack DDOS Meta-heuristic algorithm Machine learning algorithms

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Collaborative gas source localization strategy with networked nano-drones in unknown cluttered environments

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SWARM AND evolutionary COMPUTATION 2024年 89卷

作者： Tran, Vu Phi Garratt, Matthew A. Anavatti, Sreenatha G. Ravi, Sridhar Univ New South Wales Sch Engn & Technol Canberra Australia

This paper introduces a novel approach for improving gas source localization in dynamic urban environments, employing a swarm of nano-Crazyflie drones through a hybrid strategy that integrates Adaptive Robotic Particle Swarm Optimization (ARPSO) with Bidirectional Brain Emotional Learning (BBEL). The proposed method refines the ARPSO algorithm into two phases: an initial exploration phase and a subsequent seeking phase. The exploration phase activates when no gas is detected, seamlessly transitioning to the seeking phase upon gas detection. This facilitates efficient information exchange and desired velocity generation within the particle swarm. During the seeking phase, particles conduct measurements, share the global best position, intelligently navigate obstacles, and avoid collisions while directly identifying the global concentration field maximum. Unlike conventional methods, the ARPSO algorithm autonomously adapts its parameters online, mitigating algorithmic failures and local optima challenges. It also considers the limited communication of robots in complex environments, a factor often overlooked in recent methods. To enhance plume tracking robustness, facilitate the controller gain tuning process, and ensure system resilience against environmental disturbances and uncertainties, the BBEL method incorporates a sophisticated fuzzy neural network structure with reinforcement learning -based adaptation mechanisms. The stability of the closed -loop control system is rigorously proven using Lyapunov theory. Numerical simulations in complex urban scenarios validate the algorithm's effectiveness, showcasing a significant 20% improvement in turnaround time and a flawless 100% success rate with no collisions, in addition to enhanced capabilities in handling uncertainties and disturbances compared to benchmarks like ARPSO-BBEL with unlimited communication range and no delayed communications (ARPSO-BBELU), Sniffy Bug (SB)-BBELU, and ARPSO-PIDU.

关键词： Gas source localization evolutionary algorithms Particle swarm optimization Self-organizing systems Adaptive control Fuzzy neural network Flight control system Quadrotor UAV Robotic swarms

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Modeling the Prices Variation of Agricultural Products on the Stock Market Using evolutionary Approach 1st

Modeling the Prices Variation of Agricultural Products on th...

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21st International Conference on Informatics in Economy (IE) - Education, Research and Business Technologies

作者： Boldea, Costin Radu Boldea, Bogdan Univ Craiova AI Cuza 13 Craiova 200385 Romania West Univ Timisoara Vasile Parvan 4 Timisoara Romania

The paper proposes a new method to simulate the variation of the prices of agricultural products on the stock market, using a co-web evolutionary adaptive model of concurrent sales strategies.

ISBN: (纸本)9789811967573;9789811967559;9789811967542

The paper proposes a new method to simulate the variation of the prices of agricultural products on the stock market, using a co-web evolutionary adaptive model of concurrent sales strategies.

关键词： Co-web models evolutionary algorithms Stock market

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Clustered Orienteering Problem with Function-Based Rewards

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IEEE LATIN AMERICA TRANSACTIONS 2025年第6期23卷 472-478页

作者： Faria, Vinicius L. C. Macharet, Douglas G. Univ Fed Minas Gerais Comp Vis & Robot Lab VeRLab Dept Comp Sci Belo Horizonte Brazil

The Orienteering Problem is a well-studied problem that aims to determine an optimal route that maximizes the cumulative rewards while attaining a predefined travel budget. This classical formulation has been generalized in many forms, including grouping customers (locations) into clusters. However, such cluster-based formulation typically demands visiting all customers to collect the reward, thereby restricting its applicability. In this paper, we introduce a novel variant in which rewards associated with a cluster can be partially collected following a function that correlates with the number of customers attended within the cluster. This new formulation is a suitable alternative to model scenarios where the reward of the clusters can exhibit distinct and unique scaling patterns. We propose using an evolutionary algorithm to solve this problem and evaluate its performance considering different scenarios and aspects. The results demonstrate that our method effectively selects an appropriate number of clients to attend to based on the assigned function for each cluster.

关键词： Routing Urban areas Genetic algorithms Indexes evolutionary computation Gaussian distribution Vectors Probability Codes Buildings Autonomous Agents Orienteering Problem Vehicle Routing evolutionary algorithms

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Hierarchical design of mechanical metamaterials: an application on pentamode-like structures

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INTERNATIONAL JOURNAL OF MECHANICAL SCIENCES 2025年 294卷

作者： Rodriguez, S. E. Calius, E. P. Khatibi, A. Orifici, A. C. Das, R. RMIT Univ Sch Engn Aerosp Engn & Aviat Melbourne Vic 3083 Australia AUT Univ Engn Comp & Math Sci Auckland New Zealand Computed Material Auckland New Zealand

Mechanical metamaterials demonstrate that unprecedented static and dynamic behaviors can emerge from engineered nonhomogeneous architectures. However, most designs operate at a single length scale and optimize a single performance criterion. Evidence from nature and prior studies suggests that multiscale architectures can enhance performance and broaden applications, yet their design remains challenging. To address this, a Hierarchical Metamaterial Automated Design (H-MAD) framework has been developed. This framework employs a sequential, evolutionary optimization approach to generate a population of heterogeneous structures at each scale, optimize microstructure placement and ensure cross-scale compatibility. As a case study, H-MAD is applied to 2D pentamode-like hierarchical metamaterials designed for extreme bulk-to-shear modulus ratios (B/G). The resulting architectures exhibit pentamode-like behavior across diverse configurations, demonstrating the framework's efficacy. With just two length scales, these hierarchical designs surpass both non-hierarchical structures with the same mesoscale configuration and classical pentamodes with finite joints. The best design achieves B/G approximate to 15 x 10^³ under bulk modulus constraints-nearly an order of magnitude higher than the baseline pentamode ratio. Even without constraints, the optimal H-MAD design attains B/G > 10 x 10^³, significantly outperforming conventional pentamodes. The results demonstrate that hierarchical design, combined with stiffness tailoring across scales, can enhance mechanical performance while maintaining adequate bulk moduli. The persistence of pentamode-like performance across diverse hierarchical designs indicates resilience to fabrication imperfections and material uncertainties, ensuring robust performance in practical applications. This advancement in hierarchical metamaterial design represents a step towards expanding the limits of metamateria

关键词： Mechanical metamaterials Hierarchical structures evolutionary algorithms Automated multiscale design sequential optimization Pentamode materials

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 268卷

作者： Ren, Yingying Li, Qiuli Guo, Yangyang Pedrycz, Witold Xing, Lining Liu, Anfeng Song, Yanjie Guangxi Univ Sch Comp Elect & Informat Nannig Peoples R China Network Technol Guangxi Key Lab Multimedia Commun Nanning Peoples R China Guangxi Univ Key Lab Parallel Distributed & Intelligent Comp Nanning Peoples R China Beijing Jiaotong Univ Sch Syst Sci Beijing Peoples R China Univ Alberta Dept Elect & Comp Engn Edmonton AB Canada Polish Acad Sci Syst Res Inst Warsaw Poland Fac Engn & Nat Sci Dept Comp Engn Istanbul Turkiye Xidian Univ Sch Elect Engn Xian Peoples R China Cent South Univ Sch Elect Engn Changsha Peoples R China Dalian Maritime Univ Natl Engn Res Ctr Maritime Nav Syst Dalian Peoples R China

With the rapid development of the satellite industry, the information transmission network based on communication satellites has gradually become a major and important part of the future satellite ground integration network. However, the low transmission efficiency of the satellite data relay back mission has become a problem that is currently constraining the construction of the system and needs to be solved urgently. Effectively planning the task of satellite ground networking by reasonably scheduling resources is crucial for the efficient transmission of task data. In this paper, we hope to provide a task execution scheme that maximizes the profit of the networking task for satellite ground network planning considering feeding mode (SGNPFM). To solve the SGNPFM problem, a mixed-integer planning model with the objective of maximizing the gain of the link-building task is constructed, which considers various constraints of the satellite in the feed-switching mode. Based on the problem characteristics, we propose a distance similarity-based genetic optimization algorithm (DSGA), which considers the state characteristics between the tasks and introduces a weighted Euclidean distance method to determine the similarity between the tasks. To obtain more high-quality solutions, different similarity evaluation methods are designed to assist the algorithm in intelligently screening individuals. The DSGA also uses an adaptive crossover strategy based on similarity mechanism, which guides the algorithm to achieve efficient population search. In addition, a task scheduling algorithm considering the feed-switching mode is designed for decoding the algorithm to generate a high-quality scheme. The results of simulation experiments show that the DSGA can effectively solve the SGNPFM problem. Compared to other algorithms, the proposed algorithm not only obtains higher quality planning schemes but also has faster algorithm convergence speed. The proposed algorithm improves data tra

关键词： Communications satellite Satellite ground networking Feed-switching Similarity assessment evolutionary algorithms

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