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3rd International Conference on Cloud Computing, Big Data Application and Software Engineering, CBASE 2024

作者： Zhang, Han Wang, Baocheng Guo, Hao The Northern Industrial University College of Information Shijingshan District Beijing China School of Information Science North China University of Technology Shijingshan District Beijing China

ISBN: (纸本)9798331506582

The league chain is widely used in practical Byzantine fault tolerance consensus algorithm as the low throughput, delay higher problem. In order to solve the above problems, this paper proposes an optimization research of Byzantine fault tolerant consensus algorithm based on involvement, namely IBFT algorithm. During initialization, buffer node area, divided into node in the process of consensus dynamic add delete;As you progress through the consensus process, calculation with the involvement of consensus node number, in order to calculate its involvement;Finally, the nodes were dynamically replaced according to the level of involvement. The simulation results show that compared with the traditional PBFT algorithm, IBFT algorithm in the time delay are lower, in the process of consensus a greater throughput. © 2024 IEEE.

关键词： optimization algorithms

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Enhancing Intrusion Detection Systems: A Comparative Study using Whale optimization Algorithm and Hawks optimization Algorithm 11

Enhancing Intrusion Detection Systems: A Comparative Study u...

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11th International Conference on Electrical and Electronics Engineering, ICEEE 2024

作者： Abualhaj, Mosleh M. Al-Khatib, Sumaya N. Al-Allawee, Ali Munther, Alhamza Anbar, Mohammed Al-Ahliyya Amman University Department of Networks and Cybersecurity Amman Jordan Irimas University of Haute Alsace Colmar France University Technology and Applied Sciences It Department Sur Oman Universiti Sains Malaysia Penang Malaysia

ISBN: (纸本)9798350362541

Intrusion Detection Systems (IDS) play a pivotal role in safeguarding computer networks against malicious activities. This research explores the efficacy of two optimization algorithms, namely Whale optimization Algorithm (WOA) and Hawks optimization (HHO), for feature selection to enhance the performance of IDS. The NSL-KDD dataset is utilized as a benchmark to evaluate the impact of selected features on classification accuracy. The study employs the Random Forest (RF) classification algorithm to evaluate the effectiveness of feature selection methods. Implementation is conducted in Python, leveraging its versatile libraries and frameworks. Comparative analysis reveals that when integrating the selected features, RF attains superior results compared to utilizing features separately for binary and multiclass classifications. Results indicate that RF with HHO achieves an impressive accuracy of 98.02%, showcasing the prowess of HHO in enhancing IDS performance. Meanwhile, RF with WOA achieves a commendable accuracy of 97.81%. © 2024 IEEE.

关键词： optimization algorithms

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Pareto Front Estimation Model optimization for Aggregative Solution Set Representation 13

Pareto Front Estimation Model Optimization for Aggregative S...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Okumura, Naru Takadama, Keiki Sato, Hiroyuki The University of Electro-Communications 1-5-1 Chofugaoka Tokyo Chofu182-8585 Japan

ISBN: (纸本)9798350308365

This paper aims to enhance the accuracy of the Pareto front estimation model as an aggregative representation of the non-dominated solutions and proposes an algorithm named the Pareto front Model optimization Algorithm (PFMOA). The typical output of multi-objective optimization is a set of non-dominated solutions approximating the Pareto front, which is the optimal trade-off between objective function values. The more non-dominated solutions there are, the more accurately the Pareto front can be approximated. However, especially in real-world problems, there is often a limitation on increasing the number of solutions due to the time required to execute objective functions. For the issue, a Pareto front estimation method interpolates between a limited number of non-dominated solutions to represent changes in objective function values even in regions where non-dominated solutions are not actually obtained. The proposed PFMOA enhances the accuracy of the Pareto front estimation model while evaluating new solutions. PFMOA generates the estimated Pareto front based on a known non-dominated solution set using Kriging. PFMOA focuses on the point with the lowest confidence levels on the estimated Pareto front and evaluates the corresponding point on the estimated Pareto set. PFMOA also generates new solutions using evolutionary variation. PFMOA stochastically switches these two model-based and evolutionary-based solution generation methods. If the new solution is non-dominated, it is included in the known non-dominated solution set, and the process is repeated to enhance the accuracy of the Pareto front estimation model. The effectiveness of the proposed PFMOA is verified using DTLZ1-3, and WFG4 problems. The results show that, in all cases, the accuracy of the Pareto front approximation by the Pareto front estimation model is higher than that by the obtained solution set itself. Additionally, the combination of model-based and evolutionary-based solution generation is bene

关键词： optimization algorithms

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Competition-Based Stochastic Learning Particle Swarm optimization Algorithm for Large-Scale optimization Problem 11

Competition-Based Stochastic Learning Particle Swarm Optimiz...

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11th International Conference on Machine Intelligence Theory and Applications, MiTA 2024

作者： Zheng, Xin Li, Jian-Yu Yang, Qiang Duan, Dan-Ting Zhan, Zhi-Hui Henan Normal University College of Computer and Information Engineering Xinxiang453000 China Nankai University College of Artificial Intelligence Tianjin300350 China Nanjing University of Information Science and Technology School of Artificial Intelligence Jiangsu China Beijing China

ISBN: (纸本)9798350385670

With the development of information technology, large-scale optimization problem is the core issue in various applications. Due to the high dimension characteristics, large-scale optimization problems are challenging to solve. Therefore, this paper proposes a competition-based stochastic learning particle swarm optimization algorithm (CSLPSO) for solving large-scale optimization problems efficiently, which has two novel designs. First, a competition-based stochastic learning strategy is proposed to improve the exploration ability of PSO. Second, a parameter control mechanism suitable for the CSLPSO algorithm is proposed, so that the algorithm does not require manual parameter selection. In the experiment, the CSLPSO algorithm is applied to 12 large-scale problems with state-of-the-art algorithms as competitors. The experimental results show the effectiveness of the proposed CSLPSO. © 2024 IEEE.

关键词： optimization algorithms

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Addressing Phishing Threats Using A Metaheuristic Perspective On Machine Learning Classification Models Code

JOURNAL OF APPLIED SCIENCE AND ENGINEERING

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JOURNAL OF APPLIED SCIENCE AND ENGINEERING 2025年第7期28卷 1503-1514页

作者： Hu, Bo Zhang, Sainan Nanjing Normal Univ Special Educ Ctr Informat Construct & Management Nanjing 210038 Peoples R China

Web phishing attacks have emerged as a significant threat to online security, enabling phishers to steal sensitive financial information and commit fraud. To combat this, many anti-phishing systems have been developed, focusing on detecting phishing content in online communications. This study introduces novel approaches to enhance phishing detection by employing machine learning techniques. Specifically, three different single models were analyzed: Random Forest Classifier (RFC), Adaptive Boosting Classification (ADAC), and Na & iuml;ve Bayes Classification Algorithm (NBC). These models were optimized using Artificial Rabbits optimization (ARO), resulting in hybrid models RFAR, NBAR, and ADAR. The results of the models' analysis indicate that the RFAR hybrid model performs better than the other single models and their optimized models. The RFAR model achieved precision scores of 0.950 for phishing websites, 0.954 for suspicious websites, and 0.872 for legitimate websites, with corresponding recall values of 0.929, 0.954, and 0.990, respectively. In comparison, the ADAR model was notably effective in classifying legitimate websites with a precision score of 0.896. The study's novelty lies in integrating ARO with traditional classifiers to create hybrid models that improve classification accuracy.

关键词： Phishing Cyber Attacks Classification Data Mining optimization algorithms Phishing Websites Prediction Artificial Intelligence

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Covariance Matrix Adaptation MAP-Annealing: Theory and Experiments

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ACM Transactions on Evolutionary Learning and optimization 2025年第1期5卷 1-53页

作者： Zhao, Shihan Tjanaka, Bryon Fontaine, Matthew C. Nikolaidis, Stefanos Thomas Lord Department of Computer Science University of Southern California Los AngelesCA United States

Single-objective optimization algorithms search for the single highest quality solution with respect to an objective. Quality diversity (QD) optimization algorithms, such as Covariance Matrix Adaptation MAP-Elites (CMA-ME), search for a collection of solutions that are both high quality with respect to an objective and diverse with respect to specified measure functions. However, CMA-ME suffers from three major limitations highlighted by the QD community: prematurely abandoning the objective in favor of exploration, struggling to explore flat objectives, and having poor performance for low-resolution archives. We propose a new QD algorithm, CMA MAP-Annealing (CMA-MAE), and its differentiable QD variant, CMA-MAE via a Gradient Arborescence (CMA-MAEGA), that address all three limitations. We provide theoretical justifications for the new algorithm with respect to each limitation. Our theory informs our experiments, which support the theory and show that CMA-MAE achieves state-of-the-art performance and robustness on standard QD benchmark and reinforcement learning domains. © 2025 Copyright held by the owner/author(s).

关键词： optimization algorithms

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FL-Jaya scheduler: an optimized Jaya algorithm for effective independent task scheduling in cloud systems

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COMPUTING 2025年第6期107卷 1-38页

作者： Mashaqbeh, Husam S. Mjlae, Salameh A. Isra Univ Fac Informat Technol Dept Comp Sci Amman Jordan Isra Univ Fac Informat Technol Dept Informat Secur & Cyberspace Amman Jordan Al Balqa Appl Univ BAU Prince Abdullah Bin Ghazi Fac ICT Dept Comp Sci Salt Jordan

Efficient task scheduling in Cloud Computing remains an NP-hard challenge due to combinatorial search spaces and resource heterogeneity, often leading to premature convergence in existing metaheuristics. This paper proposes FL-Jaya, an enhanced Jaya algorithm that addresses these limitations through two key innovations: (1) a Fitness-Distance Balance (FDB) mechanism, which preserves population diversity by selecting solutions that optimally trade off fitness quality and spatial distribution, and (2) a L & eacute;vy Flight (LF) operator, enabling stochastic long jumps to escape local optima. By unifying FDB and LF into a single update rule, FL-Jaya dynamically balances exploration and exploitation, overcoming stagnation in large-scale scheduling. Experiments on artificial (100-1000 tasks) and real-world Google Cloud Jobs datasets demonstrate FL-Jaya's superiority over six algorithms-Jaya, Particle Swarm optimization, Coati optimization Algorithm, Whale optimization Algorithm, Bald Eagle Search, and Snake Optimizer. FL-Jaya achieves 38.98% lower makespan and 44.63% higher average resource utilization (ARU) than standard Jaya on artificial workloads, with real-world results showing 35.34% makespan reduction and 44.63% ARU improvement. These gains stem from FL-Jaya's ability to maintain solution diversity while navigating complex search spaces, outperforming peers in convergence speed and scalability. The algorithm's parameter-light design and consistent performance underscore its practicality for heterogeneous cloud environments.

关键词： Cloud computing Jaya algorithm Task scheduling FDB optimization algorithms L & eacute vy flight Sensitivity analysis

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Differential Evolution with Success Rate-based adaptation CL-SRDE for Constrained optimization 13

Differential Evolution with Success Rate-based adaptation CL...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Stanovov, Vladimir Semenkin, Eugene Institute of Informatics and Telecommunication Reshetnev Siberian State University of Science and Technology Krasnoyarsk Russia

ISBN: (纸本)9798350308365

Constrained numerical optimization problems introduce significant challenges for optimization methods. One of the popular heuristic optimization techniques for such problems is the Differential Evolution algorithm. This study focuses on applying a variant of differential evolution with two populations to the set of benchmark problems from the CEC 2024 Constrained Single Objective Numerical optimization competition. In the proposed CL-SRDE algorithm the adaptation of the scaling factor is performed based on the success rate, which is the number of replaced individuals during selection divided by population size. The constraints are handled by a modified epsilon-constraint method. The analysis of the experimental results show that the used adaptation scheme achieves better feasibility rates and overall performance, compared to the alternative approaches. © 2024 IEEE.

关键词： optimization algorithms

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Improved Intensity-Based Image Registration via Archimedes optimization Algorithm 6

Improved Intensity-Based Image Registration via Archimedes O...

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6th International Conference on Intelligent Systems and Computer Vision, ISCV 2024

作者： Kmich, Mohamed Karmouni, Hicham Harrade, Inssaf Ouazzani, Mohammed Jamil Qjidaa, Hassan Sayyouri, Mhamed Sidi Mohamed Ben Abdellah University Engineering Systems and Applications Laboratory National School of Applied Sciences Fez Morocco Cadi Ayyad University Engineering Systems and Applications Laboratory National School of Applied Sciences Marrakech Morocco Fez Morocco Sidi Mohamed Ben Abdellah University CED-ST STIC Laboratory of Electronic Signals and Systems of Information LESSI Dhar El Mahrez Faculty of Science Fez Morocco

ISBN: (纸本)9798350350180

Image registration is the process of superimposing two or more images to align them correctly. The aim is generally to minimize spatial discrepancies between images. In this context, we propose a new method for intensity-based image registration (IBIR) based on the optimization of the parameters of the 2D affine geometric transformation needed to align the images via the Archimedes Optimisation Algorithm (ArOA), and on the normalization of the sum of squared differences (NSSD) between different images taken at different times or from different views and the desired image as an objective function. The proposed method, based on ArOA, is robust to variations and perturbations in the image data. It can avoid local optima and find optimal solutions even when elements such as noise, distortions, or artefacts are present in the images. Simulations show the efficiency of the proposed method in terms of matching aligned images and computation time compared with other methods based on the Sine-Cosine Algorithm (SCA) and the Equilibrium Optimizer (EA). © 2024 IEEE.

关键词： optimization algorithms

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Hybrid Cooperative Crayfish and Mountain Gazelle Algorithm for Global optimization 9

Hybrid Cooperative Crayfish and Mountain Gazelle Algorithm f...

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9th International Conference on Information Technology and Digital Applications, ICITDA 2024

作者： Lee, Boon-Hao Lee, Ying-Loong Khor, Kok-Chin Universiti Tunku Abdul Rahman Lee Kong Chian Faculty of Engineering and Science Selangor Malaysia

ISBN: (纸本)9798331510732

The crayfish optimization algorithm (COA) and mountain gazelle optimization algorithm (MGO) have emerged as two powerful metaheuristics for global optimization. It has been shown that these two metaheuristics outperform conventional metaheuristics and are capable of finding near-optimal solutions for most of the benchmark functions. However, in certain benchmark functions, COA and MGO do not perform better than the conventional metaheuristics. To this end, a new Hybrid Cooperative COA-MGO algorithm (HCCMGA) is proposed. In the proposed HCCMGA algorithm, the solution vector is split into two solution subvectors of smaller dimensions, one optimized by COA and the other by MGO. The fitness of each search agent in COA and MGA is evaluated in a cooperative manner, whereby the solution subvector in COA is combined with the best solution subvector from MGO and vice versa. The proposed HCCMGA is evaluated using 13 benchmark functions and is compared with several state-of-the-art metaheuristics. Results show that the proposed HCCMGA outperforms state-of-the-arts optimization algorithms in solution quality, search stability and convergence speed, as the HCCMGA achieves solutions closest to the optimum in eight out of the 13 benchmark functions with low standard deviations and less numbers of iterations. © 2024 IEEE.

关键词： optimization algorithms

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