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Competitive war strategy Optimizer for global optimization and complex engineering problems

Journal of Ambient Intelligence and Humanized Computing 2025年 1-12页

作者： Tummala, S. L. V. Ayyarao Kishore, G. Indira Department of EEE GMR Institute of Technology Rajam India

To solve complex engineering problems efficiently, an optimization algorithm must exhibit robust exploration and exploitation capabilities. This paper introduces the Competitive war strategy Optimizer (CWSO), an enhanced version of the recently developed war strategy optimization (WSO) algorithm, inspired by ancient military strategies. CWSO improves the original WSO by incorporating a competition-based learning mechanism, where weaker solutions are refined through learning from stronger counterparts, and a nonlinear weight update mechanism that accelerates convergence and enhances search efficiency. CWSO's performance was rigorously assessed using the CEC-2014 benchmark functions, demonstrating substantial improvements in convergence speed and solution accuracy. It significantly outperformed popular algorithms, including Artificial Ecosystem-based optimization (AEO), Particle Swarm optimization (PSO), Comprehensive Learning Particle Swarm Optimizer (CLPSO), Grey Wolf optimization (GWO), and Harris Hawks optimization (HHO). In the Friedman ranking, CWSO secured the top spot with an average rank of 2.32, surpassing CLPSO (2.58), AEO (2.83), HHO (4.01), PSO (4.18), and GWO (5.07). To further validate its real-time optimization capabilities, CWSO was applied to the optimal power flow problem in the IEEE-30 bus system, focusing on minimizing fuel costs, reducing emissions, and minimizing power loss. When compared to twenty leading algorithms from the literature, CWSO delivered superior performance in all three optimization objectives.

关键词： Competitive mechanism Meta-heuristics optimization war strategy optimization algorithm

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An Intrusion Detection System in WSN Using an Optimized Self-Attention-Based Progressive Generative Adversarial Network

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IETE JOURNAL OF RESEARCH 2025年

作者： Ram, R. Saravana Saminathan, A. Gopi Anna Univ Reg Campus Dept Elect & Commun Engn Madurai Tamil Nadu India NPR Coll Engn & Technol Dept Elect & Commun Engn Dindigul Tamil Nadu India

In general, a number of data types can be sensed, processed, and transmitted over wireless communication networks. Therefore, an Intrusion Detection System in a wireless sensor network using an Optimized Self-Attention-Based Progressive Generative Adversarial Network (IDS-SAPGAN-NBOA-WSN) is proposed in this paper. The input data are gathered through the WSN-DS database. The obtained data are supplied into the pre-processing phase. An Altered Phase Preserving Dynamic Range Compression (APPDRC) is employed to eliminate data redundancy and restoration of missing values in WSN data. Then, war strategy optimization algorithm (WSOA) is applied to select ten features from input data. The selected features are given to Self-Attention-based Progressive Generative Adversarial Network (SAPGAN) for classifying intrusions, like Blackhole, Gray hole, Flooding, Scheduling, and Normal attacks. In general, SAPGAN does not adapt any optimization models to define optimum parameters to ensure the attack classification. That's why;the Namib Beetle optimization algorithm (NBOA) is proposed to improve the weight parameter of SAPGAN, which precisely categorizes the attacks. The proposed IDS-SAPGAN-NBOA-WSN approach is implemented and the performance metrics, such as accuracy, precision, and sensitivity are examined. The proposed technique attains 21.12%, 29.09%, 10.23%, 15.42%, and 21.05% higher accuracy when compared with existing techniques: IDS using Conditional Generative Adversarial Network in WSN (IDS-CGAN-XGBoost-WSN), Improved binary gray wolf optimizer along support vector machine for IDS in WSN (IDS-SVM-IBGWO-WSN), Intrusion detection scheme depending on a deep neural network (IDS-DNN-WSN), Effectual intrusion detection technique based upon dynamic autoencoder (IDS-LwDAN-WSN) and Enhanced grey wolf optimizer dependent particle swarm optimizer in WSN (IDS-SVM-EGWO-WSN), respectively.

关键词： Namib beetle optimization algorithm war strategy optimization algorithm WSN-DS dataset

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FHAWS: fuzzy hybrid arithmetic war strategy for parametric optimization of toughened glass on toughening machine

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第20期83卷 58421-58447页

作者： Dohare, Sunil Rajput, R. S. Rajiv Gandhi Proudyogiki Vishwavidyalaya Univ Inst Technol Dept Mech Engn Bhopal India

In recent years, toughened glass has become widely used in architectural applications due to its high structural strength, shatter resistance, soundproofing, heat resistance, and durability. However, despite its strong capabilities, toughened glass can explode completely when subjected to heavy impact, causing security-related constraints in areas prone to smash-and-grab attempts. This issue can only be prevented by enhancing the quality of toughened glass, particularly its tensile strength, compressive strength, and durability. To achieve this goal, a novel technique called the "fuzzy hybrid arithmetic war strategy" (FHAWS) approach is proposed in this paper. The approach utilizes the "hybrid arithmetic war strategy" (HAWS) algorithm to determine the optimal solution with a faster convergence rate. The HAWS algorithm integrates the standard Arithmetic optimization (AO) algorithm and war strategy optimization (WSO) algorithm. The fuzzy rule concept is introduced for regulating the improper searching algorithm. The proposed FHAWS approach optimizes toughened glass characteristics such as splitting strength, flexural strength, tensile strength, compressive strength, stress, temperature, fragmentation, heating time, cooling time, visual light, durability, and production cost. The experimental analysis illustrates that the proposed FHAWS approach is more efficient and robust in enhancing the quality of toughened glass than other compared state-of-the-art approaches. Overall, the paper proposes a novel approach to optimize the characteristics of toughened glass, addressing a critical issue in the architectural industry. However, the specific details of the approach and experimental results should be examined in the paper to assess the method's effectiveness comprehensively.

关键词： Toughened glass Arithmetic optimization algorithm war strategy optimization algorithm Fuzzy rules Tensile strength Compressive strength Durability

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An Optimal Faster Region-Based Convolutional Neural Network for Oil Adulteration Detection

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2023年第2期48卷 2231-2245页

作者： Surya, V Senthilselvi, A. SRM Inst Sci & Technol Dept Comp Sci Engn Chennai Tamil Nadu India

Natural oils such as avocado oil (AO), corn oil (CO), chamomile oil (CMO), and rapeseed oil (RSO) are used for improving health, skin, and hair care for centuries in different parts of the world. The AO, CO, and RSO oil are known for their antibacterial and moisturizing properties. The growth of the modern health and wellness industries has deceived people to find affordable, additive-free, and effective products. Consumers are always willing to pay a higher price for the unadulterated oils due to the major benefits it offers in terms of complexion and health. However, the AO, CMO, and RSO oils are rarely available in the market and the ones available are subject to adulteration risks which affect the consumer's health and also violate their rights. This paper presents a war strategy optimized faster Region-based Convolutional Neural Network (RCNN) architecture named Modified Faster RCNN for oil adulteration detection. The pure natural AO, CO, CMO, and RSO oils are adultered with different oils such as vegetable oils with different ratios and these oils themselves. Spectra analysis was conducted to identify the adultered vegetable oil samples and the Modified Faster RCNN was employed to classify the pure oil from these unadultered samples. The results show that the proposed model is effective in rapidly identifying the adulterated components presented in high-quality oils.

关键词： Chemometric methods Oil adulteration Faster region-based convolutional neural network war strategy optimization algorithm Excitation-emission matrix fluorescence

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Instant Sign Language Recognition by war strategy algorithm Based Tuned Machine Learning

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INTERNATIONAL JOURNAL OF NETWORKED AND DISTRIBUTED COMPUTING 2024年第2期12卷 344-361页

作者： Abd Al-Latief, Shahad Thamear Yussof, Salman Ahmad, Azhana Khadim, Saif Mohanad Abdulhasan, Raed Abdulkareem Natl Energy Univ Univ Tenaga Nas Coll Grad Studies COGS Kajang 43000 Selangor Malaysia Natl Energy Univ Univ Tenaga Nas Inst Informat & Comp Energy Selangor Malaysia Natl Energy Univ Univ Tenaga Nas Coll Comp & Informat Selangor Malaysia Univ Tun Hussein Onn Fac Elect & Elect Engn Johor Baharu Malaysia

Sign language serves as the primary means of communication utilized by individuals with hearing and speech disabilities. However, the comprehension of sign language by those without disabilities poses a significant challenge, resulting in a notable disparity in communication across society. Despite the utilization of numerous effective Machine learning techniques, there remains a minor compromise between accuracy rate and computing time when it comes to sign language recognition. A novel sign language recognition system is presented in this paper with an exceptionally accurate and expeditious, which is developed upon the recently devised metaheuristic war strategy optimization algorithm. Following the preprocessing, both of spatial and temporal features has been extracted using the Linear Discriminant Analysis (LDA) and Gray-level cooccurrence matrix (GLCM) methods. Afterward, the war strategy optimization algorithm has been adopted in two procedures, first in optimizing the extracted set of features, and second to fine-tune the hyperparameters of six standard machine learning models in order to achieve precise and efficient sign language recognition. The proposed system was assessed on sign language datasets of different languages (American, Arabic, and Malaysian) containing numerous variations. The proposed system attained a recognition accuracy ranging from 93.11% to 100% by employing multiple optimized machine learning classifiers and training time of 0.038-10.48 s. As demonstrated by the experimental outcomes, the proposed system is exceptionally efficient regarding time, complexity, generalization, and accuracy.

关键词： Hyperparameter tuning war strategy optimization algorithm Feature optimization Machine learning Sign language recognition

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