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dragonfly algorithm-support vector machine approach for prediction the optical properties of blood

COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING 2024年第9期27卷 1119-1128页

作者： Omari, Faiza Khaouane, Latifa Laidi, Maamar Ibrir, Abdellah Fissa, Mohamed Roubehie Hentabli, Mohamed Hanini, Salah Yahia Fares Univ Lab Biomat & Transport Phenomena LBMTP Medea Algeria Yahia Fares Univ Fac Technol Mat & Environm Lab LME Medea Algeria SAIDAL Complex Medea Qual Control Lab Medea Algeria

Knowledge of the optical properties of blood plays important role in medical diagnostics and therapeutic applications in laser medicine. In this paper, we present a very rapid and accurate artificial intelligent approach using dragonfly algorithm/Support Vector Machine models to estimate the optical properties of blood, specifically the absorption coefficient, and the scattering coefficient using key parameters such as wavelength (nm), hematocrit percentage (%), and saturation of oxygen (%), in building very highly accurate dragonfly algorithm-Support Vector Regression models (DA-SVR). 1000 training and testing sets were selected in the wavelength range of 250-1200 nm and the hematocrit of 0-100%. The performance of the proposed method is characterized by high accuracy indicated in the correlation coefficients (R) of 0.9994 and 0.9957 for absorption and scattering coefficients, respectively. In addition, the root mean squared error values (RMSE) of 0.972 and 2.9193, as well as low mean absolute error values (MAE) of 0.2173 and 0.2423, this result showed a strong match with the experimental data. The models can be used to accurately predict the absorption and scattering coefficients of blood, and provide a reliable reference for future studies on the optical properties of human blood.

关键词： Absorption coefficient scattering coefficient human blood dragonfly algorithm support vector machine

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A Novel Technique for Drone Path Planning Based on a Neighborhood dragonfly algorithm

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SENSORS 2025年第3期25卷 863-863页

作者： Agrawal, Sameer Patle, Bhumeshwar K. Sanap, Sudarshan MIT Art Design & Technol Univ Dept Mech Engn Pune 412201 India

Autonomous aerial drone navigation is a rapidly growing topic of research due to its vast application in various indoor applications, including surveillance, search and rescue missions, and environmental monitoring. Current research focuses on the implementation of neighborhood dragonfly algorithms (NDAs) for path planning for single and multiple drones in various indoor environments containing stationary and moving obstacles. The collaborative behavior of dragonflies is a key concept in the current study that helps in exploring the solution space effectively and results in a faster convergence rate. To validate the performance of the proposed NDA approach, various environments are created in real time, and replicas of the same are generated using MATLAB software. Our analysis shows a close agreement between simulation and experimental results, with path length and navigational time differences of less than 5.7%. This underscores the consistency and feasibility of the NDA approach, placing the groundwork for robust and efficient drone navigation systems. The proposed NDA approach is also compared with those already developed, like IACO and PRM, in a similar environment. The NDA approach shows a better performance in terms of smooth path planning and path length optimization. The saving in path length is more than 5%.

关键词： path planning dragonfly algorithm aerial navigation drone AI technique optimization

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A Fuzzy MARCOS-Based Analysis of dragonfly algorithm Variants in Industrial Optimization Problems

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INFORMATICA 2024年第1期35卷 155-178页

作者： Kalita, Kanak Ganesh, Narayanan Shankar, Rajendran Chakraborty, Shankar Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & T Dept Mech Engn Avadi India Chandigarh Univ Univ Ctr Res & Dev Mohali 140413 India Vellore Inst Technol Sch Comp Sci & Engn Chennai India Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Engn Vaddeswaram India Jadavpur Univ Dept Prod Engn Kolkata India

Metaheuristics are commonly employed as a means of solving many distinct kinds of optimization problems. Several natural-process-inspired metaheuristic optimizers have been introduced in the recent years. The convergence, computational burden and statistical relevance of metaheuristics should be studied and compared for their potential use in future algorithm design and implementation. In this paper, eight different variants of dragonfly algorithm, i.e. classical dragonfly algorithm (DA), hybrid memory-based dragonfly algorithm with differential evolution (DADE), quantum-behaved and Gaussian mutational dragonfly algorithm (QGDA), memory-based hybrid dragonfly algorithm (MHDA), chaotic dragonfly algorithm (CDA), biogeography-based Mexican hat wavelet dragonfly algorithm (BMDA), hybrid Nelder-Mead algorithm and dragonfly algorithm (INMDA), and hybridization of dragonfly algorithm and artificial bee colony (HDA) are applied to solve four industrial chemical process optimization problems. A fuzzy multi-criteria decision making tool in the form of fuzzy-measurement alternatives and ranking according to compromise solution (MARCOS) is adopted to ascertain the relative rankings of the DA variants with respect to computational time, Friedman's rank based on optimal solutions and convergence rate. Based on the comprehensive testing of the algorithms, it is revealed that DADE, QGDA and classical DA are the top three DA variants in solving the industrial chemical process optimization problems under consideration.

关键词： dragonfly algorithm Process optimization MCDM MARCOS

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Optimization of production schedules of multi-plants for dispatching ready-mix concrete trucks by integrating grey wolf optimizer and dragonfly algorithm

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ENGINEERING CONSTRUCTION AND ARCHITECTURAL MANAGEMENT 2024年第11期31卷 4602-4624页

作者： Pham, Vu Hong Son Trang, Nguyen Thi Nha Dat, Chau Quang Ho Chi Minh City Univ Technol HCMUT Fac Civil Engn Ho Chi Minh City Vietnam Hanoi Univ Civil Engn HUCE Fac Construct Management & Econom Hanoi Vietnam

PurposeThe paper aims to provide an efficient dispatching schedule for ready-mix concrete (RMC) trucks and create a balance between batch plants and construction ***/methodology/approachThe paper focused on developing a new metaheuristic swarm intelligence algorithm using Java code. The paper used statistical criterion: mean, standard deviation, running time to verify the effectiveness of the proposed optimization method and compared its derivatives with other algorithms, such as genetic algorithm (GA), Tabu search (TS), bee colony optimization (BCO), ant lion optimizer (ALO), grey wolf optimizer (GWO), dragonfly algorithm (DA) and particle swarm optimization (PSO).FindingsThe paper proved that integrating GWO and DA yields better results than independent algorithms and some selected algorithms in the literature. It also suggests that multi-independent batch plants could effectively cooperate in a system to deliver RMC to various construction ***/valueThe paper provides a compelling new hybrid swarm intelligence algorithm and a model allowing multi-independent batch plants to work in a system to deliver RMC. It fulfills an identified need to study how batch plant managers can expand their dispatching network, increase their competitiveness and improve their supply chain operations.

关键词： Optimization Ready-mix concrete dispatching Swarm intelligence Hybrid algorithm dragonfly algorithm Grey wolf optimizer

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Hybridization of chaos theory and dragonfly algorithm to maximize spatial area coverage of swarm robots

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EVOLUTIONARY INTELLIGENCE 2024年第3期17卷 1327-1340页

作者： Singh, Amrit Pal Kumar, Gaurav Dhillon, Guneet Singh Taneja, Harsh Guru Gobind Singh Indraprastha Univ Univ Sch Automat & Robot New Delhi India Guru Gobind Singh Indraprastha Univ USICT New Delhi India SVKMs NMIMS Univ Sch Technol Management & Engn Navi Mumbai India

Swarm robotics show collective behavior in order to work in multi agent scenario, where spatial area coverage is an emergent area of research. This work introduces a hybrid technique for optimized spatial area coverage. A combination of dragonfly algorithm (DA) and chaotic mapping is proposed and the entire study is carried out in two phases. In the first phase, DA's parameters are measured on the basis of percentage of area covered, entropy and number of pop-up threats detected. In the second phase, Chaotic distribution is applied in swarm algorithms (i.e. DA along with Bat algorithm and Accelerated Particle Swarm Optimization) to implement hybridized models. Chaotic dragonfly algorithm along with Chaotic Bat algorithm and Chaotic Accelerated Particle Swarm Optimization) is implemented. To evaluate the performance of hybridized models, a comprehensive comparison is drawn among the Levy and Chaotic versions of all three swarm algorithms. It is concluded that the proposed DA outperforms the rest. DA not only showed better results for the mentioned metrics, but also displayed uniform behavior over multiple runs.

关键词： Swarm intelligence Swarm robots dragonfly algorithm Chaotic distribution Area coverage Bat algorithm

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Cross-layer optimization using Taylor-Multi-Verse dragonfly algorithm to support interoperability in heterogeneous Internet of Things network

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JOURNAL OF CONTROL AND DECISION 2024年

作者： Desai, Jyoti Ramachandra Dammur, Annapurna PES Inst Technol Dept CSE Bangalore South Campus Bangalore India PES Inst Technol ISE Dept Bangalore India

Interoperability acts as a major issue in heterogeneous Internet of Things (IoT), nowadays. In prior researches, the interacted IoT devices are not feasible without monitoring the services coordination as well as interoperability. Thus, the Taylor-Competitive Multi-Verse dragonfly algorithm (Taylor CMVDA) is developed to support interoperability in heterogeneous IoT for cross-layer optimisation. In this paper, an optimised path through routing is established in the physical layer, the gateway selection is established in the network layer and the Cluster head (CH) is employed in the data link layer. The CH node and gateway node are selected through an adaptive black hole (BH) dynamic clustering and neighbourhood approach. The routing is performed through the introduced Taylor CMVDA, which is the integration of Competitive Multi-Verse Optimizer (CMVO) and dragonfly algorithm (DA) with the Taylor series. The proposed model attains the throughput, energy, and delay of 0.5492, 0.6768 J and 0.3538 sec.

关键词： Interoperability dragonfly algorithm Multi-Verse Optimizer Taylor series blackhole dynamic clustering

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Optimizing tribological performance in elevated temperature of TiC reinforced A356 matrix composites designed as automotive friction materials using dragonfly algorithm

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2023年第4期237卷 973-984页

作者： Simsek, Dogan Ozyurek, Dursun Ileri, Erol Karaoglan, Aslan Deniz Natl Def Univ Army NCO Vocat HE Sch Dept Automot Sci TR-10110 Balikesir Turkey Karabuk Univ Dept Mfg Engn Karabuk Turkey Balikesir Univ Dept Ind Engn Balikesir Turkey

The objective of this study designed in three steps is to evaluate the optimization of tribological performance in the elevated temperature of TiC reinforced A356 matrix composites, produced by mechanical alloying (MA), using the dragonfly algorithm (DA). In the first step, an experimental design is created to formulate the problem as an optimization problem with the help of regression modeling, and wear tests by adding a temperature module according to ASTM G99-05 standards are performed at various reinforced rates, operating temperatures, and loads. In the second step, regression models are fitted to the experimental results. In the third step, the dragonfly algorithm (DA) - one of the recent nature inspired metaheuristic optimization algorithms - has been adapted to optimize the formulated problem. The results of DA indicate that the optimum factor levels for the reinforced rate, operating temperature, and load are determined as 7%, 10 degrees C, and 20 N respectively to optimize the responses, namely weight loss, wear rate, and friction coefficient.

关键词： Composites elevated temperature TiC optimization dragonfly algorithm

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Adaptive coordination of directional overcurrent relays for meshed distribution networks with distributed generations using dragonfly algorithm

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ELECTRICAL ENGINEERING 2023年第6期105卷 3511页

作者： Sarwagya, Kumari Nayak, Paresh Kumar Ranjan, Suman Indian Sch Mines Indian Inst Technol Dept Elect Engn Dhanbad 826004 Jharkhand India BIT Sindri Dept Elect Engn Dhanbad 828123 Jharkhand India

The deterministic directional overcurrent relay (DOCR) coordination approaches find limitation in providing fast and reliable protection to today's distribution networks due to growing integration of distributed generations (DGs). In this paper, an adaptive DOCR protection coordination scheme is proposed using an efficient optimization algorithm called the dragonfly algorithm (DA). The main inspiration of the proposed DA optimization technique originates from static and dynamic swarming behaviours of dragonflies. In this approach, the dominant changes in the network topologies are identified by monitoring the status of the circuit breakers connected at the terminals of the DGs and other main power components. When any dominant changes in the network topologies or operating modes are identified, the fault current for that particular condition is calculated and the new optimal DOCRs settings for the prevailing condition are obtained from the substation central computer in online mode. The comparative results with the existing approaches justify the superiority of the proposed scheme in achieving the minimum overall relay operating time and maintaining the coordination between the primary and backup relay pairs. In the proposed adaptive protection scheme, the average percentage reduction in the overall operating times of DOCRs in the 6-bus and the IEEE 14-bus test systems with different levels DG penetration is found to be 49.7% and 15%, respectively, compared to the existing approaches.

关键词： Adaptive protection dragonfly algorithm Distribution system Distributed generation Optimal relay coordination

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A New Feature Selection Method Based on dragonfly algorithm for Android Malware Detection Using Machine Learning Techniques

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INTERNATIONAL JOURNAL OF INFORMATION SECURITY AND PRIVACY 2023年第1期17卷 1-18页

作者： Guendouz, Mohamed Amine, Abdelmalek Dr Moulay Tahar Univ Saida GeCoDe Lab Saida Algeria

Android is the most popular mobile OS;it has the highest market share worldwide on mobile devices. Due to its popularity and large availability among smartphone users from all around the world, it becomes the first target for cyber criminals who take advantage of its open-source nature to distribute malware through applications in order to steal sensitive data. To cope with this serious problem, many researchers have proposed different methods to detect malicious applications. Machine learning techniques are widely being used for malware detection. In this paper, the authors proposed a new method of feature selection based on the dragonfly algorithm, named BDA-FS, to improve the performance of Android malware detection. Different feature subsets selected by the application of this proposed method in combination with machine learning were used to build the classification model. Experimental results show that incorporating dragonfly algorithm into Android malware detection performed better classification accuracy with few features compared to machine learning without feature selection.

关键词： Android Malware Detection dragonfly algorithm Feature Selection Machine Learning

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dragonfly algorithm for Multi-target Search Problem in Swarm Robotic with Dynamic Environment Size 35th

Dragonfly Algorithm for Multi-target Search Problem in Swarm...

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35th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE)

作者： Hamami, Mohd Ghazali Mohd Ismail, Zool H. Univ Teknol MARA Cawangan Johor Coll Engn Sch Mech Engn Kampus Pasir Gudang Masai 81750 Johor Malaysia Univ Teknol Malaysia Malaysia Japan Int Inst Technol Jalan Sultan Yahya Petra Kuala Lumpur 54100 Malaysia

ISBN: (纸本)9783031085307;9783031085291

Target search elements are very important in real-world applications such as post-disaster search and rescue missions, and pollution detection. In such situations, there will be time limitations, especially under a dynamic environment size which makes multi-target search problems are more demanding and need a special approach and intention. To answer this need, a proposed multi-target search strategy, based on dragonfly algorithm (DA) has been presented in this paper for a Swarm Robotic application. The proposed strategy utilized the DA static swarm (food hunting process) and dynamic swarm (migration process) to achieve the optimized balance between the exploration and exploitation phases during the multi-target search process. For performance evaluation, numerical simulations have been done and the initial results of the proposed strategy show more stability and efficiency than the previous works.

关键词： Multi-target search problem Swarm robotics dragonfly algorithm

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