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A Quasi-Oppositional Based flamingo search algorithm Integrated with Generalized Ring Crossover for Effective Feature Selection

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IETE JOURNAL OF REsearch 2023年第12期69卷 8623-8639页

作者： Durgam, Revathi Devarakonda, Nagaraju VIT AP Univ Sch Comp Sci & Engn Amaravati India

The goal of this article is to present a new hybrid technique for solving the feature selection problem. Conventionally, the process of determining the most relevant subset based on the given criteria is known as feature selection. Specifically, feature selection is a real world issue that can be tackled by an optimization technique. In the proposed method, a novel hybrid Quasi oppositional based flamingo search algorithm with Generalized Ring Crossover (QOFSA-GRC) model is introduced to pick the most relevant features from the dataset. Specifically, the Quasi oppositional based flamingo search algorithm (QOFSA) generates two populations one by Quasi oppositional based learning and the other by the flamingo search algorithm to resolve the issue known as the curse of dimensionality. Then, by utilizing generalized ring crossover, the multiple relevant features are selected from the UCI repository dataset. Finally, the Kernel Extreme Learning Machine (KELM) classifier validates the selected features. The performance of the proposed model is tested by 20 UCI benchmark datasets and the outcomes are compared with other models. Through experimental outcomes, it has been revealed that the suggested model produces the best accuracy and also selects fewer numbers of features that are more relevant from the dataset. In terms of other performance measures, the proposed model attains better outcomes for the majority of the datasets.

关键词： Feature selection flamingo search algorithm generalized ring crossover Kernel extreme learning machine quasi oppositional learning

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Disease classification using neighbourhood centroid opposition based multi-objective flamingo search algorithm based feature selection approach

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第4期45卷 6911-6922页

作者： Durgam, Revathi Devarakonda, Nagaraju VIT AP Univ Sch Comp Sci & Engn Amaravati Andhra Pradesh India

In machine learning, a crucial task is feature selection in that the computational cost will be increased exponentially with increases in problem complexity. To reduce the dimensionality of medical datasets and reduce the computational cost, multi-objective optimization approaches are mainly utilized by researchers. Similarly, for improving the population diversity of the flamingo search algorithm, the neighbourhood centroid opposition-based learning mutation is employed. In this paper, to improve the classification accuracy, enhance their exploration capability in the search space and reduce the computational cost while increasing the size of dataset, neighbourhood centroid opposition-based learning (NCOBL) is integrated into the multi-objective optimization based flamingo search algorithm (MOFSA). The optimal selected datasets are classified by using the weighted K-Nearest Neighbour classifier. With the use of fifteen benchmark medical datasets, the efficacy of the suggested strategy is assessed in terms of recall, precision, accuracy, running time, F-measure, hamming loss, ranking loss, standard deviation, mean value error, and size of the selected features. Then the performance of the suggested feature selection technique is compared to that of the existing approaches. The suggested method produced a minimum mean value, standard deviation, mean hamming loss, and maximum accuracy of about 99%. The experimental findings demonstrate that the suggested method may enhance classification accuracy and also eliminate redundancy in huge datasets.

关键词： flamingo search algorithm K-Nearest Neighbour feature selection multi-objective optimization disease classification

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Feature Selection with a Binary flamingo search algorithm and a Genetic algorithm

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第17期82卷 26679-26730页

作者： Eluri, Rama Krishna Devarakonda, Nagaraju VIT AP Univ Sch Comp Sci & Engn Amaravati 522237 Andhra Prades India

In data mining, feature selection (FS) has become a significant data pre-processing tool that maximises the model's generalisation and minimises the feature size. Due to the large search area, the classical optimization techniques repeatedly fail to construct global optimization. Several hybrid models integrating various search policies have recently been offered;however, they mostly deal with low dimensional datasets. This paper proposes a hybrid version of binary flamingo search with a genetic algorithm (HBFS-GA) to overcome the FS problem using a wrapper model. A genetic algorithm (GA) and a flamingo search algorithm (FSA) are combined in the proposed work. HBFS-GA executes on continuous search, but the FS is in discrete space. By utilizing transfer functions (TFs), the continuous search has been transformed into a discrete one. To determine the best TF and investigate HBFS-GA, the proposed model used eight distinct TFs. Following that, the performance of the proposed HBFS-GA is evaluated using 18 different UCI datasets and many metrics. The optimal variation is chosen, and the performance of existing wrapper-based and filter-based FS models is investigated. The existing wrapper-based models include BPSO, BGA, BACO, BCS, BGWO, BBAT, BGEO, and BCSO. Some filter-based methods include a gain ratio and correlation bases FS, information gain and relief, respectively. Besides, the proposed HBFS-GA is evaluated using 30 functions from CEC'2019 and CEC'2020 benchmarks. Consequently, the proposed HBFS-GA has accomplished better outcomes than existing models. While analysing the classification accuracy with eighteen datasets, the lung cancer dataset obtains higher accuracy of 99.51% with less computation time of 0.031s.

关键词： Feature Selection Transfer Function Genetic algorithm flamingo search algorithm Classification

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flamingo search algorithm and Its Application to Path Planning Problem 21

Flamingo Search Algorithm and Its Application to Path Planni...

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Proceedings of the 2021 4th International Conference on Artificial Intelligence and Pattern Recognition

作者： Zhiheng Wang Jianhua Liu School of Computing Xi'an University of Posts and Telecommunications China

ISBN: (纸本)9781450384087

After studying the migratory and foraging behavior of flamingos, this paper proposes a new swarm intelligence optimization algorithm: flamingos search algorithm. At the same time, in order to verify the optimization effect and stability of FSA, this paper conducts tests and comparisons on 20 different benchmark functions, and standard deviation of the proposed FSA outperformed the other algorithms with regard to 20 test functions and path planning applications. Finally, this paper applies FSA to the path optimization problem, and the test results show that FSA has good engineering application potential, especially in the solution of path planning problems with good optimization performance. Experimental code for this article can be obtained from this website: https://***/18280426650/FSA.

关键词： Constrained optimization flamingo search algorithm Path planning problem Swarm intelligence optimization algorithm

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Feature selection on real-time application using hybrid of flamingo search algorithm and improved non-dominated sorting genetic algorithm III

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JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE 2024年

作者： Kousalya, C. Gnana Kumar, V. Senthil Diaz, P. M. Jiju, M. Julie Emerald St Josephs Inst Technol Dept ECE Chennai India Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & T Dept Comp Sci & Engn Chennai India Dedicated Juncture Researchers Assoc Dept Mech Engn Kanyakumari India CSI Inst Technol Dept MCA Kanyakumari India

Feature selection plays an essential role in enhancing the efficiency and effectiveness of machine learning models, particularly in the context of large-scale datasets where the dimensionality of features presents significant challenges. This research presents a novel approach to feature selection, a critical aspect of enhancing machine learning model efficiency, particularly in the context of large-scale datasets. By integrating the flamingo search algorithm (FSA), Non-dominated Sorting Genetic algorithm III (NSGA-III), and Regularised Extreme Learning Machine (RELM), the proposed method addresses limitations in existing feature selection and multi-objective optimisation algorithms. Leveraging FSA's emulation of flamingo behaviours, the approach achieves a balance between global exploration and local exploitation, mitigating issues like premature convergence and local optima. Integration with NSGA-III enhances multi-objective optimisation capabilities, maintaining a delicate equilibrium between convergence and diversity. FSA-RELM is employed for accurate feature assessment, given its rapid learning and suitability for large datasets with multiple labels. Experimental evaluations demonstrate the proposed method's superiority in feature selection accuracy, classification performance, and computational efficiency compared to existing approaches. This research contributes to advancing feature selection methodologies, offering a comprehensive solution for high-dimensional datasets in machine learning and data mining applications.

关键词： Regularized extreme learning machine NSGA-III convergence flamingo search algorithm

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Multi-objective materialized view selection using flamingo search optimization algorithm

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SOFTWARE-PRACTICE & EXPERIENCE 2023年第4期53卷 988-1012页

作者： Srinivasarao, Popuri Satish, Aravapalli Rama VIT AP Univ Sch Comp Sci & Engn Amaravati India

A materialized view selection in data warehouse management is important to speed up query processing. The data presented in data warehouses is generally stored as a set of materialized views. The major challenge is determining which views to materialize and satisfy the response time with reduced cost functions. This paper proposed an effective multi objective cost model based flamingo search algorithm for materialized view selection in data warehouse design. The multiple view processing plan structure of the data warehouse describes the search space of problem in order to select the optimal materialized views. The proposed model evaluates a multi-objective optimization problem based on the cost functions resulting from materialization. The multiple objective functions of the proposed model are maintenance costs, current query processing costs, response cost and previous query processing costs. This model selects the top-k views for materialization by satisfying the mentioned multi-objective functions. The experimental results are simulated using the TPC-H dataset. The efficacy of proposed model is measured by comparing the obtained results of the proposed model with various existing approaches.

关键词： data warehousing flamingo search algorithm materialized view selection query processing

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A secure authentication and optimization based device-to-device communication for 5G enabled IoT applications of smart cities

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WIRELESS NETWORKS 2025年第4期31卷 3167-3186页

作者： Rani, Therkkumthala Prakash Maruthu, Ayyadurai Ramakrishnan, Pavithra Guru Jayakumar, Cypto Sri Sairam Engn Coll Dept Informat Technol Chennai 600044 India Rajalakshmi Engn Coll Dept CSE Chennai 602105 Tamil Nadu India SRM Inst Sci & Technol Dept Comp Technol Kattankulathur 603203 Tamil Nadu India SRM Inst Sci & Technol Dept Comp Sci & Engn Ramapuram Campus Chennai 600069 India

Smart cities are designed by an interconnection of multiple Internet of Things devices for gathering data from objects and humans to process essential activities. However, it is a difficult task to handle the huge data inreal-time to propel smart city applications. Device to Device (D2D) communication must support authentication for secure and effective communication. Thus, a protocol namely flamingo search Aquila Optimization KeyGenerator (FSAO_KeyGen) is devised for efficient authentication in D2D devices. Here, D2D communications via Wi-Fi Direct are established using five various phases, such as (1) discovery, (2) Group Owner (GO) negotiation, (3) mutual authentication, (4) key agreement, and (5) address configuration. Initially, in the process of D2D communication, the devices are discovered in a discovery phase. After that, in a GO negotiation phase, the discovered devices negotiate with each other and the device which possesses a greater intent value wins the negotiation and becomes a GO. Thereafter, the devices generate the keys in a key generation phase. Here, the key is generated by using the proposed FSAO_KeyGen, where the proposed FASO_KeyGen is formed by the integration of flamingo search algorithm and Aquila Optimizer for secure authentication in D2D communication. Moreover, the experimental outcome reveals that the FSAO_KeyGen processed efficiently attaining computation time, memory, and response time values of 1.212S, 7.791 MB, and 0.690S.

关键词： flamingo search aquila optimization Group owner Device to device communication flamingo search algorithm Aquila optimizer

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flamingo Jelly Fish search optimization-based routing with deep-learning enabled energy prediction in WSN data communication

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NETWORK-COMPUTATION IN NEURAL SYSTEMS 2024年第1期35卷 73-100页

作者： Subramanian, Dhanabal Subramaniam, Sangeetha Natarajan, Krishnamoorthy Thangavel, Kumaravel Kongunadu Coll Engn & Technol Dept Comp Sci & Engn Namakkal Trichy Main Rd Trichy 621215 India Kongunadu Coll Engn & Technol Dept Informat Technol Trichy Tamil Nadu India Vellore Inst Technol Dept Software Syst & Engn Vellore Tamilnadu India Kongu Engn Coll Dept Comp Sci & Engn Perundurai Tamilnadu India

Nowadays, wireless sensor networks (WSN) have gained huge attention worldwide due to their wide applications in different domains. The limited amount of energy resources is considered as the main limitations of WSN, which generally affect the network life time. Hence, a dynamic clustering and routing model is designed to resolve this issue. In this research work, a deep-learning model is employed for the prediction of energy and an optimization algorithmic technique is designed for the determination of optimal routes. Initially, the dynamic cluster WSN is simulated using energy, mobility, trust, and Link Life Time (LLT) models. The deep neuro-fuzzy network (DNFN) is utilized for the prediction of residual energy of nodes and the cluster workloads are dynamically balanced by the dynamic clustering of data using a fuzzy system. The designed flamingo Jellyfish search Optimization (FJSO) model is used for tuning the weights of the fuzzy system by considering different fitness parameters. Moreover, routing is performed using FJSO model which is used for the identification of optimal path to transmit data. In addition, the experimentation is done using MATLAB tool and the results proved that the designed FJSO model attained maximum of 0.657J energy, a minimum of 0.739 m distance, 0.649 s delay, 0.849 trust, and 0.885 Mbps throughput.

关键词： Deep Neuro-Fuzzy Network jellyfish search optimization flamingo search algorithm fuzzy system

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Hybrid Approach for Cost Efficient Application Placement in Fog-Cloud Computing Environments

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Computers, Materials & Continua 2024年第6期79卷 4127-4148页

作者： Abdulelah Alwabel Chinmaya Kumar Swain Department of Computer Sciences College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Computer Science and Engineering Institute of Management and Information TechnologyCuttackBPUTOdishaIndia

Fog computing has recently developed as a new paradigm with the aim of addressing time-sensitive applications better than with cloud computing by placing and processing tasks in close proximity to the data ***,the majority of the fog nodes in this environment are geographically scattered with resources that are limited in terms of capabilities compared to cloud nodes,thus making the application placement problem more complex than that in cloud *** approach for cost-efficient application placement in fog-cloud computing environments that combines the benefits of both fog and cloud computing to optimize the placement of applications and services while minimizing *** approach is particularly relevant in scenarios where latency,resource constraints,and cost considerations are crucial factors for the deployment of *** this study,we propose a hybrid approach that combines a genetic algorithm(GA)with the flamingo search algorithm(FSA)to place application modules while minimizing *** consider four cost-types for application deployment:Computation,communication,energy consumption,and *** proposed hybrid approach is called GA-FSA and is designed to place the application modules considering the deadline of the application and deploy them appropriately to fog or cloud nodes to curtail the overall cost of the *** extensive simulation is conducted to assess the performance of the proposed approach compared to other state-of-the-art *** results demonstrate that GA-FSA approach is superior to the other approaches with respect to task guarantee ratio(TGR)and total cost.

关键词： Placement mechanism application module placement fog computing cloud computing genetic algorithm flamingo search algorithm

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Energy efficient congestion control in wireless sensor networks using meta-heuristic algorithms

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INTERNATIONAL JOURNAL OF COMMUNICATION NETWORKS AND DISTRIBUTED SYSTEMS 2023年第4期29卷 448-473页

作者： Ali, Abdul Vadivel, M. Sathyabama Inst Sci & Technol Dept Comp Sci & Engn Chennai India Vidya Jyothi Inst Technol Elect & Commun Engn Hyderabad India

A wireless sensor network (WSN) is an infrastructure-less wireless network that is used to monitor the physical or environmental conditions. In WSN, energy is the main constraint in congestion control that decreases the network's lifetime. This paper's main goal is to propose a novel clustering-based routing protocol to mitigate the congestion issue in the network and to maximise the lifespan of WSN. To control the congestion problem in WSN, an energy-efficient ultra scalable ensemble clustering is introduced in this paper. In addition, the flamingo search algorithm-based fuzzy inference system is applied for cluster head (CH) selection. Rat swarm optimisation is used to select the route between CH and BS. The experimental results demonstrated that the proposed methodology gives better results than the existing techniques and it enhances the energy efficiency of WSN. The residual energy of the proposed approach (5 J at 2,000 rounds) is greater than the existing methods.

关键词： wireless sensor network WSN ultra-scalable ensemble clustering flamingo search algorithm FSA rat swarm optimisation RSO cluster head selection

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