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A Novel lyrebird optimization algorithm for Enhanced Generation Rate-Constrained Load Frequency Control in Multi-Area Power Systems with Proportional Integral Derivative Controllers

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PROCESSES 2025年第4期13卷 949-949页

作者： El-Rifaie, Ali M. Amer Univ Middle East Coll Engn & Technol Egaila 54200 Kuwait

This study develops a novel lyrebird optimization algorithm (LOA), a technique inspired by the wild behavioral strategies of lyrebirds in response to potential threats. In a two-area interconnected power system that includes non-reheat thermal stations, this algorithm is applied to handle load frequency control (LFC) by optimizing the parameters of a Proportional-Integral-Derivative controller with a filter (PIDn). This study incorporates generation rate constraints (GRCs). The efficiency of the provided LOA-PIDn is evaluated through simulations under various disturbance scenarios and is compared against other well-established optimization techniques, including the Ziegler-Nichols (ZN), genetic algorithm (GA), Bacteria Foraging optimization algorithm (BFOA), Firefly Approach (FA), hybridized FA and pattern search (hFA-PS), self-adaptive multi-population elitist Jaya (SAMPE-Jaya)-based PI/PID controllers, and Teaching-Learning-Based Optimizer (TLBO) IDD/PIDD controllers. The results demonstrate the LOA's ability to minimize the integral of time multiplied by absolute error (ITAE) and achieve significantly lower settling times for the two-area frequencies and transferred power variances in comparison with other methods. The comprehensive comparison and the inclusion of real-world constraints validate the LOA as a robust and effective tool for addressing complex optimization challenges in modern power systems.

关键词： lyrebird optimization algorithm proportional-integral-derivative controller load frequency control generation rate constraint

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Hybrid lyrebird Red Panda optimization_Shepard Convolutional Neural Network for recognition of speech emotion in audio signals

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NEUROCOMPUTING 2025年 625卷

作者： Kanimozhi, N. Priya, R. Devi Kongu Engn Coll Dept Informat Technol Erode 638060 Tamil Nadu India KPR Inst Engn & Technol Dept Comp Sci & Engn Coimbatore 641407 Tamil Nadu India

Speech serves as the primary mode of human communication, where semantic meaning is conveyed through the combination and arrangement of words. Recent research in Speech Emotion Recognition (SER) has assisted in maintaining and improving social relationships and behaviors among individuals. In recent times, several advancements have been attained in SER systems due to the incorporation of Deep learning (DL) models. However, the conventional techniques often require large, well-annotated datasets for effective training, which was resource-intensive to collect and label. Moreover, models may struggle to generalize across diverse speakers, emotional expressions, and recording conditions, potentially limiting their real-world applicability. Therefore, this paper presents a lyrebird Red Panda optimization _Shepard Convolutional Neural Network (LRPO_ShCNN) for SER. Initially, the input speech signal is preprocessed by using Adaptive Gaussian filter. After that, the significant features from the preprocessed image are extracted. Further, data augmentation process is carried out and it generates new data points from existing data. After that, feature selection is done with LRPO. Finally, SER is accomplished by utilizing ShCNN, where the emotions are classified. Moreover, the hyperparameters of ShCNN are tuned with LRPO, which is developed by the integration of lyrebird optimization algorithm (LOA) and Red Panda optimization (RPO). The evaluation results shows that the LRPO_ShCNN obtained Accuracy, Positive Predictive Value (PPV), Negative Predictive Value (NPV), True Positive Rate (TPR), and True Negative Rate (TNR) as 91.092%, 90.552%, 90.876%, 91.230%, and 91.818% respectively.

关键词： Deep Learning Speech Emotion Recognition Red Panda optimization Shepard Convolutional Neural Network lyrebird optimization algorithm

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Flamingo lyrebird optimization-Based Holistic Approach for Improving RFID-WSN Integrated Network Lifetime

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2025年第6期38卷

作者： Rajesh, V. Rahuman, A. Kaleel SBM Coll Engn & Technol Dept Elect & Commun Engn Dindigul 624622 Tamil Nadu India PSNA Coll Engn & Technol Dept Elect & Commun Engn Dindigul Tamil Nadu India

Wireless sensor networks (WSNs) are considered a key foundation for high-level Internet of Things (IoT) practices. Moreover, WSNs depend on transmission for data transfer among sink modules and sensor nodes or intermediate points in the system. However, in WSN, there are several interruptions during the transmission of data. Further, storage space, network bandwidth, and processing power are limited, and hence, it is significant to enhance data distribution to improve network performance. Therefore, this paper devised a new approach known as the Flamingo lyrebird optimization algorithm (FLOA) to improve radio frequency identification (RFID)-WSN integrated network lifetime. Firstly, the network topology of WSN-RFID is simulated, and then cluster head (CH) selection is performed by FLOA in terms of multiobjective fitness, namely, energy, network lifetime, and inter- and intracluster distance. Here, FLOA is formed by integrating Flamingo Search optimization (FSA) and lyrebird optimization algorithm (LOA). After this, the energy-efficient multipath routing is performed by utilizing FLOA, where link life time (LLT) and energy are predicted using a gated recurrent unit (GRU). Furthermore, FLOA attained the maximum performance with network lifetime and energy of 820.11, 0.923 J, and minimum delay of 0.211 s.

关键词： Flamingo Search optimization gated recurrent unit lyrebird optimization algorithm multipath routing wireless sensor networks

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LDPC code design with 5G based on hybrid lyre puffer fish optimization

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COMPUTERS & ELECTRICAL ENGINEERING 2025年 124卷

作者： Shah, Pradeep M. Vyavahare, Prakash Jain, Anjana CVM Univ Dept Elect & Commun GCET VV Nagar Gujarat India RGPV SGSITS Indore Dept Elect & Telecom Engn Airport Rd Bhopal 462033 Madhya Pradesh India RGPV Dept Elect & Telecom Engn SGSITS Indore Airport Rd Bhopal 462033 Madhya Pradesh India RGPV SGSITS Indore Airport Rd Bhopal 462033 Madhya Pradesh India

A specific linear block code class with a Low-Density Parity-Check (LDPC) matrix is known as the LDPC code. LDPC codes are widely used in error correction due to their near-optimal and fast performance. In this research, an effective LDPC code design method called Lyre Pufferfish optimization (LPFO) is designed. Primarily, the LDPC with the 5th Generation (5G) system model is considered and the LDPC code is created. The binary data is encoded using the LDPC encoder, and parity checks are performed using the LDPC decoder. Here, the LPFO system is processed by the complete parity-check matrix (i.e., H-matrix), which is accomplished based on the proposed hybrid LPFO. The proposed LPFO is devised by the amalgamation of the lyrebird optimization algorithm (LOA) and Pufferfish optimization algorithm (POA) with Block Error Rate (BLER) for the fitness function. Additionally, LPFO recorded the least BLER, Bit Error Rate (BER), and decoding complexity about 1.00E-09, 5.00E-10 and 0.752 respectively.

关键词： Low-density parity-check lyrebird optimization algorithm Bit error rate Pufferfish optimization algorithm Block error rate

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Automatic near-optimal generation of software test data for critical paths

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JOURNAL OF SUPERCOMPUTING 2025年第5期81卷 1-34页

作者： Abdi, Mina Mardukhi, Farhad Razi Univ Dept Comp Engn & Informat Technol Kermanshah Iran

Software testing is a critical phase in software development that ensures the identification and resolution of faults. Automating test data generation can significantly reduce costs and improve efficiency. While genetic algorithms and particle swarm optimization (PSO) are widely used in this domain, recent research has introduced alternative approaches with enhanced performance. This paper presents an improved version of the Improved lyrebird optimization algorithm (ILOA) for automated test data generation, aiming to enhance its exploration and exploitation capabilities. A novel fitness function is introduced to optimize the search process and improve test case generation. The proposed method is evaluated on multiple benchmark programs, and the experimental results demonstrate that ILOA outperforms existing state-of-the-art algorithms in terms of the number of fitness evaluations, execution time, test generation efficiency, coverage rate, and success rate. These findings highlight the effectiveness of ILOA in improving test data generation for software testing.

关键词： Software testing Test data generation Search algorithm Path testing lyrebird optimization algorithm

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Fusing nature inspired fuzzy neural networks for hypervisor intrusion detection

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International Journal of Information Technology (Singapore) 2024年第5期16卷 2915-2924页

作者： Ashwitha, A. Banu, M. Sheerin Kaur, Puneet Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Information Technology R.M.K. Engineering College Tamil Nadu Kavaraipettai India Electrical and Electronics Engineering Department UIET Panjab University Chandigarh India

In the realm of cloud computing, hypervisors play a pivotal role as virtualization layers, managing and orchestrating virtual machines on physical host systems. The data generated within hypervisor environments holds the key to the operational integrity of cloud infrastructures. However, this critical data is increasingly susceptible to security threats, with intrusion posing a substantial risk. Intrusion within hypervisor networks can lead to severe consequences, ranging from compromised data integrity and confidentiality to potential disruptions in service availability. Traditional security measures often fall short in addressing the dynamic and sophisticated nature of these intrusions, necessitating advanced methodologies for robust detection and response. This paper addresses the intricate challenge of hypervisor intrusion by introducing Nature Inspired Fuzzy Neural Networks (NIFNN) as an innovative model. By synergizing the strengths of fuzzy logic and neural networks, the proposed NIFNN model enhances the precision and efficiency of hypervisor intrusion detection. The process begins with pre-processing of hypervisor data to ensure quality and reliability. The NIFNN classifier then analyses and classifies this pre-processed data to detect and identify malicious activities within hypervisor networks. Notably, we introduce the Nature Inspired lyrebird optimization algorithm (NILOA) to optimize the Fuzzy Neural Network model, further elevating the effectiveness and adaptability of our intrusion detection system. The effectiveness of the NIFNN model is evaluated by comparing it with various existing techniques based on metrics such as accuracy, recall, precision, F-measure, specificity, false positive rate (FPR), and false negative rate (FNR). The experimental findings demonstrate that the NIFNN model outperforms the existing approaches across these metrics. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： Cloud computing Fuzzy neural network Hypervisor IDS lyrebird optimization algorithm Malicious data NIFNN

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Resource Utilization Based on Hybrid WOA-LOA optimization with Credit Based Resource Aware Load Balancing and Scheduling algorithm for Cloud Computing

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JOURNAL OF GRID COMPUTING 2024年第3期22卷 61-61页

作者： Narwal, Abhikriti Govt NCT Delhi Dept Comp Sci Trained Grad Teacher Directorate Educ Delhi India

In a cloud computing environment, tasks are divided among virtual machines (VMs) with different start times, duration and execution periods. Thus, distributing these loads among the virtual machines is crucial, in order to maximize resource utilization and enhance system performance, load balancing must be implemented that ensures balance across all virtual machines (VMs). In the proposed framework, a credit-based resource-aware load balancing scheduling algorithm (HO-CB-RALB-SA) was created using a hybrid Walrus optimization algorithm (WOA) and lyrebird optimization algorithm (LOA) for cloud computing. The proposed model is developed by jointly performing both load balancing and task scheduling. This article improves the credit-based load-balancing ideas by integrating a resource-aware strategy and scheduling algorithm. It maintains a balanced system load by evaluating the load as well as processing capacity of every VM through the use of a resource-aware load balancing algorithm. This method functions primarily on two stages which include scheduling dependent on the VM's processing power. By employing supply and demand criteria to determine which VM has the least amount of load to map jobs or redistribute jobs from overloaded to underloaded VM. For efficient resource management and equitable task distribution among VM, the load balancing method makes use of a resource-aware optimization algorithm. After that, the credit-based scheduling algorithm weights the tasks and applies intelligent resource mapping that considers the computational capacity and demand of each resource. The FILL and SPILL functions in Resource Aware and Load utilize the hybrid optimization algorithm to facilitate this mapping. The user tasks are scheduled in a queued based on the length of the task using the FILL and SPILL scheduler algorithm. This algorithm functions with the assistance of the PEFT approach. The optimal threshold values for each VM are selected by evaluating the task based on

关键词： HO-CB-RALB-SA Cloud computing Virtual machines Walrus optimization algorithm lyrebird optimization algorithm Load balancing

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Improved lyrebird optimization for multi microgrid sectionalizing and cost efficient scheduling of distributed generation

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Scientific Reports 2025年第1期15卷 1-28页

作者： Nagarajan, Karthik Rajagopalan, Arul Bajaj, Mohit Raju, Valliappan Blazek, Vojtech Prokop, Lukas Department of Electrical and Electronics Engg Hindustan Institute of Technology and Science Tamil Nadu Chennai India Centre for Smart Grid Technologies School of Electrical Engineering Vellore Institute of Technology Tamil Nadu Chennai 600 127 India Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan Director of Research Perdana University Kuala Lumpur Malaysia ENET Centre CEET VSB-Technical University of Ostrava Ostrava 708 00 Czech Republic

The rising energy demand, substantial transmission and distribution losses, and inconsistent power quality in remote regions highlight the urgent need for innovative solutions to ensure a stable electricity supply. Microgrids (MGs), integrated with distributed generation (DG), offer a promising approach to address these challenges by enabling localized power generation, improved grid flexibility, and enhanced reliability. This paper introduces the Improved lyrebird optimization algorithm (ILOA) for optimal sectionalizing and scheduling of multi-microgrid systems, aiming to minimize generation costs and active power losses while ensuring system reliability. To enhance search efficiency, ILOA incorporates the Levy Flight technique for local search, which introduces adaptive step sizes with long-distance jumps, improving the exploration-exploitation balance. Unlike conventional local search strategies that rely on fixed step sizes, Levy Flight prevents premature convergence by allowing the algorithm to escape local optima and explore the solution space more effectively. Additionally, a chaotic sine map is integrated to enhance global search capability, ensuring better diversity and superior optimization performance compared to traditional algorithms. Simulation studies are conducted on a modified 33-bus distribution system segmented into three independent microgrids. The algorithm is evaluated under single-objective scenarios (cost and loss minimization) and a multi-objective optimization framework combining both objectives. In single-objective optimization, ILOA achieves a generation cost of $19,254.64/hr with 0.7118 kW of power loss, demonstrating marginal improvements over the standard lyrebird optimization algorithm and significant gains over Genetic algorithm (GA) and Jaya algorithm (JAYA). In multi-objective optimization, ILOA surpasses competing methods by achieving a generation cost of $89,792.18/hr and 10.26 kW of power loss. The optimization results indicate

关键词： Active power loss Distributed generators Energy index of reliability (EIR) Generation cost minimization lyrebird optimization algorithm Multi-microgrids Multi-objective optimization Optimal scheduling

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