Cuckoo Search Optimization Algorithm Research Articles

Optimized underwater light attenuation prior-based depth estimation and adaptive feature fusion CNN for underwater image and video enhancement

Underwater images and videos play an essential communication tool in exploring ocean resources and understanding underwater scene perception. The underwater imaging environment and the complex lighting conditions degrade the quality of the images and make them low contrast, color deviation, and blurring. These degraded images lack effective target recognition information, significantly impacting the performance of underwater application systems. To solve the above issues, a new module for underwater video as well as image enhancement is proposed. The input video is given to the frame extraction phase for converting the video into the frame. Thereafter, the blur detection and determination are done using Laplacian's variance technique for determining blurred images. The depth estimation is done using Underwater Light Attenuation Prior (ULAP), wherein coefficients are determined using the proposed Competitive Multi-Verse bird swarm Optimization (CMVBSO). The CMVBSO is combined by integrating a competitive multiverse optimizer (CMVO) and a Bird Swarm Algorithm (BSA). The blurred value, input video frame and depth estimated output are given to adaptive feature fusion CNN in which training is done utilizing Manta-Ray Foraging Lion Optimization (MRLLO). The pixel enhancement is carried out using a Type II Fuzzy system and Cuckoo Search optimization algorithm filter (T2FCS) filter. The proposed CMVBSO_ULAP provided better performance with a Peak signal to noise ratio (PSNR) of 35.037 dB, Mean square error (MSE) of 3.684, Structural similarity index (SSIM) of 0.911, underwater image quality measure (UIQM) of 5.266 and Underwater Color Image Quality Evaluation (UCIQE) of 0.818 respectively.

Optimizing breast cancer diagnosis: Harnessing the power of nature-inspired metaheuristics for feature selection with soft voting classifiers

Breast cancer is a widespread and serious condition that poses a significant threat to women's health globally, contributing significantly to mortality rates. Machine learning tools play a critical role in both the effective management and early detection of this disease. Feature selection (FS) methods are essential for identifying the most impactful features to improve breast cancer diagnosis. These methods reduce data dimensionality, eliminate irrelevant information, enhance learning accuracy, and improve the comprehensibility of results. However, the increasing complexity and dimensionality of cancer data pose substantial challenges to many existing FS methods, thereby reducing their efficiency and effectiveness. To overcome these challenges, numerous studies have demonstrated the success of nature-inspired optimization (NIO) algorithms across various domains. These algorithms excel in mimicking natural processes and efficiently solving complex optimization problems. Building on these advancements, we propose an innovative approach that combines powerful feature selection methods based on NIO techniques with a soft voting classifier. The NIO techniques employed include the Genetic Algorithm, Cuckoo Search, Salp Swarm, Jaya, Flower Pollination, Whale Optimization, Sine Cosine, Harris Hawks, and Grey Wolf Optimization algorithms. The Soft Voting Classifier integrates various machine learning models, including Support Vector Machines, Gaussian Naive Bayes, Logistic Regression, Decision Tree, and Gradient Boosting. These are used to improve the effectiveness and accuracy of breast cancer diagnosis. The proposed approach has been empirically evaluated using a variety of evaluation measures, such as F1 score, precision, recall, accuracy and Area Under the Curve (AUC), for performance comparison with individual machine learning techniques. The results demonstrate that the soft-voting ensemble technique, particularly when combined with feature selection based on the Jaya algorithm, outperforms all individual classifiers on the breast cancer dataset. It achieves the highest scores for accuracy, precision, recall, F1-score, and AUC, with values of 99.6 %, 99.21 %, 100 %, 99.6 %, and 99.6 %, respectively. Following closely, both the Genetic Algorithm and Salp Swarm feature selection demonstrate strong performance, with scores of 99.5 %, 100 %, 99 %, 99.5 %, and 99.5 %, respectively. Furthermore, the approach's effectiveness in identifying the minimal set of relevant features has been evaluated. Given the critical importance of selecting relevant features in complex cancer datasets, the proposed method proves to be a valuable tool for achieving early detection and improved interpretability. This method can also support healthcare professionals in making well-informed decisions regarding cancer diagnosis and treatment strategies.

Comparative analysis of optimization algorithm-based training algorithms for a few shot continual learning

Incremental learning relies on the availability of ample training data for novel classes, a requirement that is often unfeasible in various application scenarios, particularly when new classes are rare groups that are pricey or challenging to attain. The main focus of incremental learning is on the tricky task of continuously learning to classify new classes in incoming data with no erasing knowledge of old classes. The research intends to develop a comparative analysis of optimization algorithms in training few-shot continual learning models to conquer catastrophic forgetting. The presented mechanism integrates various steps: pre-processing and classification. Images are initially pre-processed through contrast enhancement to elevate their quality. Pre-processed outputs are then classified by employing Continually Evolved Classifiers, generated to address a matter of catastrophic forgetting. Furthermore, to further enhance performance, Serial Exponential Sand Cat Swarm optimization algorithm (SE-SCSO) is employed and compared against ten other algorithms, containing Grey Wolf Optimization (GWO) algorithm, Moth flame optimization (MFO), cuckoo Search Optimization Algorithm (CSOA), Elephant Search Algorithm (ESA), Whale Optimization Algorithm (WOA), Artificial Algae Algorithm (AAA), Cat Swarm Optimization (CSO), Fish Swarm Algorithm (FSA), Genetic Bee Colony (GBC) Algorithm, and Particle swarm optimization (PSO). From the experiment results, SE-SCSO had attained the maximum performance with an accuracy of 89.6%, specificity of 86%, precision of 83%, recall of 92.3% and f-measure of 87.4%.

Multi-objective discrete Cuckoo search algorithm for optimization of bag-of-tasks scheduling in fog computing environment

Internet of Things (IoT) applications are mostly time-sensitive and require reliable executions. The majority of IoT applications experience low quality of service (QoS) because of the intrinsic delay in utilizing the far-distant cloud infrastructures. To experience better QoS, fog computing as a complementary facility for cloud infrastructure is extended near the IoT devices. The bag of tasks (BoTs) IoT applications include a number of independent tasks each of which needs a certain amount of processing time so it is favorable to experience the lowest total execution time, makespan, for all applications. In addition, reliable execution guarantees the business continuity of IoT applications. Therefore, this paper presents a fog computing scheduler for the execution of BoT IoT applications in the fog platforms to minimize makespan and maximize the reliability execution at the same time. To this end, a new reliability model is presented. Then, the BoTs scheduling issue is formulated into a multi-objective discrete optimization problem with makespan and reliability optimality viewpoints. To solve this combinatorial problem, a multi-objective discrete cuckoo search algorithm (MoDCSA) is extended. The proposed MoDCSA optimization algorithm takes the benefit of several discrete operators each of which is smartly invoked. The quality of gained results from simulations of extensive scenarios verifies the effectiveness of the proposed MoDCSA scheduling algorithm which has 2.27%, 3.89%, 5.45%, 5.40%, and 4.55% improvement in terms of makespan and 1.99%, 5.11%, 6.39%, 7.95%, and 7.78% improvement in terms of reliability against MoBLA, MoGA, MoGWO, MoSA, and MoGSA comparative algorithms respectively.

Self-driven dual reinforcement model with meta heuristic framework to conquer the iot based clustering to enhance agriculture production

In agriculture, irrigation is essential for providing water to crops based on the type of soil they are grown in. To achieve success in farming, it is important to evaluate soil fertility, temperature, rainfall and set irrigation schedules. In this work, to enhance agricultural production, an IoT-based hydration system that utilizes soil moisture and humidity sensors to keep an eye on soil conditions and water crops precisely is developed. This system effectively manages water usage in farming, resulting in an efficient conservation of water resources. The proposed model is categorized into four main phases: (a) Pre-processing; (b) Clustering; (c) Feature extraction; (d) Classification. Initially, the collected raw data is pre-processed via a data-cleaning approach. From the pre-processed data, DB scan is used to cluster the data. From the clustered data, the important feature is extracted by using statistical features like mean, standard deviation, kurtosis, and skewness. Subsequently, from the extracted features, the most optimal features are selected via a new hybrid meta-heuristic optimization model referred to as Cuckoo search-based Levy adolescent identity search algorithm (CLAIS). The projected CLAIS model is the conceptual amalgamation of the standard Cuckoo search optimization (CSO) and adolescent identity search algorithm (AISA), respectively. CLAIS-based deep Q network (CLAIS-DQN) classifier is used to classify the optimal features. DQN is an efficient solution to offload the request optimally which improves the overall performance of the network. The proposed model is implemented using the PYTHON platform. The proposed model has recorded the highest detection accuracy as 96%.

Computational investigations on anti-cancer drug solubility in supercritical solvent for efficient cancer therapy

This study focuses on the computational analysis of solubility of an anti-cancer drug namely Temozolomide in SC-CO2, utilizing a dataset consisting of pressure and temperature as input parameters and Temozolomide solubility as the output. Temperature range between 308–338 K, and pressure between 120–400 bar were taken into account for building the models. Three regression models—K Nearest Neighbors (KNN) regression, Huber regression, and Support Vector Machine (SVM) regression—were employed to predict solubility, with hyper-parameter tuning accomplished using the Cuckoo Search (CS) optimization algorithm. The updated results underscored the efficacy of these models in predicting Temozolomide solubility. Notably, the SVM regression model displayed impressive performance, yielding an R-squared score of approximately 0.976, indicating a high degree of accuracy in predicting solubility. The RMSE for SVM regression was approximately 0.000152, signifying minimal prediction error, and the Mean Absolute Error (MAE) was approximately 0.000124.

Power quality improvement of PWM AC Chopper fed capacitor run induction motor using BFO, PSO and CSO Algorithm

This paper exhibits performance enhancement of PWM AC chopper fed capacitor run induction motor by using optimization algorithm. The PWM AC chopper utilizes enhanced asymmetrical pulse width modulation technique by incorporating four pulses in quarter cycle. The switching instants are obtained optimally by bacteria foraging optimization, particle swarm optimization and cuckoo search optimization algorithm. The power quality parameters considered in this paper are total harmonic distortion of current, total harmonic distortion of voltage, power factor and efficiency. The simulation result shows that proposed algorithm shows better performance compared to other optimization techniques.

Statistical pathways to low-carbon cities: Analyzing renewable integration, energy-efficient design, and job creation

To address the existing gap in understanding the intricate dynamics of resource management in Cloud Fog Computing (CFC)-driven Smart Grids (SG), this study introduces an innovative adaptive framework integrating Genetic Optimization Algorithm (GOA) and Cuckoo Search Optimization Algorithm (CSOA), aiming to optimize response times and enhance overall efficiency. In the domain of contemporary energy grids, the implementation of intelligent grids (IG) has ushered in a new era of dependable, streamlined, eco-friendly, and economically viable electric services. These grids constitute a fundamental facet of present-day energy resources. Moreover, the advent of cloud and mist computing (CMC) provides on-demand access to computational resources. Overcoming associated challenges, CMC not only delivers advantages like cost-effectiveness, energy conservation, scalability, flexibility, and adaptability but also presents an innovative model for resource management in IG. This study introduces a CMC-driven strategy for managing resources in IG by digitally replicating key grid components. The proposed framework aims to furnish diverse computational services for resource management within IG through a hierarchical arrangement of CMCs. Additionally, this investigation scrutinizes and simulates two optimization approaches: the cuckoo search and grasshopper optimization algorithms. These algorithms operate on an adaptive machine learning mechanism designed to balance the load, and the study meticulously compares their efficacy. These algorithms operate on an adaptive machine learning mechanism designed to balance the load, and the study meticulously compares their efficacy in the context of Renewable Integration, Energy-Efficient Design, and Job Creation.

Using the Cuckoo Optimization to Initialize the Harmony Memory in Harmony Search Algorithm to Find New Hybrid (CSHS) Algorithm

The current paper seeks to present a hybrid harmony search algorithm, using CS and HS. The proposed algorithm takes advantage of using a method to initialize the harmony memory (HM) with the help of the cuckoo search optimization algorithm. The HS algorithm is inspired by the method used by musicians to create and enhance harmony in music. It follows 3 main principles of pitch adjustment (PA), HM consideration (HMC), and random selection (RS). The performance of HS can be affected by its poor accuracy in optimization and speed of convergence, 2 main issues with it. However, to improve the HS algorithm, Cuckoo search can be used as it can do a local search using one parameter only, other than the size of the population, making it work more efficiently. This current method has been tested for its validity and efficiency in performance by being implemented on many international standard optimization problems. The results confirm the fact that the performance of the currently proposed algorithm is better and more efficient in finding solutions compared to HS and other algorithms.

Filtered Ofdm System Model for Papr Reduction in the Growth Of 5g

A proposed waveform for fifth generation (5G) communications is the filtered orthogonal frequency division multiplexing (F-OFDM) system. Filtering-based waveform frameworks distinguish themselves by suppressing out-of-band emission and asynchronous transmission. Meanwhile, the high Peak-To-Average Power Ratio (PAPR) remains a barrier to new waveform possibilities. Partials transmit sequence (PTS) is a successful strategy for reducing high PAPR in multicarrier networks. This work presents a multicarrier in OFDM and UFMC based on the Filter Bank Multicarrier (FBMC) waveform for communication systems. Furthermore, FBMC waveforms have limited orthogonality between subcarriers, which can mitigate Doppler and multipath effects. This FBMC approach uses the cosine law to filter the traffic signal, and the Welch algorithm is offered for segmentation, which lowers noise in estimated power spectra. As a result, a hybrid Selective Mapping (SLM) with Cuckoo Search and Ant Lion Optimization (CS-ALO) algorithms is suggested using the FBMC framework to reduce PAPR in the OFDM system. The proposed work is implemented, and detailed experimental research is carried out to show the increased performance in 5G networks. Based on a fair comparison, the F-OFDM scheme outperforms previous approaches in terms of BER and multicarrier system capacity. Simulation results show that the proposed waveform design and processing method outperform standard OFDM and F-OFDM methods in PAPR reduction and communication integrated systems.

Hybrid of Meta-Heuristic Techniques Based on Cuckoo Search and Particle Swarm Optimizations for Solar PV Systems Subjected to Partially Shaded Conditions

Solar energy has a significant role in meeting rising energy demand while reducing environmental impact. Solar radiation and temperature are important factors on which PV energy production depends, but its optimal operation point is influenced by variations in the aforementioned environmental factors. The nonlinear behavior of the solar system and the variable nature of environmental conditions make determining the optimal operation point difficult. To overcome these difficulties, maximum power point tracking (MPPT) finding techniques are used to extract the optimal power from the photovoltaic energy system. The behavior of MPPT varies for different weather conditions, such as partial shading conditions (PSC), and uniform irradiance conditions. Conventional techniques are simple, quick, and efficient for tracing the MPP quickly, but they are limited to uniform weather conditions. In addition, these techniques don't achieve the Global Maxima (GM) and mostly stay stuck at the Local Maxima (LM). The Meta-Heuristic techniques aid in finding the GM, but their primary disadvantage is that they take a longer time to trace the Global Maxima. This study addresses the problem by combining Cuckoo Search (CS) and Particle Swarm Optimization (PSO) algorithms, leading to a hybrid (CSPSO) technique to extract the global maximum (GM). To verify the effectiveness of the suggested technique, its performance is examined under three different irradiance patterns for different PV array configurations (such as 3S and 4S3P) through MATLAB simulation. The outcomes of CSPSO are compared with the prior well-known Meta-Heuristic techniques such as Cuckoo Search (CS), Particle Swarm Optimization (PSO), and Crow Search Algorithm (CSA). The results show the suggested technique excels over other techniques in terms of accuracy, tracking efficiency, and tracking speed. The suggested technique is capable of tracking GMPP with an average efficiency of 99.925% and an average tracking time of 0.13 s in all shading patterns studied.

Geometrical and material optimization of the functionally graded doubly-curved shell by metaheuristic optimization algorithms

This paper presents the simultaneous geometrical and material optimization of functionally graded doubly-curved shells under static load and free vibration. The weak form of governing equations is derived from third-order shear and normal deformation theory. Bending and free vibration analyses were conducted using the finite element method. The functionally graded material (FGM) is composed of metal and ceramic, and Voigt’s rule of mixtures is applied to capture the characteristics of FGMs throughout the shell thickness direction. In this regard, two multi-objective optimization algorithms (i.e., multi-objective particle swarm optimization and multi-objective cuckoo search optimization algorithms are employed. The objective functions combine minimizing the maximum normalized displacement and mass and maximizing the first dimensionless frequency. The results show that where minimizing the mass of the FG shell structure was considered an objective function, the value of the power index is equal to zero for almost all studied cases.

CSADF: ingesting cuckoo search optimization algorithm enabled with fitness function for effective model transformation pertaining to ADF

CSADF: ingesting cuckoo search optimization algorithm enabled with fitness function for effective model transformation pertaining to ADF

Fuzzy evaluation model for physical education teaching methods in colleges and universities using artificial intelligence.

The evaluation of Physical Education Teaching Methods in Colleges and Universities faces two main challenges: an excess of evaluating elements and a lack of assessment framework. Hence, the research proposes the multi-feature fuzzy evaluation model based on artificial intelligence to streamline the evaluation process and provide an efficient framework for accessing teaching methods. The framework integrates natural/human language using fuzzy instructions considering three evaluation perspectives, including the management stage, instructors, and students and employs the enhanced cuckoo search optimization algorithm. After the teaching expert has determined each parameter's ratings, they are fed into the improved cuckoo search algorithm and solved using an unbiased function to assess the assessment's final result. It incorporates the students' mobility mechanism and movement vector deconstruction designed based on functional criteria. A system for evaluating the quality of instruction has been developed utilizing the proposed model with enhanced cuckoo search optimization. The results indicate that the proposed algorithm has achieved the highest scores across multiple assessment categories, average skill performances of 97.01%, learning progress of 87.36%, physical fitness of 93.49%, participation rate of 95.04%, student satisfaction of 95.49%, and physical education of 96.8% teaching efficiency. The usefulness of the proposed framework in enhancing physical education teaching methods has been demonstrated by comparing the results with traditional methods. It contributes to advancing pedagogical practices in the field.

GMPP Tracking of Solar PV system using Spotted Hyena and Quadratic Approximation based Hybrid Algorithm under Partially shaded conditions

The demand for clean and renewable energy is increasing due to environmental concerns. Photovoltaic (PV) system is very popular from the renewable sources and its usage is increasing. The PV system power transfer efficiency depends on several factors such electrical characteristics of loads, solar irradiation, shading condition and panel temperature. The process of transferring maximum possible power to load from PV system by adjusting the system parameters is called maximum power point tracking (MPPT). Under the partially shaded conditions, multiple power peaks are created in the PV curves and makes the MPPT is a non-convex problem. It is a challenging problem to track the global maximum power point (GMPP) under partially shaded conditions. It is essential to recognize the GMPP and restart the string as early as possible to prevent the physical as well as economical damage. In this paper, a new hybrid algorithm for MPPT is proposed and called as QASHO, a combination of Spotted Hyena optimization (SHO) and Quadrature approximation (QA) technique. The QASHO is used to track the GMPP under different weather conditions such as partial shading. The simulation results are compared with Perturb and Observe (P&O) algorithm and Cuckoo Search optimization (CSO) algorithm and promised for better performance in tracking GMPP. The experimental results are provided to validate the proposed MPPT methodology.

Security and privacy concerns in social networks mathematically modified metaheuristic-based approach

Potential risks and vulnerabilities in Social Networks (SN) that may threaten the confidentiality, accuracy, and accessibility of data provided by users have been identified as safety risks. Unauthorized usage of the system, breaches of data, privacy violations, cyber-attacks, and other malicious behaviors that negatively impact the SN’s reliability and security may be addressed within these. Innovative techniques that use scientifically enhanced metaheuristic algorithms are the subject of the study, which attempts to address evolving issues of security and privacy in SNs. In order to improve the security of online social networking data encryption, researchers recommend implementing the MMCSOA approach, which is the Mathematically Modified Cuckoo Search Optimization Algorithm. An enhanced tuning of elliptic curve parameters can be accomplished by adapting the Cuckoo Search method, which, in consequence, provides an encrypted environment for multi-party computing. Users’ preferences for confidentiality are considered while applying a fuzzy logic (FL) decision matrix to select individuals. Data security is improved, and users receive additional authority over data shared with MMCSOA. Research findings prove that MMCSOA is higher compared to other methods in securing users’ privacy.

Hybrid ML-MDKL feature subset selection and classification technique accompanied with rat swarm optimizer to classify the multidimensional breast cancer mammogram image

Breast cancer is a pathological condition characterized by the abnormal proliferation of cells inside the breast tissue. The biggest challenge at the moment is determining the most optimal subset from large datasets. Numerous methodologies have been presented for the purpose of selecting the most suitable subset from datasets with high dimensions. However, the outcomes have proven to be insufficient in effectively addressing extensive quantities of multidimensional datasets. This publication presents a proposal for an efficient approach of selecting numerous feature subsets and performing classification on Multidimensional Datasets (MDD). In this study, the Moment Invariant Wavelet Feature Extraction (MI-WFE) approach is employed for the purpose of feature extraction. The New Adaptive Hybrid Levy Flight based Cuckoo Search Optimization Algorithm is employed to identify the most pertinent subset of features. This study presents a novel approach, referred to as H-RS-LVCSO, which aims to enhance the local search capability and optimization speed of the algorithm. This is achieved through the hybridization of the Rat Swarm Optimizer with the levy flight based cuckoo search optimization technique. In this study, it is suggested a hybridized approach for classifying breast cancer, utilizing a Multilayer Multiple Deep Kernel Learning (ML-MDKL) Classifier in conjunction with the standard Support Vector Machine (SVM) Classifier. The simulation of the suggested method is conducted using the MATLAB software. The approach under consideration is evaluated in comparison to two pre-existing methods. The suggested method demonstrates an accuracy improvement of 10.58% compared to existing methods. Furthermore, it outperforms these methods by 17.5% in terms of accuracy. The proposed method exhibits a precision improvement of 6.52% and 14.23% compared to the existing methods.

Cuckoo Coupled Improved Grey Wolf Algorithm for PID Parameter Tuning

In today’s automation control systems, the PID controller, as a core technology, is widely used to maintain the system output near the set value. However, in some complex control environments, such as the application of ball screw-driven rotating motors, traditional PID parameter adjustment methods may not meet the requirements of high precision, high performance, and fast response time of the system, making it difficult to ensure the stability and production efficiency of the mechanical system. Therefore, this paper proposes a cuckoo search optimisation coupled with an improved grey wolf optimisation (CSO_IGWO) algorithm to tune PID controller parameters, aiming at resolving the problems of the traditional grey wolf optimisation (GWO) algorithm, such as slow optimisation speed, weak exploitation ability, and ease of falling into a locally optimal solution. First, the tent chaotic mapping method is used to initialise the population instead of using random initialization to enrich the diversity of individuals in the population. Second, the value of the control parameter is adjusted by the nonlinear decline method to balance the exploration and development capacity of the population. Finally, inspired by the cuckoo search optimisation (CSO) algorithm, the Levy flight strategy is introduced to update the position equation so that grey wolf individuals are enabled to make a big jump to expand the search area and not easily fall into local optimisation. To verify the effectiveness of the algorithm, this study first verifies the superiority of the improved algorithm with eight benchmark test functions. Then, comparing this method with the other two improved grey wolf algorithms, it can be seen that this method increases the average and standard deviation by an order of magnitude and effectively improves the global optimal search ability and convergence speed. Finally, in the experimental section, three parameter tuning methods were compared from four aspects: overshoot, steady-state time, rise time, and steady-state error, using the ball screw motor as the control object. In terms of overall dynamic performance, the method proposed in this article is superior to the other three parameter tuning methods.

Ensemble Hybrid Model for COVID-19 Sentiment Analysis with Cuckoo Search Optimization Algorithm

The COVID-19 pandemic has caused anxiety and fear worldwide, affecting people's physical and mental health. This research work proposes a sentiment analysis approach to better understand the public's perception of COVID-19 in India. Two datasets are created by collecting tweets regarding COVID-19 in India. Pre-processing and analysis of datasets are performed by using natural language processing (NLP) techniques. Various features are extracted from collected tweets using three-word embeddings GloVe, fastText, Elmo. The optimal features are selected by cuckoo search optimization algorithm. Finally, the proposed hybrid model of Gated Recurrent Unit (GRU) and Bidirectional Long Short-Term Memory (BiLSTM) is used to categorize the tweets into three sentiment categories. Proposed model achieved 94.44% accuracy, 90.34% precision, 88.53% sensitivity, and 89.53% F1 score. It significantly improved over previous approaches, which achieved 80% accuracy.

Hybrid Encryption based Access Control Approach for Securing Cloud Computing

Cloud computing has become an integral part of modern technological infrastructure, facilitating the storage and processing of vast amounts of data. However, ensuring the security of sensitive information in the cloud remains a persistent challenge. This paper proposes a novel approach to enhance the security of cloud computing through hybrid encryption, leveraging the Whale Optimization Algorithm (WOA) and Cuckoo Search Optimization (CSO) algorithms. Hybrid encryption, combining symmetric and asymmetric cryptographic techniques, is employed to address the limitations of traditional encryption methods in cloud environments. The Whale Optimization Algorithm and Cuckoo Search Optimization are utilized to optimize key generation and management processes, enhancing the overall efficiency and security of the encryption scheme. The Whale Optimization Algorithm, inspired by the social behavior of humpback whales, is employed to optimize the parameters of the encryption algorithm. WOA's exploration and exploitation capabilities are leveraged to find an optimal balance in the encryption process, improving the overall robustness against potential attacks. Complementing WOA, the Cuckoo Search Optimization algorithm is applied to optimize the key distribution and update mechanisms. Modeled after the brood parasitism behavior of cuckoo birds, CSO excels in searching large solution spaces, making it suitable for refining the distribution of encryption keys and ensuring their constant adaptability to dynamic cloud environments.