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EACH-COA: An Energy-Aware Cluster Head Selection for the Internet of Things Using the coati optimization algorithm

INFORMATION 2023年第11期14卷 601页

作者： Somula, Ramasubbareddy Cho, Yongyun Mohanta, Bhabendu Kumar Sunchon Natl Univ Dept Informat & Commun Engn Suncheon Si 57922 South Korea Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Engn Vaddeswaram 522302 Andhra Pradesh India

In recent years, the Internet of Things (IoT) has transformed human life by improving quality of life and revolutionizing all business sectors. The sensor nodes in IoT are interconnected to ensure data transfer to the sink node over the network. Owing to limited battery power, the energy in the nodes is conserved with the help of the clustering technique in IoT. Cluster head (CH) selection is essential for extending network lifetime and throughput in clustering. In recent years, many existing optimization algorithms have been adapted to select the optimal CH to improve energy usage in network nodes. Hence, improper CH selection approaches require more extended convergence and drain sensor batteries quickly. To solve this problem, this paper proposed a coati optimization algorithm (EACH-COA) to improve network longevity and throughput by evaluating the fitness function over the residual energy (RER) and distance constraints. The proposed EACH-COA simulation was conducted in MATLAB 2019a. The potency of the EACH-COA approach was compared with those of the energy-efficient rabbit optimization algorithm (EECHS-ARO), improved sparrow optimization technique (EECHS-ISSADE), and hybrid sea lion algorithm (PDU-SLno). The proposed EACH-COA improved the network lifetime by 8-15% and throughput by 5-10%.

关键词： Internet of Things (IoT) cluster head optimization technique network lifetime coati optimization algorithm

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Path planning and engineering problems of 3D UAV based on adaptive coati optimization algorithm

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SCIENTIFIC REPORTS 2024年第1期14卷 1-53页

作者： Jia, Chuan He, Ling Liu, Dan Fu, Shengwei Guizhou Univ Key Lab Adv Mfg Technol Minist Educ Guiyang 550025 Guizhou Peoples R China

In response to the challenges faced by the coati optimization algorithm (COA), including imbalance between exploration and exploitation, slow convergence speed, susceptibility to local optima, and low convergence accuracy, this paper introduces an enhanced variant termed the Adaptive coati optimization algorithm (ACOA). ACOA achieves a balanced exploration-exploitation trade-off through refined exploration strategies and developmental methodologies. It integrates chaos mapping to enhance randomness and global search capabilities and incorporates a dynamic antagonistic learning approach employing random protons to mitigate premature convergence, thereby enhancing algorithmic robustness. Additionally, to prevent entrapment in local optima, ACOA introduces an Adaptive Levy Flight strategy to maintain population diversity, thereby improving convergence accuracy. Furthermore, underperforming individuals are eliminated using a cosine disturbance-based differential evolution strategy to enhance the overall quality of the population. The efficacy of ACOA is assessed across four dimensions: population diversity, exploration-exploitation balance, convergence characteristics, and diverse strategy variations. Ablation experiments further validate the effectiveness of individual strategy modules. Experimental results on CEC-2017 and CEC-2022 benchmarks, along with Wilcoxon rank-sum tests, demonstrate superior performance of ACOA compared to COA and other state-of-the-art optimization algorithms. Finally, ACOA's applicability and superiority are reaffirmed through experimentation on five real-world engineering challenges and a complex urban three-dimensional unmanned aerial vehicle (UAV) path planning problem.

关键词： coati optimization algorithm Dynamic antagonistic learning Exploration strategies and development Global search capability UAV path planning

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AI in gastrointestinal disease detection: overcoming segmentation challenges with coati optimization strategy

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EVOLVING SYSTEMS 2025年第1期16卷 1-19页

作者： Jagarajan, Manikandan Jayaraman, Ramkumar SRM Inst Sci & Technol Coll Engn & Technol Sch Comp Dept Comp Technol Chennai 603203 Tamil Nadu India

Today AI helps a lot within the healthcare industry in the handling and classification of diseases that affect people. Multiple AI-based computerized methodologies developed in recent years for diagnosing gastrointestinal diseases (GI) including polyps. Diagnosis of these diseases performed by human beings requires much more time is expensive and requires expertise in this field. Thus the comprised methodologies that help the doctors as a second opinion is greatly requested. The prime issues faced by these technologies are segmentation and finding the precise regions where the infected area is there since the affected region has varying locations and shapes. This is due to the segmentation inaccuracy leading the negative impacts on feature learning and further affecting the preciseness in classification. Thus this paper introduced a novel automatic strategy for diagnosing GI disease which is designed with some of the following prime phases, from preprocessing to classification. The input GI images are gained from the Kvasir Dataset contains multiple classes like polyps, ulcerative colitis, etc., and the CVC-ClinicDB dataset which includes a single class of polyps images. For extracting the features a coati optimization algorithm is employed in the feature learning model which helps in finding and focusing on the relevant features or information and makes the model more robust. A hybrid U-net model is employed for the pixel-level segmenting process, which can minimize the errors and enhance the segmentation process. Along with the Mask region based convolutional neural network (Mask RCNN) is employed to classify the segmented images, which enhances the accuracy of diagnosis and also the affected area's localization. Experimental outputs concluded that the proposed method offered realistic and pragmatic solutions for diagnosing GI diseases with enhanced accuracy of 98.8%, 98.2% recall, 97% F1-score, and 97.5% preciseness value. Further, it improved the prognosis pace

关键词： Gastrointestinal diseases coati optimization algorithm Hybrid U-Net Mask regional convolutional neural network Polyps

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Flexible Reconfiguration for Optimal Operation of Distribution Network Under Renewable Generation and Load Uncertainty

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ENERGIES 2025年第2期18卷 266-266页

作者： Esmaeilnezhad, Behzad Amini, Hossein Noroozian, Reza Jalilzadeh, Saeid Univ Zanjan Fac Engn Dept Elect Engn Zanjan 45371 38791 Iran Virginia Polytech Inst & State Univ Bradly Dept Elect & Comp Engn Blacksburg VA 24061 USA

The primary objective when operating a distribution network is to minimize operating costs while taking technical constraints into account. Minimizing the operational costs is difficult when there is a high penetration of renewable resources and variability of loads, which introduces uncertainty. In this paper, a flexible, dynamic reconfiguration model is developed that enables a distribution network to minimize operating costs on an hourly basis. The model fitness function is to minimize the system costs, including power loss, voltage deviation, purchased power from the upstream network, renewable generation, and switching costs. The uncertainty of the load and generation from renewable energies is planned to use their probability density functions via a scenario-based approach. The suggested optimization problem is solved using a metaheuristic approach based on the coati optimization algorithm (COA) due to the nonlinearity and non-convexity of the problem. To evaluate the performance of the presented approach, it is validated on the IEEE 33-bus radial system and TPC 83-bus real system. The simulation results show the impact of dynamic reconfiguration on reducing operation costs. It is found that dynamic reconfiguration is an efficient solution for reducing power losses and total energy drawn from the upstream network by increasing the number of switching operations.

关键词： coati optimization algorithm dynamic reconfiguration operation cost renewable generation uncertainty

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Improving data transmission through optimizing blockchain sharding in cloud IoT based healthcare applications

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EGYPTIAN INFORMATICS JOURNAL 2025年 30卷

作者： Mythili, J. Gopalakrishnan, R. KSRangasamy Coll Technol Comp Sci & Engn Tiruchengode Tamilnadu India KSRangasamy Coll Technol Elect & Elect Engn Tiruchengode Tamil Nadu India

The rapid progress of blockchain technology is increasingly crucial in healthcare systems, where Electronic Health Records (EHRs) store vital and confidential information. However, these systems are susceptible to security risks like unauthorized access and data breaches. To tackle these issues, a decentralized, tamper-proof, and transparent healthcare network is necessary. According to this fact, the Sec-Health protocol utilizes cryptographic techniques with blockchain and InterPlanetary File System (IPFS) to safeguard EHRs. Despite advancements, managing large files, such as medical imaging data, remains a challenge due to scalability issues and limited research. Introducing sharding has the potential to improve network scalability, but if not configured correctly, it could result in orphan blocks and forks, leading to security vulnerabilities and network delays. To address this, a new sharded blockchain-based protocol called an Adaptive Sec-Health (AdaSec-Health) is proposed, utilizing an Enhanced coati optimization algorithm (ECOA) in high-throughput and low-latency healthcare systems. The ECOA optimizes multiple factors to minimize orphan blocks and forks while balancing Fork Probability (FP) and User Experience (UE). Also, it introduces a cost function to optimize network security-stability tradeoffs. Thus, AdaSec-Health protocol improves the scalability and security of healthcare blockchain systems. The experiments are conducted on the network EIP-1559 using 1000 nodes with 1 to 4 shards to validate the scalability and security of the AdaSec-Health protocol. The results demonstrate that the AdaSec-Health protocol achieves 3280 transactions per second (tps) of mean throughput, 28 s of mean userperceived latency, 0.47 gas unit of average marginal cost, 36 transactions of average block size, and a 13-second mean interval between blocks for 1000 nodes in 4 shards compared to the other healthcare blockchain systems. In terms of security analysis, AdaSec-Health ach

关键词： Blockchain Electronic health records Sec-Health Sharding Orphan blocks Fork probability coati optimization algorithm

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Bottom-up discrete systematic modelling for analysis and prediction of future trends for land-sea environmental pollution systems

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APPLIED MATHEMATICAL MODELLING 2025年 138卷

作者： Yin, Kedong Zhao, Yufeng Li, Xuemei Zhou, Shiwei Ocean Univ China Inst Marine Dev Qingdao 266100 Peoples R China Ocean Univ China Sch Management Qingdao 266100 Peoples R China Ocean Univ China Sch Econ Qingdao 266100 Peoples R China Shandong Univ Finance & Econ Sch Management Sci & Engn Jinan 250014 Peoples R China Shandong Univ Finance & Econ Inst Marine Econ & Management Jinan 250014 Peoples R China

Addressing the challenge of marine environmental pollution requires accurate forecasting systems, yet current methods relying on artificial intelligence and big data face limitations due to insufficient and inconsistent marine data. This paper proposes an integrated bottom-up prediction system featuring a grey modeling module and an optimization module. The grey modeling module introduces a novel nonlinear nested systematic grey model, which employs multistructure discretization to capture intricate and variable system changes by leveraging the dual nonlinearity of cumulative generated sequences and time trends. Nested solutions minimize prediction errors through discrete recursive equations with underlying-upper structures. The optimization module utilizes an efficient bio-inspired meta-heuristic algorithm, the coati optimization algorithm, which balances global and local search strategies. The accuracy and robustness of the proposed model are enhanced by optimizing its nonlinear parameters through comparative analysis with other algorithms and conducting a sensitivity analysis on the number of iterations. The proposed prediction system is validated in representative Chinese provinces (Zhejiang, Shandong, and Hainan), demonstrating high prediction accuracy for both land-based and marine environmental pollution systems, with mean absolute percentage errors below 8 % in both training and test sets. Projections indicate that by 2030, land-based source pollution intensities in these provinces will decrease by approximately 5.71 %, 0.16 %, and 1.64 %, respectively, while marine pollution indices are expected to decline by 3.28 %, 4.86 %, and 2.89 %. This study provides a valuable predictive tool for addressing the complexity, variability, and uncertainty of marine environmental pollution, while contributing to the promotion of land-sea integration and the protection of marine ecosystems.

关键词： Bottom-up nested prediction system Nonlinear systematic grey model Multi-structure discretization coati optimization algorithm Marine environmental pollution system

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Facilitating Multi-UAVs application for rescue in complex 3D sea wind offshore environment: A scalable Multi-UAVs collaborative path planning method based on improved coatis optimization algorithm

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OCEAN ENGINEERING 2025年 324卷

作者： Li, Hangyu Miao, Fahui Mei, Xiaojun Shanghai Maritime Univ Coll Informat Engn Shanghai 201306 Peoples R China Minist Educ Engn Res Ctr Integrat & Applicat Digital Learning Beijing 100039 Peoples R China Shanghai Maritime Univ Merchant Marine Coll Shanghai 201306 Peoples R China

With the development of marine economy, maritime activities are becoming more frequent, and the importance of maritime rescue missions is becoming increasingly prominent. Multi-UAVs collaborative search and rescue provides a new solution for maritime rescue, and the UAV path planning problem is one of the key challenges. First, this paper uses the lamb-Ossen model to simulate the sea wind and construct a 3D offshore environmental model with sea wind. Secondly, this paper comprehensively considers factors such as sea wind and distance between UAVs, and proposes a multi-UAVs path cost function. Meanwhile, a guided collaborative control mechanism is proposed to form a novel multi-UAVs collaborative path planning method. In addition, the multiUAVs path solution uses an improved multi-Dimensional coati optimization algorithm (MDCOA), designs new strategies through Levy flight, differential operator, and dynamic opposite learning, and adds a covariance matrix learning (CML) stage to expand the search perspective. Finally, the MDCOA algorithm was used to solve the multi-UAVs paths in different offshore simulation scenarios. The paths obtained by the algorithm reduced the path cost and showed excellent performance compared with other algorithms. Experimental results prove the effectiveness and scalability of the algorithm proposed in this paper.

关键词： Offshore environment Path planning of Multi-UAVs coati optimization algorithm Dynamic opposite learning Covariance matrix learning

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Downlink OTFS-RSMA Cross-Domain Transmission Scheme and Sum-Rate Maximization

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IEEE COMMUNICATIONS LETTERS 2025年第3期29卷 600-604页

作者： Huai, Qiwei Yuan, Weina Wu, Yue Fan, Pingzhi East China Univ Sci & Technol Sch Informat Sci & Engn Shanghai 200237 Peoples R China Southwest Jiaotong Univ Key Lab Informat Coding & Transmiss Chengdu 611756 Peoples R China

Rate splitting multiple access (RSMA) is an emerging multiple access scheme that plays a significant role in improving user sum-rate. This letter proposes a downlink orthogonal time frequency space-RSMA (OTFS-RSMA) cross-domain transmission scheme based on heterogeneous mobile user grouping and multicast technology, where high-speed users are served in the delay-Doppler (DD) domain and low-speed users are served in the time-frequency (TF) domain. Specifically, the OTFS-RSMA scheme categorizes users into high-speed users group and low-speed users group based on their mobility and then uses multicast to send cross-domain signals to the respective user groups, with RSMA used for transmission within the groups. By jointly optimizing the rate allocation and power control, this letter proposes a two-stage algorithm based on the coati optimization algorithm (COA) to maximize user sum-rate under the constraints of base station transmission power and QoS rate requirements. Our simulation results show that the proposed OTFS-RSMA scheme significantly outperforms the OTFS-NOMA scheme in terms of improving sum-rate.

关键词： RSMA OTFS coati optimization algorithm sum-rate maximization RSMA OTFS coati optimization algorithm sum-rate maximization

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Interpretable real-time monitoring of short-term rockbursts in underground spaces based on microseismic activities

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SCIENTIFIC REPORTS 2025年第1期15卷 1-13页

作者： Kadkhodaei, Mohammad Hossein Ghasemi, Ebrahim Isfahan Univ Technol Dept Min Engn Esfahan *** Iran

In this study, two novel hybrid intelligent models were developed to evaluate the short-term rockburst using the random forest (RF) method and two meta-heuristic algorithms, whale optimization algorithm (WOA) and coati optimization algorithm (COA), for hyperparameter tuning. Real-time predictive models of this phenomenon were created using a database comprising 93 case histories, taking into account various microseismic parameters. The results indicated that the WOA achieved the highest overall performance in hyperparameter tuning for the RF model, outperforming the COA. RF-WOA model accurately predicted the occurrence of this phenomenon with an accuracy of 0.944. Additionally, for this model, precision, recall and F1-score were obtained as 0.950, 0.944 and 0.943, respectively, indicating that the proposed model is robust in predicting damage severity of rockburst in deep underground projects. Subsequently, the Shapley additive explanations (SHAP) method was employed to interpret and explain the prediction process and assess the influence of input features based on RF-WOA model. The results showed that three parameters including cumulative seismic energy, cumulative microseismic events, and cumulative apparent volume have the greatest impact on the occurrence of rockburst events. This study provides an interpretable and transparent resource for accurately predicting rockburst events in real time. It can facilitate estimating project costs, selecting a suitable support system, and identifying essential ways to limit the danger of rockburst.

关键词： Short-term rockburst Microseismic monitoring Random forest Whale optimization algorithm coati optimization algorithm

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Quantitative Structure–Activity Relationship Modeling Based on Improving Kernel Ridge Regression

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Journal of Chemometrics 2025年第5期39卷

作者： Mahmood, Shaimaa Waleed Basheer, Ghalya Tawfeeq Algamal, Zakariya Yahya Department of Statistics and Informatics University of Mosul Mosul Iraq Department of Operations Research and Intelligent Techniques University of Mosul Mosul Iraq

The quantitative structure–activity relationship (QSAR) as an effective and promising model to better understands the relationship between chemical activity and chemical compounds is usually used in modeling chemical datasets. Kernel ridge regression (KRR) has attracted the interest of scholars recently because of its non-iterative methodology for problem solving. KRR is a highly regarded and practical machine learning approach that has successfully tackled classification and regression issues. So is a regression method that uses a nonlinear kernel function to define an inner product in a higher-dimensional transformed space. This allows for generalization performance based on regularization least squares solution. However, the performance of KRR is affected by the choices of the values of the hyper-parameters that define the type of kernel. This has a major processing cost, uses memory, and is also accompanied by poor accuracy performance when studying the prior methods of determining these hyper-parameter values. Thus, the main highlighted enhancement in this paper is the enhancement of the coati optimization algorithm by applying elite opposite-based learning to increase the density of population around the search space to optima for the proper selection of the best hyperparameters. Thus, it is necessary to verify and compare its work with the proposed improvement of KRR in increasing its performance, seven public chemical datasets were used. Based on several assessment criteria, the results show that the proposed improvement is superior to all the baseline methods regarding the classification performance. © 2025 John Wiley & Sons Ltd.

关键词： coati optimization algorithm kernel learning kernel ridge regression opposite-based learning QSAR

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