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Enhanced coati optimization algorithm and its application in power allocation for NOMA-VLC systems

OPTICS COMMUNICATIONS 2025年 579卷

作者： Sun, Yunshan Wang, Wenxiang Liu, Ting Zhang, Jingyu Fei, Teng Guo, Yuhan Tianjin Univ Commerce Sch Informat Engn Tianjin 300134 Peoples R China China Mobile Commun Grp Co Ltd Shanghai Peoples R China

coati optimization algorithm has drawbacks, including reduced optimization accuracy in the search space and a tendency to get trapped in local optima. To address these shortcomings, this paper proposes an Enhanced coati optimization algorithm (EnCOA). Firstly, to address the shortcomings of the traditional COA, which tends to local optimization guided by iguana, a convex lens imaging strategy and mutation perturbation are proposed to enhance population diversity and improve the algorithm's global exploration capability. Secondly, the concepts of bidirectional grouping in the teaching phase of the Teaching-Learning-Based optimization and the cosine learning factor are introduced to improve convergence speed and accuracy. Finally, the water wave dynamic adaptive factor is utilized to enhance the stability of the COA. The performance of EnCOA is assessed using various benchmark functions, including those from CEC2017, to highlight its effectiveness in optimization tasks. Furthermore, Visible Light Communication (VLC) provides high transmission speeds, robust interference immunity, and low energy consumption, positioning it as an effective solution for high-speed data transmission needs. To enhance system performance, Non-Orthogonal Multiple Access (NOMA) is integrated into the VLC framework. This study focuses on power allocation within the NOMA-VLC framework, with the goal of maximizing user rates while ensuring fairness. Simulation results show that EnCOA outperforms other algorithms in power allocation within the NOMA-VLC framework, especially under varying user numbers, residual interference, LED application scenarios, and shifts in photodetector alignment. EnCOA consistently demonstrates efficient power distribution and superior performance under complex communication conditions. Therefore, EnCOA is of significant importance in solving global optimization problems and power allocation challenges in NOMA-VLC systems.

关键词： coati optimization algorithm Visible light communication Non-orthogonal multiple access Mutation perturbation Power allocation

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Enhanced coati optimization algorithm for Static and Dynamic Transmission Network Expansion Planning Problems

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IEEE ACCESS 2025年 13卷 35068-35100页

作者： Demirbas, Muhammet Dosoglu, M. Kenan Duman, Serhat Kastamonu Univ Tosya Vocat Sch Dept Elect & Energy TR-37302 Kastamonu Turkiye Duzce Univ Fac Engn Dept Elect & Elect Engn TR-81620 Duzce Turkiye Bandirma Onyedi Eylul Univ Fac Engn & Nat Sci Dept Elect Engn TR-10200 Bandirma Turkiye

The power systems are becoming more and more complex due to the inclusion of new components and increasing load demand. Consequently, it is imperative to incorporate additional generation units and transmission links into the system. Transmission Network Expansion Planning (TNEP) seeks to include generation units and transmission lines into the system at optimal locations and minimal costs. Mathematical techniques are extensively employed to address the problem. Nonetheless, mathematical methods necessitate extensive computation durations. Consequently, novel solution strategies are under investigation. The TNEP problem is characterized by an innovative and effective metaheuristic optimization techniques. This study presents a novel Opposition Based Learning and Fitness Distance Balance based coati optimization algorithm (FDBCOA-OBL) designed to address Static and Dynamic TNEP problems. An extensive experimental investigation was undertaken to evaluate the efficacy of the suggested method in addressing the benchmark test suites and the TNEP problem. The FDBCOA-OBL algorithm, utilizing the Elite OBL approach, surpassed all other comparative versions in addressing the benchmark test problems. The Wilcoxon analysis indicates that it lost 6 problems, tied in 110, and won 166 problems. The proposed approach resolved the TNEP problem in 6, 25, and 93-bus test systems. The Static TNEP solution was applied to the 6 and 25 bus test systems, while the Dynamic Multistage TNEP method was utilized in the 93-bus test system. The acquired investment expenses were compared to the research already documented in the literature. The findings indicate that the suggested method demonstrates robust performance.

关键词： optimization Heuristic algorithms Metaheuristics Costs Planning Investment Load flow Power transmission lines Power system dynamics Genetic algorithms coati optimization algorithm fitness-distance balance method opposition-based learning transmission network expansion planning problem

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SL-COA:Hybrid Efficient and Enhanced coati optimization algorithm for Structural Reliability Analysis

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Computer Modeling in Engineering & Sciences 2025年第4期143卷 767-808页

作者： Yunhan Ling Huajun Peng Yiqing Shi Chao Xu Jingzhen Yan Jingjing Wang Hui Ma China Academy of Machinery Beijing Research Institute of Mechanical&Electrical Technology Co.Ltd.Beijing100083China AVICGuizhou Anda Aviation Forging Co. Ltd.Anshun561000China School ofMechanical and Electrical Engineering University of Electronic Science and Technology of ChinaChengdu611731China

Thetraditional first-order reliability method(FORM)often encounters challengeswith non-convergence of results or excessive calculation when analyzing complex engineering *** improve the global convergence speed of structural reliability analysis,an improved coati optimization algorithm(COA)is proposed in this *** this study,the social learning strategy is used to improve the coati optimization algorithm(SL-COA),which improves the convergence speed and robustness of the newheuristic optimization ***,the SL-COAis comparedwith the latest heuristic optimization algorithms such as the original COA,whale optimization algorithm(WOA),and osprey optimization algorithm(OOA)in the CEC2005 and CEC2017 test function sets and two engineering optimization design *** optimization results show that the proposed SL-COA algorithm has a high ***,this study introduces the SL-COA algorithm into the MPP(Most Probable Point)search process based on FORM and constructs a new reliability analysis ***,the proposed reliability analysis method is verified by four mathematical examples and two engineering *** results show that the proposed SL-COA-assisted FORM exhibits fast convergence and avoids premature convergence to local optima as demonstrated by its successful application to problems such as composite cylinder design and support bracket analysis.

关键词： Hybrid reliability analysis single-loop interactive hybrid analysis most probability point metaheuristic algorithms coati optimization algorithm

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Enhanced coati optimization algorithm using elite opposition-based learning and adaptive search mechanism for feature selection (May, 10.1007/s13042-024-02222-3, 2024)

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2025年第1期16卷 395-395页

作者： Qtaish, Amjad Braik, Malik Albashish, Dheeb Alshammari, Mohammad T. Alreshidi, Abdulrahman Alreshidi, Eissa Jaber Univ Hail Dept Informat & Comp Sci Hail Saudi Arabia Appl Sci Private Univ Accounting Dept Amman Jordan Al Balqa Appl Univ Comp Sci Dept Salt Jordan

The rapid rise in volume and feature dimensions is negatively impacting machine learning and many other areas, leading to worse classification accuracy and higher computational costs. Feature Selection (FS) methods are crucial to lessen feature dimensionality, which act by removing attributes like irrelevant and less informative information which may have a detrimental impact on the performance of classifiers. This paper presents an Enhanced variant of the coati optimization algorithm (ECOA) that features a better search ability than the basic COA. The COA algorithm was newly evolved to imitate the behavior of coatis when they hunt and attack iguanas as well as when they try to flee from predators. Although the authors of this algorithm state that it is promising, it occasionally exhibits poor search performance and early convergence. To mitigate these issues, the ECOA algorithm was proposed that makes use of elite opposite-based learning in addition to some adaptive search mechanisms. ECOA is expected to have an improved search mechanism and can prevent trapping at local optimum, depending on the mutation, mutation neighborhood search, and rollback procedures. Moreover, it enhances population variety and convergence rate. The COA and ECOA algorithms were used to solve FS problems by selecting optimal feature subsets based on a binary version of each adopted algorithm and the k-Nearest Neighbor (k-NN) classifier. To assess the performance of the Binary ECOA (BECOA), a number of experiments was performed on 24 datasets collected from many sources. Further, six criteria-sensitivity, specificity, classification accuracy, fitness value, number of chosen features, and run time-were used to assess the performance of BECOA. Experimental findings show the excellence of BECOA over other k-NN based FS methods, including Binary COA (BCOA) and other binary optimization methods, in a number of assessment aspects. In particular, among the 24 datasets deemed, BECOA, which yielded

关键词： coati optimization algorithm Feature selection Opposition-based learning optimization

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Self-adaptive differential evolution-based coati optimization algorithm for multi-robot path planning

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ROBOTICA 2025年

作者： Zhu, Lun Zhou, Guo Zhou, Yongquan Luo, Qifang Huang, Huajuan Wei, Xiuxi Guangxi Minzu Univ Coll Artificial Intelligence Nanning Peoples R China China Univ Polit Sci & Law Dept Sci & Technol Teaching Beijing Peoples R China Guangxi Key Labs Hybrid Computat & IC Design Anal Nanning Peoples R China

The multi-robot path planning problem is an NP-hard problem. The coati optimization algorithm (COA) is a novel metaheuristic algorithm and has been successfully applied in many fields. To solve multi-robot path planning optimization problems, we embed two differential evolution (DE) strategies into COA, a self-adaptive differential evolution-based coati optimization algorithm (SDECOA) is proposed. Among these strategies, the proposed algorithm adaptively selects more suitable strategies for different problems, effectively balancing global and local search capabilities. To validate the algorithm's effectiveness, we tested it on CEC2020 benchmark functions and 48 CEC2020 real-world constrained optimization problems. In the latter's experiments, the algorithm proposed in this paper achieved the best overall results compared to the top five algorithms that won in the CEC2020 competition. Finally, we applied SDECOA to optimization multi-robot online path planning problem. Facing extreme environments with multiple static and dynamic obstacles of varying sizes, the SDECOA algorithm consistently outperformed some classical and state-of-the-art algorithms. Compared to DE and COA, the proposed algorithm achieved an average improvement of 46% and 50%, respectively. Through extensive experimental testing, it was confirmed that our proposed algorithm is highly competitive. The source code of the algorithm is accessible at: https://***/matlabcentral/fileexchange/164876-HDECOA.

关键词： differential evolution coati optimization algorithm self-adaptive differential evolution-based coati optimization multi-robot path planning metaheuristic

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Economic analysis of grid-connected wind generators with permanent magnet synchronous generator and flywheel energy storage using coati optimization algorithm and triple-memristor Hopfield neural network

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INTERNATIONAL JOURNAL OF DYNAMICS AND CONTROL 2025年第4期13卷 1-16页

作者： Venkatarajan, Shanmugasundaram Manickam, Panneer Selvam Rao, Sumana Sreenivasa Patra, Jyoti Prasad Sona Coll Technol Dept Elect & Elect Engn Salem 636005 Tamilnadu India Dayanandasagar Coll Engn Dept Elect & Elect Engn Bengaluru Karnataka India NIET UG PG Diploma Engn Nigam Inst Engn & Technol Govind PurMundali Cuttack 754006 Odisha India

The permanent magnet synchronous generator (PMSG) integrated with flywheel energy storage system (FESS) increases the efficiency level and operational reliability of grid-connected wind generators through its effective power storage and stabilization capabilities. A hybrid method aims to assess economically the PMSG connected to FESS for grid-connected wind generation purposes. This paper presents a novel hybrid approach combining the coati optimization algorithm (COA) with the triple-memristor Hopfield neural network (TMHNN) for the economic performance of a PMSG integrated with a FESS for grid-connected wind generation. The aim is to improve the economic performance of the PMSG integrated with a FESS by lowering the levelized cost of energy (LCOE). The COA is utilized to optimize the operational parameters of the PMSG and the FESS, ensuring efficient energy management (EM) and storage. The TMHNN is utilized to forecast energy generation and demand trends, enabling optimized scheduling and resource allocation for enhanced EM. A MATLAB platform implements the proposed method for testing with the existing approaches including wild horse optimizer (WHO) and their counterparts gases Brownian motion optimization (GBMO) and artificial bee colony optimization (ABCO) and Archimedes optimization algorithm (AOA) and particle swarm optimization (PSO). The research introduces a complete framework which decreases LCOE to $0.28/kWh alongside improving wind energy system operational stability and efficiency. Unlike traditional optimization methods, which often focus on single aspects of EM, the COA-TMHNN technique integrates advanced forecasting capabilities with proactive EM strategies, ensuring better adaptability to fluctuating energy demands and generation patterns.

关键词： coati optimization algorithm Triple-memristor Hopfield neural network Permanent magnet synchronous generator Grid-connected wind generation

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coati optimization algorithm for brain tumor identification based on MRI with utilizing phase-aware composite deep neural network

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ELECTROMAGNETIC BIOLOGY AND MEDICINE 2025年第2期44卷 119-136页

作者： Thangavel, Rajesh Kumar Sundarraj, Antony Allwyn Ramakrishnan, Jayabrabu Balasubramanian, Krishnasamy Sri Krishna Coll Technol Dept Comp Sci & Engn Artificial Intelligence & Mac Coimbatore 641042 Tamil Nadu India Sri Shakthi Inst Engn & Technol Dept Food Technol Coimbatore Tamil Nadu India Jazan Univ Coll Engn & Comp Sci Dept Comp Sci Jazan Saudi Arabia Deemed Univ Kalasalingam Acad Res & Educ Dept Comp Applicat Krishnankoil India

Brain tumors can cause difficulties in normal brain function and are capable of developing in various regions of the brain. Malignant tumours can develop quickly, pass through neighboring tissues, and extend to further brain regions or the central nervous system. In contrast, healthy tumors typically develop slowly and do not invade surrounding tissues. Individuals frequently struggle with sensory abnormalities, motor deficiencies affecting coordination, and cognitive impairments affecting memory and focus. In this research, Utilizing Phase-aware Composite Deep Neural Network Optimized with coati Optimized algorithm for Brain Tumor Identification Based on Magnetic resonance imaging (PACDNN-COA-BTI-MRI) is proposed. First, input images are taken from the brain tumour Dataset. To execute this, the input image is pre-processed using Multivariate Fast Iterative Filtering (MFIF) and it reduces the occurrence of over-fitting from the collected dataset;then feature extraction using Self-Supervised Nonlinear Transform (SSNT) to extract essential features like model, shape, and intensity. Then, the proposed PACDNN-COA-BTI-MRI is implemented in Matlab and the performance metrics Recall, Accuracy, F1-Score, Precision Specificity and ROC are analysed. Performance of the PACDNN-COA-BTI-MRI approach attains 16.7%, 20.6% and 30.5% higher accuracy;19.9%, 22.2% and 30.1% higher recall and 16.7%, 21.9% and 30.8% higher precision when analysed through existing techniques brain tumor identification using MRI-Based Deep Learning Approach for Efficient Classification of Brain Tumor (MRI-DLA-ECBT), MRI-Based Brain Tumor Detection using Convolutional Deep Learning Methods and Chosen Machine Learning Techniques (MRI-BTD-CDMLT) and MRI-Based Brain Tumor Image Detection using CNN-Based Deep Learning Method (MRI-BTID-CNN) methods, respectively.

关键词： Brain tumor coati optimization algorithm multivariate fast iterative filtering phase-aware composite deep neural network and self-supervised nonlinear transform

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Loading pattern optimization of the multi-batch boron-free core by the coati optimization algorithm

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NUCLEAR ENGINEERING AND TECHNOLOGY 2025年第7期57卷

作者： Hosseinllu, M. Safarzadeh, O. Abbasi, M. Dehghani, F. Shahid Beheshti Univ Fac Engn Tehran Iran Shahed Univ Fac Engn Tehran Iran

To reach the soluble-boron-free reactor core proper performance and efficient operation, attention to the loading pattern strategy is one of the fundamental problems. Fuel management of these reactors is a more complicated issue due to the absence of soluble boron and using of more burnable poisons. This paper discusses the optimization of the loading pattern of the soluble-boron-free core by a novel meta-heuristic algorithm known as the coati algorithm which reproduces their strategy while attacking iguanas and escaping from predators. Furthermore, a neutronic/thermal-hydraulic multi-objective function is used in this context. The excess reactivity, radial power peaking factors, reactivity swing, maximum fuel centerline temperature, and coolant enthalpy-rise factor have been considered in the proposed multi-objective function. The coati optimization algorithm proposed optimized fuel arrangements that give a proper radial power distribution over the cycle, including a power profile that will generally yield the minimum relative power distributions. For the final optimized pattern, the criticality search is done during the operation cycle by proper control rod behavior strategy. The obtained outcomes indicate that this algorithm and the defined multi-objective function can provide proper solutions for the boron-free cores loading pattern optimization, in such a way that can considering the reactivity swing entire the cycle.

关键词： coati optimization algorithm Small modular reactor Fuel arrangement optimization Boron-free core Multi-batch core

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Cost-Effective Prediction Model for Optimal Selection of Software Faults Using coati optimization algorithm

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SN Computer Science 2025年第5期6卷 1-26页

作者： Kumar, Hrishikesh Das, Himansu School of Computer Engineering Kalinga Institute of Industrial Technology (KIIT) Deemed to be University Odisha Bhubaneswar 751024 India

Software fault prediction (SFP) is a cost-effective method that helps to detect software faults at an early stage and improves the quality of software. Different machine learning models were used for the prediction of software defects. These algorithms use previous software metrics and fault data as independent features to detect the defects in the software. Early detection of software faults saves the costs required to rectify those vulnerabilities. But as the amount of data (independent features) is very large, feature selection (FS) has become a crucial process to get the most useful information from the data. In this work, a novel and economical approach for SFP has been proposed namely, FS using coati optimization algorithm (FSCOA). It selects the relevant and most useful subset of features from the data. This FS algorithm was embedded with four popular classifiers (KNN, QDA, DT, and NB) and the results were compared with four well-known FS algorithms such as, FS using PSO, FS using ACO, FS using GA, and FS using DE. Twelve open-source software defect datasets were used for conducting the experiments. The experimental results indicated that the FSCOA outperformed the compared algorithms in more than 90% of the test cases. FSCOA gave higher accuracies of classification and the number of optimal features selected were less than 50% of the original features. Moreover, the superiority of the proposed FSCOA in comparison to the other FS models has been statistically validated with the help of Friedman Test and Holm Test where, FSCOA held a Friedman rank 1 in all the test cases. The results from these statistical tests supports the experimental results showing the enhanced performance of FSCOA compared to the other FS models taken into consideration. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Classification coati optimization algorithm Feature selection Features Machine learning Software fault prediction

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Enhanced coati optimization algorithm for Big Data optimization Problem

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NEURAL PROCESSING LETTERS 2023年第8期55卷 10131-10199页

作者： Bas, Emine Yildizdan, Gulnur Konya Tech Univ Fac Engn & Nat Sci Dept Software Engn TR-42075 Konya Turkiye Selcuk Univ Kulu Vocat Sch TR-42770 Konya Turkiye

The recently proposed coati optimization algorithm (COA) is one of the swarm-based intelligence algorithms. In this study, the current COA algorithm is developed and Enhanced COA (ECOA) is proposed. There is an imbalance between the exploitation and exploration capabilities of the COA. To balance the exploration and exploitation capabilities of COA in the search space, the algorithm has been improved with two modifications. These modifications are those that preserve population diversity for a longer period of time during local and global searches. Thus, some of the drawbacks of COA in search strategies are eliminated. The achievements of COA and ECOA were tested in four different test groups. COA and ECOA were first compared on twenty-three classic CEC functions in three different dimensions (10, 20, and 30). Later, ECOA was tested on CEC-2017 with twenty-nine functions and on CEC-2020 with ten functions, and its success was demonstrated in different dimensions (5, 10, and 30). Finally, ECOA has been shown to be successful in different cycles (300, 500, and 1000) on Big Data optimization Problems (BOP), which have high dimensions. Friedman and Wilcoxon tests were performed on the results, and the obtained results were analyzed in detail. According to the results, ECOA outperformed COA in all comparisons performed. In order to prove the success of ECOA, seven newly proposed algorithms (EMA, FHO, SHO, HBA, SMA, SOA, and JAYA) were selected from the literature in the last few years and compared with ECOA and COA. In the classical test functions, ECOA achieved the best results, surpassing all other algorithms when compared. It achieved the second-best results in CEC-2020 test functions and entered the top four in CEC-2017 and BOP test functions. According to the results, ECOA can be used as an alternative algorithm for solving small, medium, and large-scale continuous optimization problems.

关键词： coati optimization algorithm Large dimension CEC-2017 CEC-2020 Big data optimization problem (BOP)

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