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Wind speed prediction by a swarm intelligence based deep learning model via signal decomposition and parameter optimization using improved chimp optimization algorithm

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ENERGY 2023年第1期276卷

作者： Suo, Leiming Peng, Tian Song, Shihao Zhang, Chu Wang, Yuhan Fu, Yongyan Nazir, Muhammad Shahzad Huaiyin Inst Technol Fac Automat Huaian 223003 Peoples R China Huaiyin Inst Technol Jiangsu Permanent Magnet Motor Engn Res Ctr Huaian 223003 Peoples R China

Accurate prediction of wind speed plays a very important role in the stable operation of wind power plants. In this study, the goal is to establish a hybrid wind speed prediction model based on Time Varying Filtering based Empirical Mode Decomposition (TVFEMD), Fuzzy Entropy (FE), Partial Autocorrelation Function (PACF), improved chimp optimization algorithm (IChOA) and Bi-directional Gated Recurrent Unit (BiGRU). Firstly, the original wind speed data was decomposed by TVFEMD to obtain modal components, and FE aggregation is used to decrease the computational complexity. Secondly, the components are processed by PACF to extract important input features. Thirdly, the BiGRU parameters are optimized using IChOA which is an improved version of ChOA. Finally, the optimized BiGRU is used to predict the decomposed components, and the predicted components are summed to obtain the final prediction result. In this experiment, the proposed model is used to predict the data of four months of a year from Station 46,060 of National Data Buoy Center, and the performance of eight benchmark models is analyzed. Experimental results show that TVFEMD and PACF can improve the prediction accuracy of the model. IChOA is feasible to optimize the parameters of BiGRU and can improve the prediction performance.

关键词： Wind speed prediction TVFEMD chimp optimization algorithm BiGRU Deep learning

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A dual-adaptive stochastic reinforcement chimp optimization algorithm for fire detection and multidimensional problem solving

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SCIENTIFIC REPORTS 2024年第1期14卷 1-28页

作者： Zhang, Ziyang Tan, Lingye Martin, Diego Qian, Leren Khishe, Mohammad Jangir, Pradeep Nanyang Technol Univ Sch Civil & Environm Engn 50 Nanyang Ave Singapore 639798 Singapore Univ Valladolid Escuela Ingn Informat Segovia Dept Informat Segovia 40005 Spain Arizona State Univ Sch Comp & Augmented Intelligence Tempe AZ 85281 USA Imam Khomeini Naval Sci Univ Nowshahr Dept Elect Engn Nowshahr Iran Yuan Ze Univ Innovat Ctr Artificial Intelligence Applicat Taoyuan Taiwan Appl Sci Private Univ Appl Sci Res Ctr Amman Jordan Saveetha Inst Med & Tech Sci Saveetha Sch Engn Dept Biosci Chennai 602105 India Jadara Univ Jadara Univ Res Ctr Irbid Jordan G Era Hill Univ Dept CSE Dehra Dun 248002 India Graphic Era Deemed Be Univ Dept Comp Sci & Engn Dehra Dun Uttarakhand India Chandigarh Univ Univ Ctr Res & Dev Mohali 140413 India

chimp optimization algorithm (CHOA) is a recently developed nature-inspired technique that mimics the swarm intelligence of chimpanzee colonies. However, the original CHOA suffers from slow convergence and a tendency to reach local optima when dealing with multidimensional problems. To address these limitations, we propose TASR-CHOA, a twofold adaptive stochastic reinforced variant. The TASR-CHOA algorithm integrates two novel methodologies: a stochastic approach to improve the speed at which convergence is achieved and a dual adaptive weighting approach to optimize the exploration of early patterns, which refer to initial trends or behaviors in the algorithm's convergence process during the early stages of iterations and the exploitation of subsequent tendencies, indicating how these initial trends develop over time as the algorithm iterates and refines its search. To evaluate TASR-CHOA, we apply it to 29 conventional optimization benchmark functions, 10 IEEE CEC-06 benchmarks, 30 complicated IEEE CEC-BC benchmark functions, and ten well-known benchmark real-world challenges. We evaluate TASR-CHOA against 4 categorical optimization techniques as well as 18 top IEEE CEC-BC algorithms. Based on our broad investigation done using three statistical tests, we claim that TASR-CHOA outperforms the majority of the algorithms since within a position takes the best place, 54 out of 73 evaluation functions and engineering problems. In other cases, the results are almost the same as those of SHADE and CMA-ES over several comparisons. As an illustrative application of this joint approach, a computer-aided fire detection task is performed using a deep convolutional neural network combined with TASR-CHOA. We also outline the algorithm executed in steps, indicating computational complexity, which is O(NIxNV) + O(NIxNVx6) + O(NIxNV + 2xNIxNV) as a function of number of individuals (NI) and dimensions (NV).

关键词： chimp optimization algorithm Metaheuristic Twofold adaptive weighting IEEE CEC-BC competitions optimization Multidimensional problems

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A novel feature descriptor based coral image classification using extreme learning machine with ameliorated chimp optimization algorithm

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ECOLOGICAL INFORMATICS 2022年 68卷 101527-101527页

作者： Ganesan, Annalakshmi Santhanam, Sakthivel Murugan Sri Sivasubramaniya Nadar Coll Engn Dept Elect & Commun Engn Underwater Acoust Res Lab Chennai Tamil Nadu India

Coral reefs are one of the most biodiverse and productive ecosystems on earth, which acts as a habitat and provides food to a wide range of organisms. The coral reefs protect the coasts from storms, waves, and erosion. Unfortunately, this ecosystem is under threat by various natural and manmade factors, including pollution, ocean acidification, sedimentation, and climate change. The marine ecosystems need to be monitored regularly in order to minimize the aforementioned threats. Hence, an automated classification system is necessary for the classi-fication of coral images. In this paper, a novel method called "Systematic Local Directional Encoded Derivative Binary Pattern " (SLDEDBP) is proposed to extract the coral reef feature values. In this descriptor, the feature values are derived using two phases: Local Directional Derivative Binary Pattern (LDDBP) and Systematic Local Discriminate Binary pattern (SLDBP). In the LDDBP phase, the eight directional kirsch mask is employed in each 3 x 3 neighbourhood window to extract the directional edge response of an image. Then, in the SLDBP phase, the local neighbourhood pixel values are divided into four non-overlapping groups of Z, M, TZ (Tilted Z), and TM (Tilted M) to extract unique features values by considering the centre pixel value in a 3 x 3 neighbourhood window. Finally, the derived feature values of LDDBP and SLDBP are concatenated to procedure final discrim-inate feature values. The extracted feature values of coral images are applied to a hybridized method called Ameliorated chimp optimization algorithm with Extreme Learning Machine Classifier (AChOA-ELM) to opti-mize the relative weight of the input and biases of neural networks. This avoids overfitting and makes the model more generalized and robust. The proposed AChOA-ELM method performance is evaluated using different da-tabases of coral and standard images. The reliability of the proposed model has been evaluated utilizing the model performance metrics, ac

关键词： Image enhancement chimp optimization algorithm Extreme learning machine Coral image classification

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Simultaneous Feature Selection and Support Vector Machine optimization Using an Enhanced chimp optimization algorithm

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algorithmS 2021年第10期14卷 282页

作者： Wu, Di Zhang, Wanying Jia, Heming Leng, Xin Sanming Univ Sch Educ & Mus Sanming 365004 Peoples R China Northeast Forestry Univ Coll Mech & Elect Engn Harbin 150040 Peoples R China Sanming Univ Sch Informat Engn Sanming 365004 Peoples R China

chimp optimization algorithm (ChOA), a novel meta-heuristic algorithm, has been proposed in recent years. It divides the population into four different levels for the purpose of hunting. However, there are still some defects that lead to the algorithm falling into the local optimum. To overcome these defects, an Enhanced chimp optimization algorithm (EChOA) is developed in this paper. Highly Disruptive Polynomial Mutation (HDPM) is introduced to further explore the population space and increase the population diversity. Then, the Spearman's rank correlation coefficient between the chimps with the highest fitness and the lowest fitness is calculated. In order to avoid the local optimization, the chimps with low fitness values are introduced with Beetle Antenna Search algorithm (BAS) to obtain visual ability. Through the introduction of the above three strategies, the ability of population exploration and exploitation is enhanced. On this basis, this paper proposes an EChOA-SVM model, which can optimize parameters while selecting the features. Thus, the maximum classification accuracy can be achieved with as few features as possible. To verify the effectiveness of the proposed method, the proposed method is compared with seven common methods, including the original algorithm. Seventeen benchmark datasets from the UCI machine learning library are used to evaluate the accuracy, number of features, and fitness of these methods. Experimental results show that the classification accuracy of the proposed method is better than the other methods on most data sets, and the number of features required by the proposed method is also less than the other algorithms.

关键词： chimp optimization algorithm highly disruptive polynomial mutation Spearman's rank correlation coefficient Beetle Antennae Search algorithm support vector machine

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Social coevolution and Sine chaotic opposition learning chimp optimization algorithm for feature selection

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SCIENTIFIC REPORTS 2024年第1期14卷 1-22页

作者： Zhang, Li Chen, Xiaobo Jiangsu Univ Technol Coll Comp Engn Changzhou 213001 Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Educ Changchun 130012 Peoples R China Peoples Bank China Changzhou City Ctr Branch Changzhou 213001 Jiangsu Peoples R China

Feature selection is a hot problem in machine learning. Swarm intelligence algorithms play an essential role in feature selection due to their excellent optimisation ability. The chimp Optimisation algorithm (CHoA) is a new type of swarm intelligence algorithm. It has quickly won widespread attention in the academic community due to its fast convergence speed and easy implementation. However, CHoA has specific challenges in balancing local and global search, limiting its optimisation accuracy and leading to premature convergence, thus affecting the algorithm's performance on feature selection tasks. This study proposes Social coevolution and Sine chaotic opposition learning chimp optimization algorithm (SOSCHoA). SOSCHoA enhances inter-population interaction through social coevolution, improving local search. Additionally, it introduces sine chaotic opposition learning to increase population diversity and prevent local optima. Extensive experiments on 12 high-dimensional classification datasets demonstrate that SOSCHoA outperforms existing algorithms in classification accuracy, convergence, and stability. Although SOSCHoA shows advantages in handling high-dimensional datasets, there is room for future research and optimization, particularly concerning feature dimensionality reduction.

关键词： chimp optimization algorithm Feature selection High-dimensional classification data Social coevolution Sine chaotic opposition learning

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A Novel chimp optimization algorithm with Refraction Learning and Its Engineering Applications

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algorithmS 2022年第6期15卷 189-189页

作者： Zhang, Quan Du, Shiyu Zhang, Yiming Wu, Hongzhuo Duan, Kai Lin, Yanru Ningbo Univ Fac Elect Engn & Comp Sci Ningbo 315211 Peoples R China Chinese Acad Sci Ningbo Inst Mat Technol & Engn Engn Lab Adv Energy Mat Ningbo 315201 Peoples R China

The chimp optimization algorithm (ChOA) is a heuristic algorithm proposed in recent years. It models the cooperative hunting behaviour of chimpanzee populations in nature and can be used to solve numerical as well as practical engineering optimization problems. ChOA has the problems of slow convergence speed and easily falling into local optimum. In order to solve these problems, this paper proposes a novel chimp optimization algorithm with refraction learning (RL-ChOA). In RL-ChOA, the Tent chaotic map is used to initialize the population, which improves the population's diversity and accelerates the algorithm's convergence speed. Further, a refraction learning strategy based on the physical principle of light refraction is introduced in ChOA, which is essentially an Opposition-Based Learning, helping the population to jump out of the local optimum. Using 23 widely used benchmark test functions and two engineering design optimization problems proved that RL-ChOA has good optimization performance, fast convergence speed, and satisfactory engineering application optimization performance.

关键词： chimp optimization algorithm refraction learning tent chaos global optimization engineering design optimization

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Enhancing solar photovoltaic energy production prediction using diverse machine learning models tuned with the chimp optimization algorithm

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SCIENTIFIC REPORTS 2024年第1期14卷 1-18页

作者： Al-Dahidi, Sameer Alrbai, Mohammad Alahmer, Hussein Rinchi, Bilal Alahmer, Ali German Jordanian Univ Sch Appl Tech Sci Dept Mech & Maintenance Engn Amman 11180 Jordan Univ Jordan Sch Engn Dept Mech Engn Amman 11942 Jordan Al Balqa Appl Univ Fac Artificial Intelligence Dept Automated Syst Salt 19117 Jordan Tuskegee Univ Dept Mech Engn Tuskegee AL 36088 USA

Solar photovoltaic (PV) systems, integral for sustainable energy, face challenges in forecasting due to the unpredictable nature of environmental factors influencing energy output. This study explores five distinct machine learning (ML) models which are built and compared to predict energy production based on four independent weather variables: wind speed, relative humidity, ambient temperature, and solar irradiation. The evaluated models include multiple linear regression (MLR), decision tree regression (DTR), random forest regression (RFR), support vector regression (SVR), and multi-layer perceptron (MLP). These models were hyperparameter tuned using chimp optimization algorithm (ChOA) for a performance appraisal. The models are subsequently validated on the data from a 264 kWp PV system, installed at the Applied Science University (ASU) in Amman, Jordan. Of all 5 models, MLP shows best root mean square error (RMSE), with the corresponding value of 0.503, followed by mean absolute error (MAE) of 0.397 and a coefficient of determination (R2) value of 0.99 in predicting energy from the observed environmental parameters. Finally, the process highlights the fact that fine-tuning of ML models for improved prediction accuracy in energy production domain still involves the use of advanced optimization techniques like ChOA, compared with other widely used optimization algorithms from the literature.

关键词： Solar photovoltaic Energy production Prediction Machine learning Hyperparameter tuning chimp optimization algorithm

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A Novel Six-Dimensional chimp optimization algorithm-Deep Reinforcement Learning-Based optimization Scheme for Reconfigurable Intelligent Surface-Assisted Energy Harvesting in Batteryless IoT Networks

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FUTURE INTERNET 2024年第12期16卷 460-460页

作者： Shoeibi, Mehrdad Oskouei, Anita Ershadi Kaveh, Masoud Worcester Polytech Inst WPI Business Sch Worcester MA 01605 USA Stevens Inst Technol Sch Syst & Enterprises Hoboken NJ 07030 USA Aalto Univ Dept Informat & Commun Engn Espoo 02150 Finland

The rapid advancement of Internet of Things (IoT) networks has revolutionized modern connectivity by integrating many low-power devices into various applications. As IoT networks expand, the demand for energy-efficient, batteryless devices becomes increasingly critical for sustainable future networks. These devices play a pivotal role in next-generation IoT applications by reducing the dependence on conventional batteries and enabling continuous operation through energy harvesting capabilities. However, several challenges hinder the widespread adoption of batteryless IoT devices, including the limited transmission range, constrained energy resources, and low spectral efficiency in IoT receivers. To address these limitations, reconfigurable intelligent surfaces (RISs) offer a promising solution by dynamically manipulating the wireless propagation environment to enhance signal strength and improve energy harvesting capabilities. In this paper, we propose a novel deep reinforcement learning (DRL) algorithm that optimizes the phase shifts of RISs to maximize the network's achievable rate while satisfying IoT devices' energy harvesting constraints. Our DRL framework leverages a novel six-dimensional chimp optimization algorithm (6DChOA) to fine-tune the hyper-parameters, ensuring efficient and adaptive learning. The proposed 6DChOA-DRL algorithm optimizes RIS phase shifts to enhance the received power of IoT devices while mitigating interference from direct and RIS-cascaded links. The simulation results demonstrate that our optimized RIS design significantly improves energy harvesting and achievable data rates under various system configurations. Compared to benchmark algorithms, our approach achieves higher gains in harvested power, an improvement in the data rate at a transmit power of 20 dBm, and a significantly lower root mean square error (RMSE) of 0.13 compared to 3.34 for standard RL and 6.91 for the DNN, indicating more precise optimization of RIS phase shifts.

关键词： Internet of Things energy harvesting reconfigurable intelligent surfaces advanced deep reinforcement learning chimp optimization algorithm

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MCWOA Scheduler:Modified chimp-Whale optimization algorithm for Task Scheduling in Cloud Computing

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Computers, Materials & Continua 2024年第2期78卷 2593-2616页

作者： Chirag Chandrashekar Pradeep Krishnadoss Vijayakumar Kedalu Poornachary Balasundaram Ananthakrishnan School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India Center for Cyber Physical Systems School of Computer Science and EngineeringVellore Institute of TechnologyChennai600127India

Cloud computing provides a diverse and adaptable resource pool over the internet,allowing users to tap into various resources as *** has been seen as a robust solution to relevant challenges.A significant delay can hamper the performance of IoT-enabled cloud ***,efficient task scheduling can lower the cloud infrastructure’s energy consumption,thus maximizing the service provider’s revenue by decreasing user job processing *** proposed Modified chimp-Whale optimization algorithm called Modified chimp-Whale optimization algorithm(MCWOA),combines elements of the chimp optimization algorithm(COA)and the Whale optimization algorithm(WOA).To enhance MCWOA’s identification precision,the Sobol sequence is used in the population initialization phase,ensuring an even distribution of the population across the solution ***,the traditional MCWOA’s local search capabilities are augmented by incorporating the whale optimization algorithm’s bubble-net hunting and random search mechanisms into MCWOA’s position-updating *** study demonstrates the effectiveness of the proposed approach using a two-story rigid frame and a simply supported beam *** outcomes reveal that the new method outperforms the original MCWOA,especially in multi-damage detection *** excels in avoiding false positives and enhancing computational speed,making it an optimal choice for structural damage *** efficiency of the proposed MCWOA is assessed against metrics such as energy usage,computational expense,task duration,and *** simulated data indicates that the new MCWOA outpaces other methods across all *** study also references the Whale optimization algorithm(WOA),chimp algorithm(CA),Ant Lion Optimizer(ALO),Genetic algorithm(GA)and Grey Wolf Optimizer(GWO).

关键词： Cloud computing scheduling chimp optimization algorithm whale optimization algorithm

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Vehicular communication using federated learning empowered chimp optimization (FLECO) algorithm

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MULTIMEDIA TOOLS AND APPLICATIONS 2024年第29期83卷 72321-72356页

作者： Gupta, Ruchi Alam, Tanweer Abdul Kalam Tech Univ Ajay Kumar Garg Engn Coll Ghaziabad Fac Informat Technol Lucknow India Islamic Univ Madinah Fac Comp & Informat Syst Madinah Saudi Arabia

Recently in the field of vehicular communication, there has been a concentration of research on the integration of a vehicle-to-vehicle (V2V) network. With vehicle-to-vehicle (V2V) communication, users can directly exchange significant information with nearby vehicles. Typically, automobiles tend to travel at higher speeds on highways compared to roads at intersections. As a result, it is necessary to have a reliable system in place that can effectively and securely facilitate communication. In recent times, scientists have developed different methods for distributing information. However, these systems have various issues such as latency, reliability, mobility, and communication cost. Consequently, this results in a lack of dependability for real-time communication. Therefore, this study introduces a novel approach to Federated Learning (FL) by including the chimp optimization algorithm (ChOA). Federated Learning is an approach in the field of machine learning that enables multiple devices or nodes to collaboratively train a model without the need for data exchange. In the area of vehicular communication, utilization of Federated Learning can be employed to develop a predictive model that estimates the trajectory of nearby vehicles by utilizing collected data. The chimp optimization algorithm (ChOA) is designed to improve the model's efficacy. The proposed method aims to enhance the accuracy of the model's predictions regarding the conduct of nearby vehicles, while also reducing the amount of data exchanged between vehicles, by combining Federated Learning and chimp optimization termed FLECO. This method has the potential to enhance vehicular communication effectiveness and security, while also improving road safety and traffic management. Federated Learning facilitates the group control of a machine learning (ML) system by vehicles through the adjustment of model parameters. To enhance the energy efficiency of the system, the implementation of resource allocation

关键词： Federated learning Vehicle communication chimp optimization algorithm SUMO FLECO

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