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

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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Optimal Design of Microgrid Using chimp optimization algorithm

Optimal Design of Microgrid Using Chimp Optimization Algorit...

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IEEE IFAC International Conference on Automation (ICA) / 24th Congress of the Chilean-Association-of-Automatic-Control (ACCA)

作者： Kharrich, Mohammed Mohammed, Omar Hazem Kamel, Salah Aljohani, Mansour Akherraz, Mohammed Mosaad, Mohamed, I Mohammed V Univ Mohammadia Sch Engineers Dept Elect Engn Rabat Morocco Northern Tech Univ Tech Power Engn Tech Coll Mosul Iraq Aswan Univ Dept Elect Engn Aswan Egypt Yanbu Ind Coll Dept Elect & Elect Engn Yanbu Saudi Arabia

ISBN: (纸本)9781665401272

The hybrid power systems become necessary, mainly in non-electrified areas as in Africa, where millions of peoples have not access to electricity. This study solves the design problem of the microgrid systems, containing PV panels, wind turbines and battery storage system. This paper focuses to apply a recent algorithm named chimp optimization algorithm (ChOA) and comparing its performance with two algorithms called IWO and GWO. The microgrid system design is based on optimizing of net present cost function, respecting some constraints. The results showed that the recent ChOA algorithm is better than the IWO and GWO, however, the suggested system is very suitable in the Dakhla location, where the metrological conditions are suitable.

关键词： chimp optimization algorithm GWO Hybrid Power Systems IWO Microgrid Net Present Cost

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Breast DCE-MRI segmentation for lesion detection using chimp optimization algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2022年第0期204卷

作者： Si, Tapas Patra, Dipak Kumar Mondal, Sukumar Mukherjee, Prakash Bankura Unnayani Inst Engn Dept Comp Sci & Engn Bankura 722146 West Bengal India Raja Narendralal Khan Womens Coll Res Ctr Nat & Appl Sci Dept Comp Sci Midnapore 721102 West Bengal India Raja Narendralal Khan Womens Coll Dept Math Midnapore 721102 West Bengal India Hijli Coll Dept Math Kharagpur 721306 West Bengal India

The high prevalence of breast cancer in women has increased dramatically in recent times. Physician's knowledge in breast cancer diagnosis and detection using computerized algorithms for extraction and segmentation of features can help. Image segmentation is a critical component of image analysis that has a direct impact on the quality of the results. This article presents Kapur's entropy-based multilevel thresholding using chimp optimization algorithm (ChOA) to estimate optimal values for the lesion segmentation of breast DCE-MRI. An improved ChOA is also developed by incorporating Opposition based-learning (OBL) in it, termed as ChOAOBL, and applied to solve the same problem. The proposed methods are evaluated using 200 Sagittal T2-Weighted fat-suppressed DCE-MRI images of 40 patients. The proposed methods are compared with Improved ChOA (IChOA), Particle Swarm optimization (PSO), Multi-verse Optimizer (MVO), Slime Mould algorithm (SMA), Arithmetic optimization algorithm (AOA), Tunicate Swarm algorithm (TSA), Multilevel Otsu Threshold (MLOT), Conventional Markov Random Field (CMRF), Hidden Markov Random Field (HMRF), and Improved Markov Random Field (IMRF). The high sensitivity, accuracy, and Dice Efficient Coefficient (DSC) level of the proposed ChOA-based method are achieved at 90.75%, 98.24%, and 87.09% respectively. The accuracy value of 99.02%, sensitivity 95.73%, and DSC 93.25% are achieved using another proposed ChOAOBL-based segmentation method. The results are analyzed using a one-way ANOVA test followed by Tukey HSD, and Wilcoxon Signed Rank Test. We have also analyzed the overall performance using Multi Criteria Decision Making based on accuracy, precision, specificity, F-measure, sensitivity, false-positive rate, Geometric-Mean (G-mean), and DSC. The proposed methods outperform other compared methods, according to both quantitative and qualitative outcomes.

关键词： Breast DCE-MRI Segmentation Entropy Multi-level thresholding chimp optimization algorithm Opposition-based learning

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Diagnose Parkinson?s disease and cleft lip and palate using deep convolutional neural networks evolved by IP-based chimp optimization algorithm

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2022年第0期77卷 000-000页

作者： Chen, Feng Yang, Chunyan Khishe, Mohammad Wenzhou Med Univ Fac Informat Wenzhou 325035 Zhejiang Peoples R China Ctr China Normal Univ Fac Artificial Intelligence Educ Wuhan 430079 Hubei Peoples R China Imam Khomeini Marine Sci Univ Dept Elect Engn Nowshahr Iran

Speech signals often include paralinguistic features such as pathologies that impair a speaker's capability to communicate. Those cognitive symptoms have various causes depending on the disease. For example, morphological diseases like cleft lip and palate create hypernasality, while neurodegenerative conditions like Parkinson's disease cause hypokinetic dysarthria. Automatic assessment of abnormal speech supports early diagnosis or disease severity evaluation. Conventional methods rely on manually assessing single aspects like shimmer, jitter, or formant frequencies, which may not fully reflect the disease's manifestations. In this paper, we use deep convolutional neural networks (DCNNs) to recognize disordered speech. Despite DCNNs' many approved benefits, selecting the best structure is challenging. In order to overcome this issue, this research looks into using the chimp optimization algorithm (ChOA) to automatically select the optimal DCNN structure. In order to achieve the goal, three ChOA-based advancements are proposed. First, an internet protocol address-based (IPA-based) encoding method for DCNN layers employing chimp vectors is created. Then an Enfeebled layer with specified chimp vector dimensions is presented for variable-length DCNNs. Eventually, large datasets are partitioned into smaller ones and evaluated at random to recognize abnormal speech signals from patients with Parkinson's disease and cleft lip and palate. In addition to receiver operating characteristic (ROC) and precision-recall curves, five well-known metrics were used: sensitivity, specificity, accuracy, precision, F1-Score. The proposed model accurately diagnoses disordered and normal speech signals, with an accuracy of up to 96.37%, which is 1.62 more accurate than the second-best approach, i.e., VLNSGA-II.

关键词： Pathological speech Deep convolutional neural networks Parkinson?s disease Cleft lip and palate chimp optimization algorithm

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Predicting the environmental economic dispatch problem for reducing waste nonrenewable materials via an innovative constraint multi-objective chimp optimization algorithm

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JOURNAL OF CLEANER PRODUCTION 2022年 365卷

作者： Zhu, Lei Ren, Hao Habibi, Mostafa Mohammed, Khidhair Jasim Khadimallah, Mohamed Amine Xian Univ Architecture & Technol Sch Publ Adm Xian 710055 Shaanxi Peoples R China Xijing Univ Sch Humanities & Educ Xian 710123 Shaanxi Peoples R China Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Duy Tan Univ Fac Elect Elect Engn Da Nang 550000 Vietnam Sharif Univ Technol Ctr Excellence Design Robot & Automat Dept Mech Engn Azadi AvePOB 11365-9567 Tehran Iran Al Mustaqbal Univ Coll Air Conditioning & Refrigerat Tech Engn Dept Babylon 51001 Iraq Prince Sattam Bin Abdulaziz Univ Coll Engn Civil Engn Dept Al Kharj 16273 Saudi Arabia Univ Carthage Polytech Sch Tunisia Lab Syst & Appl Mech Tunis Tunisia

The usage of conventional fossil fuels has aided fast economic growth while also having negative consequences, such as increased global warming and the destruction of the ecosystem. This paper proposes a novel swarm-based metaheuristic method called chimp optimization algorithm (ChOA) to tackle the environmental, economic dispatch issue and reducing the waste nonrenewable materials. In this regard, two objective functions named fuel cost function and emission cost function are proposed. Unique constrained handling also solves the challenge of multi-objective optimization. Standard IEEE 30 bus with six generators and a 10-unit system are used to demonstrate the usefulness of ChOA. The result of ChOA is compared with Individual Best Memory Penalty Factor Grey Wolf Optimizer (IBMPF-GWO), Improved Whale Trainer (IWT), Chaotic Biogeography-Based Optimizer (CBBO), Non-Linear Migration BBO (NLBBO), Hybrid Gravitational Search algorithm Particle Swarm optimization (GSAPSO), Covariance Matrix Adaptation Evolution Strategy (CMA-ES), Differential Evolution-Crossover Quantum Particle Swarm optimization (DE-CQPSO), Salp Swarm algorithm (SSA), Dragonfly algorithm (DA), and Fuzzy Grasshopper optimization algorithm (FGOA) to confirm its efficiency. For both single -and multi-objective optimization, ChOA's assessment index and convergence rate are superior to other bench-mark algorithms, regardless of whether the goal is to reduce emissions or to reduce fuel costs. The efficacy and robustness of the ChOA in handling environmental economic dispatch problems have been shown by discovering a good compromise value.

关键词： chimp optimization algorithm Carbon emission reduction Multi-objective optimization Environmental economic dispatch

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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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Classification of underwater acoustical dataset using neural network trained by chimp optimization algorithm

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APPLIED ACOUSTICS 2020年 157卷 107005-107005页

作者： Khishe, M. Mosavi, M. R. Imam Khomeini Marine Sci Univ Dept Elect Engn Nowshahr Iran Iran Univ Sci & Technol Dept Elect Engn Tehran *** Iran

Due to the variability of the radiated signal of the underwater targets, the classification of the underwater acoustical dataset is a challenging problem in the real world application. In this paper, to classify underwater acoustical targets, first, a new meta-heuristic chimp optimization algorithm (ChOA) inspired by chimp hunting behaviour is developed for training an Artificial Neural Network (ANN). Second, a new underwater acoustical dataset is developed using passive propeller acoustic data collected in a laboratory. To evaluate the proposed classifier, this algorithm is compared to the Ion Motion algorithm (IMA), Gray Wolf optimization (GWO), and a hybrid algorithm. Measured metrics are convergence speed, the possibility of trapping in local minimum and classification accuracy. The results show that the newly proposed algorithm in most cases provides better or comparable performance compared to the other benchmark algorithms. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Classification Underwater acoustical dataset Multi-Layer Perceptron Neural Networks chimp optimization algorithm

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Enhancing Global Maximum Power Point of Solar Photovoltaic Strings under Partial Shading Conditions Using chimp optimization algorithm

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ENERGIES 2021年第14期14卷 4086页

作者： Nagadurga, Timmidi Narasimham, Pasumarthi Venkata Ramana Lakshmi Vakula, V. S. Devarapalli, Ramesh Marquez, Fausto Pedro Garcia Lakireddy Bali Reddy Coll Engn Dept Elect & Elect Engn Mylavaram 521230 India Jawaharlal Nehru Technol Univ Univ Coll Engn Dept Elect & Elect Engn Kakinada 533003 India Velagapudi Ramakrishna Siddhartha Engn Coll Dept Elect & Elect Engn Kanuru 520007 India JNTUK Univ Coll Engn Dept Elect & Elect Engn Vizianagaram 535003 India Birsa Inst Technol Sindri Dept Elect Engn Dhanbad 828123 Bihar India Univ Castilla La Mancha Ingenium Res Grp Ciudad Real 13071 Spain

This paper proposes the application of a metaheuristic algorithm inspired by the social behavior of chimps in nature, called chimp optimization algorithm (ChOA), for the maximum power point tracking of solar photovoltaic (PV) strings. In this algorithm, the chimps hunting process is mathematically articulated, and new mechanisms are designed to perform the exploration and exploitation. To evaluate the ChOA, it is applied to some fixed dimension benchmark functions and engineering problem application of tracking maximum power from solar PV systems under partial shading conditions. Partial shading condition is a common problem that appears in the solar PV modules installed in domestic areas. This shading alters the power developed by the solar PV panel, and exhibits multiple peaks on the power variation with voltage (P-V) characteristic curve. The dynamics of the solar PV system have been considered, and the mathematical model of a single objective function has been framed for tuning the optimal control parameter with the suggested algorithm. Implementing various practical shading patterns of solar PV systems with the ChOA algorithm has shown improved solar power point tracking performance compared to other algorithms in the literature.

关键词： solar photovoltaic system maximum power point tracking chimp optimization algorithm grey wolf optimization availability reliability

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Real-time COVID-19 diagnosis from X-Ray images using deep CNN and extreme learning machines stabilized by chimp optimization algorithm

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2021年第0期68卷 102764-102764页

作者： Hu Tianqing Khishe, Mohammad Mohammadi, Mokhtar Parvizi, Gholam-Reza Karim, Sarkhel H. Taher Rashid, Tarik A. Henan Polytech Univ Coll Comp Sci & Technol Jiaozuo Henan Peoples R China Imam Khomeini Marine Sci Univ Dept Elect Engn Nowshahr Iran Lebanese French Univ Dept Informat Technol Erbil Krg Iraq Univ Isfahan Fac Foreign Languages Esfahan Iran Univ Halabja Coll Sci Comp Dept Halabja Iraq Univ Kurdistan Hewler Comp Sci & Engn Dept Erbil Krg Iraq

Real-time detection of COVID-19 using radiological images has gained priority due to the increasing demand for fast diagnosis of COVID-19 cases. This paper introduces a novel two-phase approach for classifying chest X-ray images. Deep Learning (DL) methods fail to cover these aspects since training and fine-tuning the model's parameters consume much time. In this approach, the first phase comes to train a deep CNN working as a feature extractor, and the second phase comes to use Extreme Learning Machines (ELMs) for real-time detection. The main drawback of ELMs is to meet the need of a large number of hidden-layer nodes to gain a reliable and accurate detector in applying image processing since the detective performance remarkably depends on the setting of initial weights and biases. Therefore, this paper uses chimp optimization algorithm (ChOA) to improve results and increase the reliability of the network while maintaining real-time capability. The designed detector is to be benchmarked on the COVID-Xray-5k and COVIDetectioNet datasets, and the results are verified by comparing it with the classic DCNN, Genetic algorithm optimized ELM (GA-ELM), Cuckoo Search optimized ELM (CS-ELM), and Whale optimization algorithm optimized ELM (WOA-ELM). The proposed approach outperforms other comparative benchmarks with 98.25 % and 99.11 % as ultimate accuracy on the COVID-Xray-5k and COVIDetectioNet datasets, respectively, and it led relative error to reduce as the amount of 1.75 % and 1.01 % as compared to a convolutional CNN. More importantly, the time needed for training deep ChOA-ELM is only 0.9474 milliseconds, and the overall testing time for 3100 images is 2.937 s.

关键词： COVID-19 Real-time chimp optimization algorithm Deep convolutional neural networks Chest X-ray images

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