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A many objective chimp optimization algorithm to de-cluster earthquake catalogs in space time domain

SOFT COMPUTING 2024年第6期28卷 5287-5320页

作者： Sharma, Ashish Nanda, Satyasai Jagannath Malaviya Natl Inst Technol Jaipur Dept Elect & Commun Engn Jaipur 302017 Rajasthan India

Earthquakes are complex phenomena that generate various forms of clusters that are highly correlated in the space and time domains. To study the dynamic properties of seismicity and estimate risk, the earthquake catalog needs to be separated into clustered and background events. Here, seismicity de-clustering is formulated as a binary many-objective optimization problem. A swarm-based many-objective chimp optimization algorithm (MaOChOA) is proposed to segregate the earthquake catalog into aftershocks and background events. The reference point-based leader selection strategy is adopted to update the solution. The proposed MaOChOA is evaluated on many objective benchmark test functions. The results are compared with existing many-objective techniques using performance metrics such as generational distance, inverse generational distance, spacing metric, hyper-volume distance, and runtime. Further, de-clustering the earthquake catalog is performed using a binary version of many-objective chimp optimization algorithms (BMaChOA), where the sigmoid function is used in the position update mechanism. The BMaChOA is applied to 32-year historical earthquake catalogs of the Japan, California, Indonesia, and Himalayan regions. The potential of the proposed algorithm is reported by comparing it to five benchmark de-clustering techniques. The results are validated using the epicenter plot, inter-event time Vs. inter-event distance plot, cumulative plot, lambda-plot, and statistical parameters such as the m-Morisita index, coefficient of variance, average nearest neighbor, and nearest neighbor distance.

关键词： Many objective optimization chimp optimization algorithm Reference point Seismicity de-clustering Coefficient of variance

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Enhancing Robot Path Planning Through a Twin-Reinforced chimp optimization algorithm and Evolutionary Programming algorithm

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IEEE ACCESS 2024年 12卷 170057-170078页

作者： Zhang, Yang Zhang, Hu Beijing AIQI Technol Co LTD Beijing Peoples R China

The importance of efficient path planning (PP) cannot be overstated in the domain of robots, as it involves the utilization of intelligent algorithms to determine the optimal trajectory for robot to navigate between two given points. The main target of PP is to determine potential trajectories for robot operating in a complex environment containing various obstacles. The implementation of these movements should facilitate robot in traversing a path without encountering any collisions, starting from its initial location and reaching the intended destination. In order to address the challenges associated with robot PP, this study applies the chimp optimization algorithm (CHOA) as a local searching (LS) technique and the evolutionary programming algorithm (EPA) to enhance the potential route discovered via a collection of LSs. In order to address CHOA's tendency to converge to local minima, a new updating technique called twin-reinforced (TR) is developed. In order to assess the effectiveness of the TRCHOA, we conducted a comparative analysis with other widely used meta-heuristic algorithms that are typically employed for solving robot PP problems. Additionally, we included the conventional probabilistic roadmap method (PRM) in our evaluation. We evaluated the planning performances of these algorithms on a standardized set of benchmark problems. Our findings indicate that the TRCHOA outperforms the other algorithms in terms of its planning performance. The evaluation of planning effectiveness encompasses several key criteria, namely path length, consistency of scheduled paths, time complexity, and rate of success. The experiments conducted in this study provide evidence of the statistically significant value of the enhancements obtained through the implementation of the proposed method. The findings derived from the TRCHOA provide compelling evidence of its capacity to accurately determine the most optimal route within the specified test map.

关键词： Robots optimization Collision avoidance Navigation Convergence Trajectory Animals Evolutionary computation Path planning chimp optimization algorithm evolutionary programming path planning robot twin-reinforced technique

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Enhancing the chimp optimization algorithm to evolve deep LSTMs for accounting profit prediction using adaptive pair reinforced technique

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EVOLVING SYSTEMS 2024年第4期15卷 1159-1178页

作者： Yang, Chengchen Wu, Tong Zeng, Lingzhuo Chengdu Univ Technol Sch Comp & Network Secur Chengdu 610000 Sichuan Peoples R China

Accurately predicting accounting profit (PAP) plays a vital role in financial analysis and decision-making for businesses. The analysis of a business's financial achievements offers significant insights and aids in the formulation of strategic plans. This research paper focuses on improving the chimp optimization algorithm (CHOA) to evolve deep long short-term memory (LSTM) models specifically for financial accounting profit prediction. The proposed hybrid approach combines CHOA's global search capabilities with deep LSTMs' sequential modeling abilities, considering both the global and temporal aspects of financial data to enhance prediction accuracy. To overcome CHOA's tendency to get stuck in local minima, a novel updating technique called adaptive pair reinforced (APR) is introduced, resulting in APRCHOA. In addition to well-known conventional prediction models, this study develops five deep LSTM-based models, namely conventional deep LSTM, CHOA (deep LSTM-CHOA), adaptive reinforcement-based genetic algorithm (deep LSTM-ARGA), marine predator algorithm (deep LSTM-MPA), and adaptive reinforced whale optimization algorithm (deep LSTM-ARWOA). To comprehensively evaluate their effectiveness, the developed deep LSTM-APRCHOA models are assessed using statistical error metrics, namely root mean square error (RMSE), bias, and Nash-Sutcliffe efficiency (NSEF). In the validation set, at a lead time of 1 h, the NSEF values for LSTM, LSTM-MPA, LSTM-CHOA, LSTM-ARGA, LSTM-ARWOA, and deep LSTM-APRCHOA were 0.9100, 0.9312, 0.9350, 0.9650, 0.9722, and 0.9801, respectively. The results indicate that among these models, deep LSTM-APRCHOA demonstrates the highest accuracy for financial profit prediction.

关键词： Prediction Deep long short-term memory Accounting profit chimp optimization algorithm Adaptive pair reinforced technique

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Evolving chimp optimization algorithm using quantum mechanism for engineering applications: a case study on fire detection

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JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING 2024年第5期11卷 143-163页

作者： Zhang, Ziyang Khishe, Mohammad Qian, Leren Martin, Diego Abualigah, Laith Ghazal, Taher M. Nanyang Technol Univ Sch Civil & Environm Engn 50 Nanyang Ave Singapore 639798 Singapore Univ Halabja Comp Sci Dept Halabja 46018 Iraq Appl Sci Private Univ Appl Sci Res Ctr Amman 11937 Jordan Yuan Ze Univ Innovat Ctr Artificial Intelligence Applicat Taoyuan City 32003 Taiwan Arizona State Univ Sch Comp & Augmented Intelligence Tempe AZ 85281 USA Univ Politecn Madrid ETSI Telecomunicac Ave Complutense 30 Madrid 28040 Spain Al Al Bayt Univ Comp Sci Dept Mafraq 25113 Jordan Middle East Univ MEU Res Unit Amman 11831 Jordan Chitkara Univ Inst Engn & Technol Ctr Res Impact & Outcome Rajpura 140401 Punjab India Univ Tabuk Artificial Intelligence & Sensing Technol AIST Res Tabuk 71491 Saudi Arabia Univ Kebangsaan Malaysia Fac Informat Sci & Technol Ctr Cyber Secur Bangi 43600 Selangor Malaysia Appl Sci Univ Coll Arts & Sci POB 5055 Manama Bahrain

This paper introduces the Quantum chimp optimization algorithm (QU-ChOA), which integrates the chimp optimization algorithm (ChOA) with quantum mechanics principles to enhance optimization capabilities. The study evaluates QU-ChOA across diverse domains, including benchmark tests, the IEEE CEC-06-2019 100-Digit Challenge, real-world optimization problems from IEEE-CEC-2020, and dynamic scenarios from IEEE-CEC-2022. Key findings highlight QU-ChOA's competitive performance in both unimodal and multimodal functions, achieving an average success rate (SR) of 88.98% across various benchmark functions. QU-ChOA demonstrates robust global search abilities, efficiently finding optimal solutions with an average fitness evaluations (AFEs) of 14 012 and an average calculation duration of 58.22 units in fire detection applications. In benchmark tests, QU-ChOA outperforms traditional algorithms, including achieving a perfect SR of 100% in the IEEE CEC-06-2019 100-Digit Challenge for several functions, underscoring its effectiveness in complex numerical optimization. Real-world applications highlight QU-ChOA's significant improvements in objective function values for industrial processes, showcasing its versatility and applicability in practical scenarios. The study identifies gaps in existing optimization strategies and positions QU-ChOA as a novel solution to these challenges. It demonstrates QU-ChOA's numerical advancements, such as a 20% reduction in AFEs compared to traditional methods, illustrating its efficiency and effectiveness across different optimization tasks. These results establish QU-ChOA as a promising tool for addressing intricate optimization problems in diverse fields. Graphical Abstract

关键词： fire detection chimp optimization algorithm engineering application quantum mechanism

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Diagnosis of tomato leaf disease using OTSU multi-threshold image segmentation-based chimp optimization algorithm and LeNet-5 classifier

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JOURNAL OF PLANT DISEASES AND PROTECTION 2024年第6期131卷 2221-2236页

作者： Ramesh Babu, Padamata Srikrishna, Atluri Gera, Venkateswara Rao Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Engn Vaddeswaram Andhra Pradesh India RVR & JC Coll Engn Dept Informat Technol Guntur Andhra Pradesh India Kallam Haranadhareddy Inst Technol Dept Comp Sci & Engn Guntur Andhra Pradesh India

The ability to diagnose crop diseases is crucial which affects the crop yield and agricultural productivity. The primary area of study for crop disease diagnostics now centres on deep learning techniques. However, deep learning techniques require high computational power, which limits their portability. This paper used the variation of convolution neural network model LeNet-5 for classification and the Otsu multi-thresholding method with an optimization algorithm for the segmentation of the images. The classifier is trained using the Plant Village dataset which contains images of tomato leaves with various types of diseases. This method is highlighted for its high accuracy in disease identification. Additionally, to assess its ability to perform well with new, unseen data, real-time diseased images are tested in the proposed method. This can ensure that the method can effectively generalize beyond the initial dataset it was trained on. The performance using the dataset can be calculated using precision, recall, F1-score, and accuracy. These are compared with three existing approaches Xception, ResNet50, and VGG16 from this comparison the proposed approach gives the best accuracy for classification.

关键词： LeNet-5 Segmentation Classification Multi-thresholding chimp optimization algorithm

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Hybrid Modified chimp optimization algorithm and Reinforcement Learning for Global Numeric optimization

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Journal of Bionic Engineering 2023年第6期20卷 2896-2915页

作者： Mohammad ShDaoud Mohammad Shehab Laith Abualigah Cuong-Le Thanh College of Engineering Al Ain University112612 Abu DhabiUnited Arab Emirates College of Computer Sciences and Informatics Amman Arab UniversityAmman 11953Jordan Center for Engineering Application Technology Solutions Ho Chi Minh City Open UniversityHo Chi Minh City 700000Vietnam

chimp optimization algorithm(ChOA)is one of the most efficient recent optimization algorithms,which proved its ability to deal with different problems in various ***,ChOA suffers from the weakness of the local search technique which leads to a loss of diversity,getting stuck in a local minimum,and procuring premature *** response to these defects,this paper proposes an improved ChOA algorithm based on using Opposition-based learning(OBL)to enhance the choice of better solutions,written as ***,utilizing Reinforcement Learning(RL)to improve the local research technique of OChOA,called *** way effectively avoids the algorithm falling into local *** performance of the proposed RLOChOA algorithm is evaluated using the Friedman rank test on a set of CEC 2015 and CEC 2017 benchmark functions problems and a set of CEC 2011 real-world *** results and statistical experiments show that RLOChOA provides better solution quality,convergence accuracy and stability compared with other state-of-the-art algorithms.

关键词： chimp optimization algorithm Reinforcement learning Disruption operator Opposition-based learning CEC 2011 real-world problems CEC 2015 and CEC 2017 benchmark functions problems

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Recent Advances of chimp optimization algorithm:Variants and Applications

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Journal of Bionic Engineering 2023年第6期20卷 2840-2862页

作者： Mohammad Sh.Daoud Mohammad Shehab Laith Abualigah Mohammad Alshinwan Mohamed Abd Elaziz Mohd Khaled Yousef Shambour Diego Oliva Mohammad AAlia Raed Abu Zitar College of Engineering Al Ain University112612Abu DhabiUnited Arab Emirates College of Computer Sciences and Informatics Amman Arab UniversityAmman11953Jordan Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl Al-Bayt UniversityMafraq25113Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman19328Jordan MEU Research Unit Middle East UniversityAmman11831Jordan Applied Science Research Center Applied Science Private UniversityAmman11931Jordan School of Computer Sciences Universiti Sains Malaysia11800Pulau PinangMalaysia Faculty of Information Technology Applied Science Private UniversityAmman 11931Jordan Department of Mathematics Faculty of ScienceZagazig UniversityZagazig 7120001Egypt The Custodian of the Two Holy Mosques Institute for Haj and Umrah Research Umm Al-Qura UniversityMakkah 24382Saudi Arabia Depto.de Innovacio'n Basada en la Informacio'n y el Conocimiento Universidad de GuadalajaraCUCEI45129 GuadalajaraMexico Cyber Security Department Faculty of Science and Information TechnologyAl Zaytoonah University of JordanAmman 11733Jordan Sorbonne Center of Artificial Intelligence Sorbonne University-Abu Dhabi38044 Abu DhabiUnited Arab Emirates School of Engineering and Technology Sunway University Malaysia27500Petaling JayaMalaysia

chimp optimization algorithm(ChOA)is one of the recent metaheuristics swarm intelligence *** has been widely tailored for a wide variety of optimization problems due to its impressive characteristics over other swarm intelligence methods:it has very few parameters,and no derivation information is required in the initial ***,it is simple,easy to use,flexible,scalable,and has a special capability to strike the right balance between exploration and exploitation during the search which leads to favorable ***,the ChOA has recently gained a very big research interest with tremendous audiences from several domains in a very short ***,in this review paper,several research publications using ChOA have been overviewed and ***,introductory information about ChOA is provided which illustrates the natural foundation context and its related optimization conceptual *** main operations of ChOA are procedurally discussed,and the theoretical foundation is ***,the recent versions of ChOA are discussed in detail which are categorized into modified,hybridized,and paralleled *** main applications of ChOA are also thoroughly *** applications belong to the domains of economics,image processing,engineering,neural network,power and energy,networks,*** of ChOA is also *** review paper will be helpful for the researchers and practitioners of ChOA belonging to a wide range of audiences from the domains of optimization,engineering,medical,data mining,and *** well,it is wealthy in research on health,environment,and public ***,it will aid those who are interested by providing them with potential future research.

关键词： Artificial intelligence Nature-inspired optimization algorithms chimp optimization algorithm optimization problems

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IoT device type identification using training deep quantum neural networks optimized with a chimp optimization algorithm for enhancing IoT security

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JOURNAL OF HIGH SPEED NETWORKS 2024年第2期30卷 191-201页

作者： Shirley, C. P. Kumar, Jaydip Rane, Kantilal Pitambar Kumar, Narendra Rani, Deevi Radha Harshitha, Kuntamukkula Tiwari, Mohit Karunya Inst Technol & Sci Dept Comp Sci & Engn Coimbatore Tamil Nadu India Sanskriti Univ Dept Comp Sci & Engn Mathura UP India Bharati Vidyapeeth Coll Engn Dept Elect & Telecommun Engn Navi Mumbai Maharashtra India RNS Inst technol Dept Elect & Commun Engn Bengaluru India VFSTR Deemed To Be Univ Dept Adv CSE Guntur Andhra Pradesh India Koneru Lakshmaiah Educ Fdn Sch Architecture Vaddeswaram India Bharati Vidyapeeths Coll Engn Dept Comp Sci & Engn Delhi India

IoT networks can be defined as groups of physically connected things and devices that can connect to the Internet and exchange data with one another. Since enabling an increasing number of internets of things devices to connect with their networks, organizations have become more vulnerable to safety issues and attacks. A major drawback of previous research is that it can find out prior seen types only, also any new device types are considered anomalous. In this manuscript, IoT device type detection utilizing Training deep quantum neural networks optimized with a chimp optimization algorithm for enhancing IOT security (IOT-DTI-TDQNN-COA-ES) is proposed. The proposed method entails three phases namely data collection, feature extraction and detection. For Data collection phase, real network traffic dataset from different IoT device types are collected. For feature mining phase, the internet traffic features are extracted through automated building extraction (ABE) method. IoT device type identification phase, Training deep quantum neural networks (TDQNN) optimized with chimp optimization algorithm (COA) is utilized to detect the category of IoT devices as known and unknown device. IoT network is implemented in Python. Then the simulation performance of the proposed IOT-DTI-TDQNN-COA-ES method attains higher accuracy as26.82% and 23.48% respectively, when compared with the existing methods.

关键词： IOT security device type identification training deep quantum neural networks chimp optimization algorithm

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Recalling-Enhanced Recurrent Neural Network optimized with chimp optimization algorithm based speech enhancement for hearing aids

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INTELLIGENT DECISION TECHNOLOGIES-NETHERLANDS 2024年第1期18卷 123-134页

作者： Rai, Rahul R. Mathivanan, M. VTU SJB Inst Technol Dept Elect & Commun Engn Belagavi India ACS Coll Engn Dept Elect & Commun Engn Bengaluru Karnataka India

Background noise often distorts the speech signals obtained in a real-world environment. This deterioration occurs in certain applications, like speech recognition, hearing aids. The aim of Speech enhancement (SE) is to suppress the unnecessary background noise in the obtained speech signal. The existing approaches for speech enhancement (SE) face more challenges like low Source-distortion ratio and memory requirements. In this manuscript, Recalling-Enhanced Recurrent Neural Network (R-ERNN) optimized with chimp optimization algorithm based speech enhancement is proposed for hearing aids (R-ERNN-COA-SE-HA). Initially, the clean speech and noisy speech are amassed from MS-SNSD dataset. The input speech signals are encoded using vocoder analysis, and then the Sample RNN decode the bit stream into samples. The input speech signals are extracted using Ternary pattern and discrete wavelet transforms (TP-DWT) in the training phase. In the enhancement stage, R-ERNN forecasts the associated clean speech spectra from noisy speech spectra, then reconstructs a clean speech waveform. chimp optimization algorithm (COA) is considered for optimizing the R-ERNN which enhances speech. The proposed method is implemented in MATLAB, and its efficiency is evaluated under some metrics. The R-ERNN-COA-SE-HA method provides 23.74%, 24.81%, and 19.33% higher PESQ compared with existing methods, such as RGRNN-SE-HA, PACDNN-SE-HA, ARN-SE-HA respectively.

关键词： Speech enhancement hearing aids MS-SNSD dataset ternary pattern and discrete wavelet transforms Recalling-Enhanced Recurrent Neural Network chimp optimization algorithm

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SCChOA:Hybrid Sine-Cosine chimp optimization algorithm for Feature Selection

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Computers, Materials & Continua 2023年第12期77卷 3057-3075页

作者： Shanshan Wang Quan Yuan Weiwei Tan Tengfei Yang Liang Zeng School of Electrical and Electronic Engineering Hubei University of TechnologyWuhan430068China Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System Hubei University of TechnologyWuhan430068China Xiangyang Industrial Institute of Hubei University of Technology Xiangyang441100China

Feature Selection(FS)is an important problem that involves selecting the most informative subset of features from a dataset to improve classification ***,due to the high dimensionality and complexity of the dataset,most optimization algorithms for feature selection suffer from a balance issue during the search ***,the present paper proposes a hybrid Sine-Cosine chimp optimization algorithm(SCChOA)to address the feature selection *** this approach,firstly,a multi-cycle iterative strategy is designed to better combine the Sine-Cosine algorithm(SCA)and the chimp optimization algorithm(ChOA),enabling a more effective search in the objective ***,an S-shaped transfer function is introduced to perform binary transformation on ***,the binary SCChOA is combined with the K-Nearest Neighbor(KNN)classifier to form a novel binary hybrid wrapper feature selection *** evaluate the performance of the proposed method,16 datasets from different dimensions of the UCI repository along with four evaluation metrics of average fitness value,average classification accuracy,average feature selection number,and average running time are ***,seven state-of-the-art metaheuristic algorithms for solving the feature selection problem are chosen for *** results demonstrate that the proposed method outperforms other compared algorithms in solving the feature selection *** is capable of maximizing the reduction in the number of selected features while maintaining a high classification ***,the results of statistical tests also confirm the significant effectiveness of this method.

关键词： Metaheuristics chimp optimization algorithm sine-cosine algorithm feature selection and classification

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