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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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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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Greedy opposition-based learning for chimp optimization algorithm

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ARTIFICIAL INTELLIGENCE REVIEW 2023年第8期56卷 7633-7663页

作者： Khishe, Mohammad Imam Khomeini Marine Sci Univ Dept Elect Engn Nowshahr Iran

The chimp optimization algorithm (ChOA) is a hunting-based model and can be utilized as a set of optimization rules to tackle optimization problems. Although ChOA has shown promising results on optimization functions, it suffers from a slow convergence rate and low exploration capability. Therefore, in this paper, a modified ChOA is proposed to improve the exploration and exploitation capabilities of the ChOA. This improvement is performed using greedy search and opposition-based learning (OBL), respectively. In order to investigate the efficiency of the OBLChOA, the OBLChOA's performance is evaluated by twenty-three standard benchmark functions, ten suit tests of IEEE CEC06-2019, randomly generated landscape, and twelve real-world Constrained optimization Problems (IEEE COPs-2020) from a variety of engineering fields, including industrial chemical producer, power system, process design and synthesis, mechanical design, power-electronic, and livestock feed ration. The results are compared to benchmark optimizers, including CMA-ES and SHADE as high-performance optimizers and winners of IEEE CEC competition;standard ChOA;OBL-GWO, OBL-SSA, and OBL-CSA as the best benchmark OBL-based algorithms. OBLChOA and CMA-ES rank first and second among twenty-seven numerical test functions, respectively, with forty and eleven best results. In the 100-digit challenge, jDE100 achieves the highest score of 100, followed by DISHchain1e + 12, and OBLChOA achieves the fourth-highest score of 93. In total, eighteen state-of-the-art algorithms achieved the highest score in seven out of ten issues. Finally, OBLChOA and CMA-ES achieve the best performance in five and four real-world engineering challenges, respectively.

关键词： Metaheuristics chimp optimization algorithm Opposition-based learning Greedy search

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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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Adaptive chimp optimization algorithm with chaotic map for global numerical optimization problems

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JOURNAL OF SUPERCOMPUTING 2023年第6期79卷 6507-6537页

作者： Wang, Yiwen Liu, Hao Ding, Guiyan Tu, Liangping Univ Sci & Technol Liaoning Sch Sci Anshan 114051 Peoples R China

chimp optimization algorithm (ChOA) is a meta-heuristic algorithm inspired by individual intelligence and sexual motivation during group hunting. It is designed to speed up the convergence of the optimal solution. Because of its simplicity and low computational cost, the algorithm has been widely used to solve complex global optimization problem. But in the process of searching, it is easy to fall into the local optima, and the balance between exploitation and exploration cannot be realized well. In this paper, an adaptive chimp optimization algorithm called AChOA is proposed. Firstly, the Tent chaotic map is firstly used to initialize the chimp population to obtain a better initial solutions and improve convergence precision. Secondly, adaptive non linear convergence factor and adaptive weight are introduced in the global search stage, and the parameters vary adaptively according to the number of iterations and population diversity, so as to improve the population diversity. Thirdly, the Levy flight strategy is introduced into the position update formula to mitigate the shortcomings of ChOA algorithm, such as finding the local optima rather than the global optima, and lack of balance between the exploitation and exploration process. Finally, a comparison with 10 famous algorithms on 19 benchmark functions of the solving accuracy and convergence speed of AChOA is presented. The results show that AChOA has the advantages of fast convergence speed, high solution accuracy.

关键词： chimp optimization algorithm Tent chaotic map Adaptive parameter Levy flight

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Hybrid multi-strategy chaos somersault foraging chimp optimization algorithm research

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2023年第7期20卷 12263-12297页

作者： Yang, Xiaoru Zhang, Yumei Lv, Xiaojiao Yang, Honghong Sun, Zengguo Wu, Xiaojun Shaanxi Normal Univ Key Lab Modern Teaching Technol Minist Educ Xian Peoples R China Shaanxi Normal Univ Sch Comp Sci Xian Peoples R China Serv Technol Folk Song Key Lab Intelligent Comp Xian Peoples R China

To address the problems of slow convergence speed and low accuracy of the chimp optimization algorithm (ChOA), and to prevent falling into the local optimum, a chaos somersault foraging ChOA (CSFChOA) is proposed. First, the cat chaotic sequence is introduced to generate the initial solutions, and then opposition-based learning is used to select better solutions to form the initial population, which can ensure the diversity of the algorithm at the beginning and improve the convergence speed and optimum searching accuracy. Considering that the algorithm is likely to fall into local optimum in the final stage, by taking the optimal solution as the pivot, chimps with better adaptation at the mirror image position replace chimps from the original population using the somersault foraging strategy, which can increase the population diversity and expand the search scope. The optimization search tests were performed on 23 standard test functions and CEC2019 test functions, and the Wilcoxon rank sum test was used for statistical analysis. The CSFChOA was compared with the ChOA and other improved intelligent optimization algorithms. The experimental results show that the CSFChOA outperforms most of the other algorithms in terms of mean and standard deviation, which indicates that the CSFChOA performs well in terms of the convergence accuracy, convergence speed and robustness of global optimization in both low-dimensional and high-dimensional experiments. Finally, through the test and analysis comparison of two complex engineering design problems, the CSFChOA was shown to outperform other algorithms in terms of optimal cost. For the design of the speed reducer, the performance of the CSFChOA is 100% better than other algorithms in terms of optimal cost;and, for the design of a three-bar truss, the performance of the CSFChOA is 6.77% better than other algorithms in terms of optimal cost, which verifies the feasibility, applicability and superiority of the CSFChOA in practical e

关键词： chimp optimization algorithm cat chaotic sequence opposition-based learning somersault foraging convergence local optimum

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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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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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