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Medical Image Segmentation using PCNN based on Multi-feature Grey Wolf Optimizer bionic algorithm

Journal of bionic Engineering 2021年第3期18卷 711-720页

作者： Xue Wang Zhanshan Li Heng Kang Yongping Huang Di Gai College of Computer Science and Technology Jilin UniversityChangchun 130012China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin UniversityChangchun 130012China ZICT Technology Co. Ltd.Shenzhen 518063China

Medical image segmentation is a challenging task especially in multimodality medical image *** this paper,an improved pulse coupled neural network based on multiple hybrid features grey wolf optimizer(MFGWO-PCNN)is proposed for multimodality medical image ***,a two-stage medical image segmentation method based on bionic algorithm is presented,including image fusion and image *** image fusion stage fuses rich information from different modalities by utilizing a multimodality medical image fusion model based on maximum energy *** the stage of image segmentation,an improved PCNN model based on MFGWO is proposed,which can adaptively set the parameters of PCNN according to the features of the *** modalities of FLAIR and TIC brain MRIs are applied to verify the effectiveness of the proposed MFGWO-PCNN *** experimental results demonstrate that the proposed method outperforms the other seven algorithms in subjective vision and objective evaluation indicators.

关键词： grey wolf optimizer pulse coupled neural network bionic algorithm medical image segmentation

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Emission source tracing based on bionic algorithm mobile sensors with artificial olfactory system

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ROBOTICA 2022年第4期40卷 976-996页

作者： Ma, Denglong Mao, Weigao Tan, Wei Gao, Jianmin Zhang, Zaoxiao Xie, Yunchuan Xi An Jiao Tong Univ Sch Mech Engn Xian Peoples R China Xi An Jiao Tong Univ Sch Chem Engn & Technol Xian Peoples R China Xi An Jiao Tong Univ Sch Chem Xian Peoples R China

The leakage of hazardous chemicals and toxic volatile substances in the atmosphere may cause serious consequences such as explosion and poisoning. To identify the unknown leakage locations and gas compositions, a mobile robot system to trace the leak source in the outdoor was investigated. First, two bionic searching algorithms, Zigzag and Silkworm algorithms, were tested with outdoor experiments for locating the leak source. The results showed that the locating errors of these two algorithms were within 0.5 m in 10 by 20 m search space, but the failing ratio of Zigzag and Silkworm algorithm was still high (about 40-50%). Therefore, an improved tracing algorithm combining the Silkworm and Zigzag algorithm, called as zigzag-Silkworm algorithm, was proposed. Compared with Silkworm and Zigzag algorithms, zigzag-Silkworm algorithm had a higher success ratio of 80% in outdoor source tracing tests, and the searching efficiency was enhanced, the efficiency parameter L: L ( 0 ) has improved from 2.58 for Silkworm and 2.66 for Zigzag to 2.17 for zigzag-Silkworm. Then, in order to identify the composition of the leaked gases during the source tracing, an artificial olfaction system (AOS) based on the gas sensor array and support vector machine was set on the mobile robot. The test results in the source tracing experiments with ammonia and ethanol emissions indicated that the recognition accuracy of emission gases reached to 99% with AOS equipped on the robot. Therefore, the mobile robot system equipped with the zigzag-Silkworm algorithm and the AOS is feasible to trace the leakage source and identify the emission composition in the outdoor leakage event with good performance in efficiency and accuracy although some underlying problems still need to be addressed in future work.

关键词： source tracing mobile sensor bionic algorithm sensor array AOS

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Research on wireless sensor location technology for biologic signal measuring based on intelligent bionic algorithm

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PEER-TO-PEER NETWORKING AND APPLICATIONS 2021年第4期14卷 2495-2500页

作者： Jiang, Binbin Nanyang Inst Technol Sch Software Nanyang 473000 Henan Peoples R China

Biological signal measurement system based on wireless sensor network is a combination of traditional medical monitor and modern communication technology. It is of great significance for clinical application and the development of medical instruments, especially in family medical treatment. The application of intelligent bionic algorithm in wireless sensor network node location has become a hot topic in academic research. Traditional particle swarm optimization (PSO), as a common method to solve optimization problems, has great advantages in finding the optimal solution iteratively. However, the convergence speed of PSO cannot be adjusted dynamically according to the operation degree of the algorithm, therefore it is easy to go into the situation of finding the local optimal solution. To solve these above problems, this paper proposes a DV-Hop localization algorithm based on particle swarm bionic optimization, which improves the performance of traditional PSO algorithm from three aspects: population selection, inertia weight and learning factor. The simulation results show that, the algorithm can adjust the convergence speed dynamically, and jump out of the local optimal dilemma to the maximum extent, which improves the iterative accuracy of the algorithm for the biologic signal measuring system.

关键词： Particle swarm optimization bionic algorithm Wireless sensor network Biologic signal measuring

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An Original bionic algorithm: Interdependent Balance algorithm 2

An Original Bionic Algorithm: Interdependent Balance Algorit...

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IEEE 2nd International Conference on Electronic Information and Communication Technology (ICEICT)

作者： Hao, Haiqiang Luo, Yabo Wuhan Univ Technol Sch Mech & Elect Engn Wuhan Peoples R China

ISBN: (纸本)9781538692981

bionic algorithm is the bionics applied in the field of algorithm. It is an optimization algorithm which is realized by simulating the existing behavior mechanism of nature. From the viewpoint of system complexity, the earth ecosystem involving millions of organisms has an amazing complexity, and thus it is almost impossible to obtain a satisfactory solution by using mathematical analytical methods to achieve a stable balance among these organisms. However, nature realizes the steady dynamic balance of biological system by the simple and smart ecological balance mechanism. Through simulating ecological balance mechanism, this paper proposes a new bionic algorithm: Interdependent Balance algorithm (IBA), in which the design variables are regarded as the population species and population size is to correspond to the value of design variable. The convergence of IBA is proved by mathematical analytic method. Accordingly, the self-growth function, interdependent function, resource constraint, and algorithm rules have been constructed for IBA. The algorithmic comparison tests demonstrated the reliability and superiority of IBA, which is easy to realize and especially effective to solve large size problems of complex system optimization.

关键词： bionic algorithm optimization NP-hard problem complex system constraint satisfaction problem

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Exploration of Financial Market Credit Scoring and Risk Management and Prediction Using Deep Learning and bionic algorithm

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JOURNAL OF GLOBAL INFORMATION MANAGEMENT 2022年第9期30卷 1页

作者： Du, Peng Shu, Hong Shaanxi Univ Sci & Technol Sch Econ & Management Xian Peoples R China Shaanxi Polytech Inst Dept Publ Courses Xian Peoples R China

The purpose is to effectively manage the financial market, comprehensively assess personal credit, and reduce the risk of financial enterprises. Given the systemic risk problem caused by the lack of credit scoring in the existing financial market, a credit scoring model is put forward based on the deep learning network. The proposed model uses RNN (recurrent neural network) and BRNN (bidirectional recurrent neural network) to avoid the limitations of shallow models. Afterward, to optimize path analysis, bionic optimization algorithms are introduced, and an integrated deep learning model is proposed. Finally, a financial credit risk management system using the integrated deep learning model is proposed. The probability of default or overdue customers is predicted through verification on three real credit data sets, thus realizing the credit risk management for credit customers.

关键词： bionic algorithm Deep Learning Financial Market Integrated Model Risk Management

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An efficient multi-threshold image segmentation method for COVID-19 images using reinforcement learning-based enhanced sand cat algorithm

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JOURNAL OF SUPERCOMPUTING 2025年第1期81卷 1-45页

作者： Hu, Kun Mo, Yuanbin Guangxi Minzu Univ Sch Artificial Intelligence Nanning 530006 Peoples R China Guangxi Minzu Univ Guangxi Key Lab Hybrid Computat & IC Design Anal Nanning 530006 Peoples R China

Since late 2019, coronavirus disease 2019 (COVID-19) has been spreading globally, presenting a significant threat to human lives and health, and exerting a profound impact on worldwide economic development. Due to the highly contagious nature of COVID-19, precise and prompt diagnosis has become paramount. The effective and rapid identification of COVID-19 through computed tomography (CT) images has thus garnered substantial interest, prompting scientists to propose various segmentation methods aimed at improving the diagnostic accuracy of CT images. Drawing from these foundations, the study introduces an innovative multilevel threshold segmentation method known as the Reinforcement Learning-based Enhanced Sand Cat algorithm (QLSCSO). QLSCSO represents a novel optimization algorithm distinguished by its remarkable convergence accuracy and the capacity to escape local optima. The introduction of this optimizer incorporates reinforcement learning methodologies into the population iteration process of heuristic techniques. In the algorithm's update phase, a hybrid model and three distinct mutation strategies are employed to enhance its capability to overcome local optima. Consequently, the developed QLSCSO method produces high-quality segmentation results while reducing vulnerability to segmentation process stagnation. To establish the effectiveness of the proposed method, comparative analyses are initiated between QLSCSO and other advanced meta-algorithms using the IEEE CEC 2022 benchmark functions. Furthermore, QLSCSO undergoes experimental evaluations on CT images of COVID-19, including comprehensive comparative assessments with other competing segmentation methods and thorough validation. The results conclusively demonstrate the outstanding performance of the unique segmentation method based on QLSCSO across a range of performance evaluation metrics. Therefore, this approach offers an efficient segmentation procedure for COVID-19 images and even other pathological m

关键词： Meta-heuristics Reinforcement learning Image segmentation Otsu and Kapur method COVID-19 CT images bionic algorithm

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Chaotic Social Group Optimization for Structural Engineering Design Problems

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Journal of bionic Engineering 2023年第4期20卷 1852-1877页

作者： Anima Naik Department of CSE VIITVisakhapatnamAndhra Pradesh530049India

Till now,several novel metaheuristic algorithms are proposed for global *** only specific algorithms have become popular or attracted researchers,who are efficient in solving global optimization problems as well as real-world application *** Social Group Optimization(SGO)algorithm is a new metaheuristic bioinspired algorithm inspired by human social behavior that attracted researchers due to its simplicity and problem-solving *** this study,to deal with the problems of low accuracy and local convergence in SGO,the chaos theory is introduced into the evolutionary process of *** chaotic mapping has certainty,ergodicity,and stochastic property,by replacing the constant value of the self-introspection parameter with chaotic maps,the proposed chaotic social group optimization algorithm increases its convergence rate and resulting *** proposal chaotic SGO is validated through 13 benchmark functions and after that 9 structural engineering design problems have been *** simulated results have been noticed as competent with that of state-of-art algorithms regarding convergence quality and accuracy,which certifies that improved SGO with chaos is valid and feasible.

关键词： Chaos bionic algorithm Constrained optimization SGO Design problem

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Dynamic Individual Selection and Crossover Boosted Forensic-based Investigation algorithm for Global Optimization and Feature Selection

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Journal of bionic Engineering 2023年第5期20卷 2416-2442页

作者： Hanyu Hu Weifeng Shan Jun Chen Lili Xing Ali Asghar Heidari Huiling Chen Xinxin He Maofa Wang School of Emergency Management Institute of Disaster PreventionSanhe065201China Earthquake Administration of Anhui Province Hefei230031China Department of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhou325035China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic TechnologyGuilin541004China

The advent of Big Data has rendered Machine Learning tasks more intricate as they frequently involve higher-dimensional *** Selection(FS)methods can abate the complexity of the data and enhance the accuracy,generalizability,and interpretability of ***-heuristic algorithms are often utilized for FS tasks due to their low requirements and efficient *** paper introduces an augmented Forensic-Based Investigation algorithm(DCFBI)that incorporates a Dynamic Individual Selection(DIS)and crisscross(CC)mechanism to improve the pursuit phase of the ***,a binary version of DCFBI(BDCFBI)is applied to *** conducted on IEEE CEC 2017 with other metaheuristics demonstrate that DCFBI surpasses them in search *** influence of different mechanisms on the original FBI is analyzed on benchmark functions,while its scalability is verified by comparing it with the original FBI on benchmarks with varied *** is then applied to 18 real datasets from the UCI machine learning database and the Wieslaw dataset to select near-optimal features,which are then compared with six renowned binary *** results show that BDCFBI can be more competitive than similar methods and acquire a subset of features with superior classification accuracy.

关键词： Feature selection Forensic-based investigation algorithm Crisscross mechanism Global optimization Metaheuristic algorithms bionic algorithm

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Boosting Whale Optimizer with Quasi-Oppositional Learning and Gaussian Barebone for Feature Selection and COVID-19 Image Segmentation

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Journal of bionic Engineering 2023年第2期20卷 797-818页

作者： Jie Xing Hanli Zhao Huiling Chen Ruoxi Deng Lei Xiao Key Laboratory of Intelligent Informatics for Safety&Emergency of Zhejiang Province Wenzhou UniversityWenzhou325035China

Whale optimization algorithm(WOA)tends to fall into the local optimum and fails to converge quickly in solving complex *** address the shortcomings,an improved WOA(QGBWOA)is proposed in this ***,quasi-opposition-based learning is introduced to enhance the ability of WOA to search for optimal ***,a Gaussian barebone mechanism is embedded to promote diversity and expand the scope of the solution space in *** verify the advantages of QGBWOA,comparison experiments between QGBWOA and its comparison peers were carried out on CEC 2014 with dimensions 10,30,50,and 100 and on CEC 2020 test with dimension ***,the performance results were tested using Wilcoxon signed-rank(WS),Friedman test,and post hoc statistical tests for statistical *** accuracy and speed are remarkably improved,as shown by experimental ***,feature selection and multi-threshold image segmentation applications are demonstrated to validate the ability of QGBWOA to solve complex real-world *** proves its superiority over compared algorithms in feature selection and multi-threshold image segmentation by performing several evaluation metrics.

关键词： Whale optimization algorithm Quasi-opposition-based learning Gaussian barebone Image segmentation Feature selection bionic algorithm

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Multi-strategies Boosted Mutative Crow Search algorithm for Global Tasks:Cases of Continuous and Discrete Optimization

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Journal of bionic Engineering 2022年第6期19卷 1830-1849页

作者： Weifeng Shan Hanyu Hu Zhennao Cai Huiling Chen Haijun Liu Maofa Wang Yuntian Teng School of Emergency Management Institute of Disaster PreventionLangfang065201China Institute of Geophysics China Earthquake AdministrationBeijing100081China Department of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhou325035China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic TechnologyGuilin541004China

Crow Search algorithm(CSA)is a swarm-based single-objective optimizer proposed in recent years,which tried to inspire the behavior of crows that hide foods in different locations and retrieve them when *** original version of the CSA has simple parameters and moderate ***,it often tends to converge slowly or get stuck in a locally optimal region due to a missed harmonizing strategy during the exploitation and exploration ***,strategies of mutation and crisscross are combined into CSA(CCMSCSA)in this paper to improve the performance and provide an efficient optimizer for various optimization *** verify the superiority of CCMSCSA,a set of comparisons has been performed reasonably with some well-established metaheuristics and advanced metaheuristics on 15 benchmark *** experimental results expose and verify that the proposed CCMSCSA has meaningfully improved the convergence speed and the ability to jump out of the local *** addition,the scalability of CCMSCSA is analyzed,and the algorithm is applied to several engineering problems in a constrained space and feature selection *** results show that the scalability of CCMSCSA has been significantly improved and can find better solutions than its competitors when dealing with combinatorial optimization *** proposed CCMSCSA performs well in almost all experimental ***,we hope the researchers can see it as an effective method for solving constrained and unconstrained optimization problems.

关键词： Crow search algorithm Feature selection Global optimization Metaheuristic algorithms Engineering problems bionic algorithm

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