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Improved Manta Ray Foraging Optimizer-based SVM for Feature Selection Problems:A Medical Case Study

Journal of Bionic Engineering 2024年第1期21卷 409-425页

作者： Adel Got Djaafar Zouache Abdelouahab Moussaoui Laith Abualigah Ahmed Alsayat Faculty of informatics University of Science and Technology Houari BoumedieneAlgiersAlgeria LRIA Laboratory University of Science and Technology Houari BoumedieneAlgiersAlgeria Computer Science Department University of Mohamed El Bachir El IbrahimiBordj Bou ArreridjAlgeria Computer Science Department University of Ferhat AbbasSetifAlgeria Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl al-Bayt UniversityMafraq 25113Jordan Department of Computer Science College of Computer and Information SciencesJouf UniversityJoufSaudi Arabia Department of Electrical and Computer Engineering Lebanese American University13-5053ByblosLebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman 19328Jordan MEU Research Unit Middle East UniversityAmman 11831Jordan Applied science research center Applied science private universityAmman 11931Jordan School of Computer Sciences Universiti Sains Malaysia11800 PenangMalaysia School of Engineering and Technology Sunway University Malaysia27500 Petaling JayaMalaysia

Support Vector Machine(SVM)has become one of the traditional machine learning algorithms the most used in prediction and classification ***,its behavior strongly depends on some parameters,making tuning these parameters a sensitive step to maintain a good *** the other hand,and as any other classifier,the performance of SVM is also affected by the input set of features used to build the learning model,which makes the selection of relevant features an important task not only to preserve a good classification accuracy but also to reduce the dimensionality of *** this paper,the MRFO+SVM algorithm is introduced by investigating the recent manta ray foraging optimizer to fine-tune the SVM parameters and identify the optimal feature subset *** proposed approach is validated and compared with four SVM-based algorithms over eight benchmarking ***,it is applied to a disease Covid-19 *** experimental results show the high ability of the proposed algorithm to find the appropriate SVM’s parameters,and its acceptable performance to deal with feature selection problem.

关键词： Support vector machine Parameters tuning Feature selection bioinspired algorithms Manta ray foraging optimizer

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VMMISD: An Efficient Load Balancing Model for Virtual Machine Migrations via Fused Metaheuristics With Iterative Security Measures and Deep Learning Optimizations

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IEEE ACCESS 2024年 12卷 39351-39374页

作者： Brahmam, Madala Guru Anand, Vijay R. Vellore Inst Technol Sch Comp Sci Engn & Informat Syst Vellore 632014 Tamil Nadu India

As cloud computing continues to evolve, the need for efficient and secure management of virtual machine (VM) migrations has become increasingly evident. Traditional models often fall short in optimizing load balancing and energy consumption while ensuring a high level of security. In this work, we propose the load balancing and energy-efficient migration model, an innovative approach that leverages bioinspired algorithms and advanced security measures to optimize VM migrations. The initial novelty of our model is the integration of Genetic algorithms with Ant Colony Optimization for resource scheduling operations. These algorithms were specifically chosen for their proven effectiveness in solving complex optimization problems by simulating natural processes. Additionally, our model incorporates a deep reinforcement learning-based iterative-learning contextual side chaining model to enhance security measures. This approach not only learns and adapts to new security threats over time but also utilizes contextual side-chaining to link related security events, thereby providing a robust defense mechanism against potential threats. The affinity between VMs and physical machines is quantified using K Means clustering and fuzzy logic, which ensures optimal load balancing while accounting for the uncertainty inherent in the migration process. Furthermore, we employ bidirectional long short-term memory networks with recurrent graph neural network, for accurate workload prediction and informed migration decision making process. The selection of these techniques is grounded in their proven capability to analyze historical data and predict future trends with high accuracy levels. Our proposed model demonstrates marked improvements in several key performance metrics. We achieved a 4.5% reduction in makespan, a 4.9% increase in deadline hit ratio, and a 3.9% improvement in task diversity. Furthermore, computational complexity was reduced by 8.3%, VM migration efficiency improved

关键词： Virtual machine migrations bioinspired algorithms advanced security measures machine learning techniques cloud computing optimization

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Lens-Oppositional Wild Geese Optimization Based Clustering Scheme for Wireless Sensor Networks Assists Real Time Disaster Management

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Computer Systems Science & Engineering 2023年第7期46卷 835-851页

作者： R.Surendran Youseef Alotaibi Ahmad F.Subahi Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennaiIndia Department of Computer Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkah21955Saudi Arabia Department of Computer Science University College of Al JamoumUmm Al-Qura UniversityMakkah21421Saudi Arabia

Recently,wireless sensor networks(WSNs)find their applicability in several real-time applications such as disaster management,military,surveillance,healthcare,*** utilization of WSNs in the disaster monitoring process has gained significant attention among research communities and ***-time monitoring of disaster areas using WSN is a challenging process due to the energy-limited sensor ***,the clustering process can be utilized to improve the energy utilization of the nodes and thereby improve the overall functioning of the *** this aspect,this study proposes a novel Lens-Oppositional Wild Goose Optimization based Energy Aware Clustering(LOWGO-EAC)scheme for WSN-assisted real-time disaster *** major intention of the LOWGO-EAC scheme is to perform effective data collection and transmission processes in disaster *** achieve this,the LOWGOEAC technique derives a novel LOWGO algorithm by the integration of the lens oppositional-based learning(LOBL)concept with the traditional WGO algorithm to improve the convergence *** addition,the LOWGO-EAC technique derives a fitness function involving three input parameters like residual energy(RE),distance to the base station(BS)(DBS),and node degree(ND).The proposed LOWGO-EAC technique can accomplish improved energy efficiency and lifetime of WSNs in real-time disaster management *** experimental validation of the LOWGO-EAC model is carried out and the comparative study reported the enhanced performance of the LOWGO-EAC model over the recent approaches.

关键词： Disaster management real-time applications wireless sensor networks clustering bioinspired algorithms

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Physarum Route Optimization: Bio-inspired Strategies for Mobile Robot Navigation

Physarum Route Optimization: Bio-inspired Strategies for Mob...

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2024 Latin American Robotics Symposium

作者： Martinelli, Dieisson de Oliveira, Andre Schneider Kalempa, Vivian Cremer Univ Tecnol Fed Parana UTFPR Grad Program Elect & Comp Engn Curitiba Parana Brazil Univ Estado Santa Catarina UDESC Dept Informat Syst Sao Bento Do Sul Brazil

ISBN: (纸本)9798331508814;9798331508807

The objective of this project is to explore the applicability of a bioinspired approach, derived from the behavior of the Physarum polycephalum fungus, in optimizing trajectory navigation for mobile robot structures. The precise organism Physarum, which has the capability to quick locate efficient paths among factors in complicated environments, serves as a herbal version for constructing inspired trajectory algorithms. In this project, the development of a trajectory set of rules is proposed, that's as compared to conventional trajectory introduction techniques, aiming to introduce a novel approach to cellular robot navigation. This work is presented thru a aggregate of experimental observations and computational simulations that show the effectiveness of this bioinspired approach in trajectory choice. As a end result, an set of rules is developed that mimics the behavior of the Physarum and, similarly to conventional navigation algorithms, paves the manner for research and improvement.

关键词： bioinspired algorithms Autonomous robotics Trajectory optimization Mobile robotic navigation Physarum polycephalum

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A Hybrid bioinspired Approach for Advanced Image Reconstruction

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IEEE ACCESS 2024年 12卷 165948-165962页

作者： Lobato-Larios, Salvador Starostenko, Oleg Alarcon-Aquino, Vicente Univ Sierra Juarez Inst Environm Studies Ixtlan de Juarez 68725 Oaxaca Mexico Univ las Amer Puebla Dept Comp Elect & Mechatron Cholula 72810 Mexico

With the exponential growth of digital imagery, some novel techniques for visual information reconstruction are needed since the development of high-speed and precision methods is still an open problem for several applications such as medical diagnosis, satellite imaging, and general image processing. This paper proposes a new hybrid approach for reconstructing images using Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Wavelet Fusion (WF) techniques. While PSO seeks a solution modeled on the flocking and schooling patterns in birds and fish, GA helps to explore the solution space, by reducing the risk of local minima as well as improving the process of searching by modeling specific natural mechanisms at play in evolution, and WF enhances image quality by lessening noise. This is considered as the major contribution of the work, which lies in the bio-inspired algorithm by integrating swarm intelligence and wavelet fusion techniques applied to multiple initial reconstruction steps for an image to approximate the intended reconstructed one. Experimental results show that this hybrid approach converges fast and gives better reconstruction with a low Mean Squared Error (MSE). The proposed methodology provides a strong foundation for developing image reconstruction techniques by demonstrating that swarm intelligence can be integrated with wavelet-based techniques.

关键词： Image reconstruction Image reconstruction bioinspired algorithms bioinspired algorithms particle swarm optimization particle swarm optimization wavelet fusion wavelet fusion

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Tangent search algorithm for solving optimization problems

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NEURAL COMPUTING & APPLICATIONS 2022年第11期34卷 8853-8884页

作者： Layeb, Abdesslem Univ Constantine 2 Comp Sci & Its Applicat Dept LISIA Lab NTIC Fac El Khroub Algeria

This article proposes a new population-based optimization algorithm called the Tangent Search Algorithm (TSA) to solve optimization problems. The TSA uses a mathematical model based on the tangent function to move a given solution toward a better solution. The tangent flight function has the advantage to balance between the exploitation and the exploration search. Moreover, a novel escape procedure is used to avoid to be trapped in local minima. Besides, an adaptive variable step-size is also integrated in this algorithm to enhance the convergence capacity. The performance of TSA is assessed in three classes of tests: classical tests, CEC benchmarks, and engineering optimization problems. Moreover, several studies and metrics have been used to observe the behavior of the proposed TSA. The experimental results show that TSA algorithm is capable to provide very promising and competitive results on most benchmark functions thanks to better balance between exploration and exploitation of the search space. The main characteristics of this new optimization algorithm is its simplicity and efficiency and it requires only a small number of user-defined parameters.

关键词： Optimization Metaheuristics bioinspired algorithms Constrained optimization Unconstrained optimization

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Biologically Inspired Unified Artificial Immune System for Industrial Equipment Diagnostic 1

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8th Annual International Conference on Machine Learning, Optimization and Data science (LOD)

作者： Samigulina, Galina Samigulina, Zarina Inst Informat & Comp Technol Alma Ata Kazakhstan Kazakh British Tech Univ Alma Ata Kazakhstan

ISBN: (数字)9783031258916

ISBN: (纸本)9783031258909;9783031258916

Modern high-tech industrial enterprises are equipped with sophisticated equipment and microprocessor technology. The maintenance of such production facilities is an expensive procedure, and the replacement of equipment due to breakdown or wear and tear leads to significant financial costs. In the oil and gas industry, the idle operation of an enterprise for several hours or a working day can cause serious losses to the company. In this regard, it is relevant to develop intelligent diagnostic systems aimed at timely detection of faults, assessing the degree of their criticality and predicting possible breakdowns in the future. The use of bioinspired machine learning methods for diagnosing industrial equipment in real industrial production is a promising area of research. The article presents the developed diagnostic system for industrial equipment based on the methodology of analysis of modes, failures of their influence, degree of criticality (Failure Mode and Effects Analysis, FMEA) and a unified artificial immune system (UAIS), created on the basis of systematization and classification of modified algorithms of artificial immune systems (AIS). Unification is used to select the most efficient modified AIS algorithm based on the theories of clonal selection, negative selection and the immune network for processing heterogeneous data. UAIS is especially effective in the analysis of dynamically changing production data and a small number of training samples corresponding to equipment failures. Simulation results obtained on real data of TengizChevroil refinery.

关键词： bioinspired algorithms Unified artificial immune system Diagnostic of industrial equipment Fault detection

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OKSP: A Novel Deep Learning Automatic Event Detection Pipeline for Seismic Monitoring in Costa Rica 3

OKSP: A Novel Deep Learning Automatic Event Detection Pipeli...

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3rd IEEE International Conference on bioinspired Processing (BIP)

作者： van der Laat, Leonardo Baldares, Ronald J. L. Chaves, Esteban J. Meneses, Esteban Costa Rica High Technol Ctr San Jose Costa Rica Costa Rica Inst Technol San Jose Costa Rica Natl Univ Volcanol & Seismol Observ Costa Rica Heredia Costa Rica

ISBN: (纸本)9781665427227

Small magnitude earthquakes are the most abundant but the most difficult to locate robustly and well due to their low amplitudes and high frequencies usually obscured by heterogeneous noise sources. They highlight crucial information about the stress state and the spatio-temporal behavior of fault systems during the earthquake cycle, therefore, its full characterization is then crucial for improving earthquake hazard assessment. Modern deep learning algorithms along with the increasing computational power and efficiency are exploiting the continuously growing seismological databases, worldwide, allowing scientists to improve the completeness for earthquake catalogs, systematically detecting and locating smaller magnitude earthquakes and reducing the errors introduced mainly by human intervention through traditional approaches in seismological observatories. In this work, we introduce OKSP, a novel deep learning automatic earthquake detection pipeline for seismic monitoring in Costa Rica. Using Kabre supercomputer from the Costa Rica High Technology Center, we applied OKSP to the day before and the first 5 days following the Puerto Armuelles, M6.5, earthquake that occurred on 26 June, 2019, along the Costa Rica-Panama border and found 1100 more earthquakes previously unidentified by the Volcanological and Seismological Observatory of Costa Rica. From these events, a total of 23 earthquakes with magnitudes below 1.0 occurred a day to hours prior to the mainshock, shedding light about the rupture initiation and earthquake interaction leading to the occurrence of this productive seismic sequence. Our observations show that for the study period, the model was 100% exhaustive and 82% precise, resulting in an F1 score of 0.90. This effort represents the very first attempt for automatically detecting earthquakes in Costa Rica using deep learning methods and demonstrates that, in the near future, earthquake monitoring routines will be carried out entirely by AI algorithms.

关键词： deep learning automatic earthquake detection phase picking foreshock mainshock aftershock bioinspired algorithms

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Multiobjective artificial fish swarm algorithm for multiple sequence alignment

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INFOR 2020年第1期58卷 38-59页

作者： Dabba, Ali Tari, Abdelkamel Zouache, Djaafar Abderrahmane Mira Univ Fac Sci Comp Sci Dept Bejaia Algeria Mohamed Elbachir Elibrahimi Univ Comp Sci Dept Bordj Bou Arreridj Algeria

Multiple sequence alignment (MSA) represents a basic task for many bioinformatics applications. MSA allows finding common conserved regions among various sequences of proteins or DNA. However, to find the optimal multiple sequence alignment, it is necessary to design an efficient exploration approach that could explore a huge number of possible multiple sequence alignments. As well as, it is required to use a powerful evaluation method to assess the biological relevance of these multiple sequence alignment. To address these main problems, this article presents a multiobjective artificial fish swarm algorithm (MOAFS) to solve multiple sequence alignment. MOAFS uses the behaviors of artificial fish swarm algorithm such as the cooperation, decentralization and parallelism to ensure a good trade-off between the exploration and the exploitation of the search space of MSA problem. To preserve the quality and consistency of alignment, two fitness functions have been simultaneously used by the MOAFS algorithm: (i) Weighted Sum of Pairs to determine similar regions horizontally and (ii) Similarity function to determine vertically similar regions between the sequences of an alignment. Following the exploration of space search, the Pareto-optimal set is obtained by MOAFS which performs the optimal multiple sequence alignments for both fitness functions. The performance of MOAFS algorithm has been proved by comparing our algorithm with different progressive alignment methods, and other alignment methods based on evolutionary algorithms with singleobjective and many-objective. The experiment results conducted on BAliBASE 2.0 and BAliBASE 3.0 benchmark confirm that the MOAFS algorithm provides a greater accuracy statistical significance in terms of SP or CS scores.

关键词： Multiple sequence alignment artificial fish swarm algorithm bioinspired algorithms bioinformatics optimization algorithms evolutionary algorithm molecular biology swarm intelligence

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Modifications of Flower Pollination, Teacher-Learner and Firefly algorithms for Solving Multiextremal Optimization Problems

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algorithms 2022年第10期15卷 359-359页

作者： Sorokovikov, Pavel Gornov, Alexander Russian Acad Sci Matrosov Inst Syst Dynam & Control Theory Siberian Branch Optimal Control Lab Irkutsk 664033 Russia

The article offers a possible treatment for the numerical research of tasks which require searching for an absolute optimum. This approach is established by employing both globalized nature-inspired methods as well as local descent methods for exploration and exploitation. Three hybrid nonconvex minimization algorithms are developed and implemented. Modifications of flower pollination, teacher-learner, and firefly algorithms are used as nature-inspired methods for global searching. The modified trust region method based on the main diagonal approximation of the Hessian matrix is applied for local refinement. We have performed the numerical comparison of variants of the realized approach employing a representative collection of multimodal objective functions. The implemented nonconvex optimization methods have been used to solve the applied problems. These tasks utilize an optimization of the low-energy metal Sutton-Chen clusters potentials with a very large number of atoms and the parametric identification of the nonlinear dynamic model. The results of this research confirms the performance of the suggested algorithms.

关键词： global optimization multiextremal function bioinspired algorithms atomic-molecular clusters

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