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Multiscale Quantum Harmonic Oscillator algorithm With Strict Metastability Constraints for Multi-Modal optimization

IEEE ACCESS 2019年 7卷 17377-17388页

作者： Li, Bo Wang, Peng Jin, Jin Chinese Acad Sci Inst Comp Applicat Chengdu 610041 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Tangshan Univ Dept Comp Sci & Technol Tangshan 063000 Peoples R China Southwest Minzu Univ Coll Comp Sci & Technol Chengdu 610225 Sichuan Peoples R China

Multi-modal optimization is a troublesome problem faced by optimization algorithms. The multiscale quantum harmonic oscillator algorithm (MQHOA) utilizes group statistics strategy to evaluate the state of the population and neglects the individual state. It will lead the particles to be trapped in local optima when addressing multi-modal optimization problems. This paper proposes a modified MQHOA by introducing strict metastability constraints strategy (MQHOA-SMC). The new strategy adopts a joint constraint mechanism to make the particle states mutual constraint with each other. The modified algorithm enhances the ability to find a better quality solution in local areas. To demonstrate the efficiency and effectiveness of the proposed algorithm, simulations are carried out with SPSO2011, ABC, and QPSO on classical benchmark functions and with the newly CEC2013 test suite, respectively. The computational results demonstrate that MQHOA-SMC is a competitive algorithm for multi-modal problems.

关键词： Multiscale quantum harmonic oscillator algorithm metastability multi-modal optimization optimization algorithm

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A Jaya algorithm based wrapper method for optimal feature selection in supervised classification

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JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2022年第6期34卷 3851-3863页

作者： Das, Himansu Naik, Bighnaraj Behera, H. S. Veer Surendra Sai Univ Technol Dept Informat Technol Sambalpur 768018 Odisha India Veer Surendra Sai Univ Technol Dept Comp Applicat Sambalpur 768018 Odisha India

In recent years, Jaya optimization algorithm has been successfully applied in several optimization problems. This paper presents a novel feature selection (FS) approach based on Jaya optimization algorithm (FSJaya) along with supervised machine learning techniques to select the optimal features. This approach uses a search technique to find the best suitable features by updating the worst features to reduce the dimensions of the feature space. This improves the performance of supervised machine learning techniques. The effectiveness of the proposed approach is evaluated for ten benchmark datasets and compared with several FS approaches such as FS using genetic algorithm (FSGA), FS using particle swarm optimization algorithm (FSPSO), and FS using differential evolutionary (FSDE). The experimental result has shown that the average classification accuracy of FSJaya on most of the datasets is superior over the existing methods such as FSGA, FSPSO, and FSDE. The proof of statistical significance of the proposed approach has been validated by using Friedman and Holm test. This proposed approach is found efficient in selecting an optimal subset of features as compared to other counterparts. (C) 2020 The Authors. Published by Elsevier B.V. on behalf of King Saud University.

关键词： Feature selection Supervised classification Jaya optimization algorithm Wrapper method

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Rolling Bearing Fault Diagnosis Based on Parameter-Optimized ICEEMDAN and IPOA-SVM 6

Rolling Bearing Fault Diagnosis Based on Parameter-Optimized...

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6th IEEE International Conference on Automation, Electronics and Electrical Engineering, AUTEEE 2023

作者： He, Fei Shen, Xiangyun Liaoning Technical University College of Electrical and Control Engineering Huludao China

ISBN: (纸本)9798350305623

Aiming at the problem of low accuracy of traditional rolling bearing fault diagnosis, a fault diagnosis model of parameter optimization Improved Adaptive Noise Complete Ensemble Empirical Mode Decomposition (ICEEMDAN) and Improved Pelican optimization algorithm (IPOA) optimized Support Vector Machine (SVM) was proposed. We employ the optimized ICEEMDAN method, along with Multiscale Fuzzy Entropy (MFE) and Linear Discriminant Analysis (LDA) for modal decomposition and feature extraction of the original bearing vibration signals. To enhance the accuracy and convergence speed of the algorithm, we introduce Tent chaotic mapping, Levy flights, and differential variation perturbation factors into the basic Pelican optimization algorithm. Furthermore, the improved Pelican optimization algorithm is utilized to optimize the penalty parameter (c) and kernel function (g) of SVM, thus enhancing the fault diagnosis accuracy of the SVM algorithm. Experimental results on a public dataset demonstrate that the proposed method achieves an overall recognition rate of 99% for fault diagnosis of rolling bearings under 10 operating conditions. © 2023 IEEE.

关键词： Fault diagnosis Feature extraction Machine learning optimization algorithm Rolling bearing

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The Defending Mobile Ad Hoc Networks from Anonymity Threats with an Identity-less, OnDemand Routing Scheme 2

The Defending Mobile Ad Hoc Networks from Anonymity Threats ...

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2nd IEEE International Conference on Futuristic Technologies, INCOFT 2023

作者： Ahluwalia, Gunveen Chitkara University Institute of Engineering and Technology Chitkara University Centre for Interdisciplinary Research in Business and Technology Punjab India

ISBN: (纸本)9798350308846

We call a network that can be rapidly formed and rearranged on the fly a 'mobile ad hoc network' (MANET). Because of its decentralized administration and lack of stable infrastructure, this sort of network design is best suited for temporary connections. It is challenging to offer quality of service (QoS) and safety-aware routing in such a network because of its unstable architecture and limited resources. The basic objective of safe and trust-based immediately multiple routes transportation is to find a route from origin to target that fulfills at least two end-to-end QoS requirements. Using a variant of the widely used ad hoc ondemand multiple-pathway distance vector technique, this study assesses the idea. The researchers provide a mesh-based multipath navigation system for effective communication in MANET, complete with a secure nearby position trust validation mechanism and a superior link optimum route search using the Dolphin Echolocation algorithm. Our suggested routing protocol has been shown to improve packet delivery percentage, packet latency, overheads, and security against attacks and weaknesses in both analytical and numerical evaluations of its performance. © 2023 IEEE.

关键词： Mobile Ad Hoc Network optimization algorithm Quality of Service Secure & Trusted optimization Trust Verification

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An Optimized Binary Scheduling Controller for Microgrid Energy Management Considering Real Load Conditions

An Optimized Binary Scheduling Controller for Microgrid Ener...

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2023 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2023

作者： Mannan, Musfika Roslan, M.F. Reza, M.S. Mansor, M. Jern, Ker Pin Hossain, M.J. Hannan, M.A. Universiti Tenaga Nasional Department of Electrical and Electronic Engineering Kajang43000 Malaysia University of Technology Sydney School of Electrical and Data Engineering UltimoNSW2007 Australia Sunway University School of Engineering and Technology Petaling Jaya Bandar Sunway47500 Malaysia Universiti Tenaga Nasional Institute of Sustainable Energy Kajang43000 Malaysia

ISBN: (纸本)9781665471640

A dynamical power demand and stochastic nature of energy resources posses difficulties in controlling and managing output power. These challenges lead to instability and inconsistency of the entire operation which can cause unstable and power quality issues. This study presents an optimal schedule controller for microgrid energy management, utilizing the Binary Particle Swarm algorithm (BPSO) to minimize costs and ensure optimal power delivery to loads. The controller's aims include minimizing total operating costs for distributed energy resources and solving intricate constraint optimization issues with scheduling management operations. The proposed approach's effectiveness is evaluated within an IEEE 14-bus configuration with five microgrids (MGs) integrated with RESs using real load data from Perlis, Malaysia. The BPSO optimization technique offers an exceptional binary fitness function to find the optimal cell, utilizing real data such as solar radiation, wind speed, battery charging/discharging, fuel conditions, and demand. To confirm the efficiency of the developed controller, a comparison is conducted between the results achieved with and without microgrid (MG) integration. The results reveal the robustness of the BPSO algorithm in reducing energy consumption and cost by 199.6 MW to 316.53 MW and RM 87,250.35 to RM 138,327.5 respectively. As a result, an optimized scheduling controller-based BPSO optimization outperforms in terms of savings cost, reduced energy consumption, optimal DER use, and decreased CO2 emissions. © 2023 IEEE.

关键词： Binary Particle Swarm optimization Distributed energy resources Energy management Microgrid optimization algorithm Scheduling controller

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Meta-Importance Sampling Fuzzy Simulation Combined with Kriging Model for Fuzzy Reliability Analysis

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AIAA JOURNAL 2022年第8期60卷 5018-5022页

作者： Wang, Jiaqi Lu, Zhenzhou Northwestern Polytech Univ Sch Aeronaut Xian 710072 Peoples R China

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Identification of tissue-specific tumor biomarker using different optimization algorithms

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GENES & GENOMICS 2019年第4期41卷 431-443页

作者： Bhowmick, Shib Sankar Bhattacharjee, Debotosh Rato, Luis Heritage Inst Technol Dept Elect & Commun Engn Kolkata 700107 India Jadavpur Univ Dept Comp Sci & Engn Kolkata 700032 India Univ Evora Dept Informat P-7004516 Evora Portugal

BackgroundIdentification of differentially expressed genes, i.e., genes whose transcript abundance level differs across different biological or physiological conditions, was indeed a challenging task. However, the inception of transcriptome sequencing (RNA-seq) technology revolutionized the simultaneous measurement of the transcript abundance levels for thousands of *** this paper, such next-generation sequencing (NGS) data is used to identify biomarker signatures for several of the most common cancer types (bladder, colon, kidney, brain, liver, lung, prostate, skin, and thyroid)MethodsHere, the problem is mapped into the comparison of optimization algorithms for selecting a set of genes that lead to the highest classification accuracy of a two-class classification task between healthy and tumor samples. As the optimization algorithms Artificial Bee Colony (ABC), Ant Colony optimization, Differential Evolution, and Particle Swarm optimization are chosen for this experiment. A standard statistical method called DESeq2 is used to select differentially expressed genes before being feed to the optimization algorithms. Classification of healthy and tumor samples is done by support vector machineResultsCancer-specific validation yields remarkably good results in terms of accuracy. Highest classification accuracy is achieved by the ABC algorithm for Brain lower grade glioma data is 99.10%. This validation is well supported by a statistical test, gene ontology enrichment analysis, and KEGG pathway enrichment analysis for each cancer biomarker signatureConclusionThe current study identified robust genes as biomarker signatures and these identified biomarkers might be helpful to accurately identify tumors of unknown origin

关键词： Biomarker Machine learning tools Messenger RNA optimization algorithm Pathway analysis

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Automated Launch Period Selection

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JOURNAL OF SPACECRAFT AND ROCKETS 2022年第5期59卷 1795-1799页

作者： Jesick, Mark CALTECH Jet Prop Lab Inner Planet Mission Anal Grp 4800 Oak Grove Dr Pasadena CA 91109 USA

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Secure Transmission in Reconfigurable Intelligent Surface Aided Cooperative Cognitive Radio System

Secure Transmission in Reconfigurable Intelligent Surface Ai...

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2023 IEEE Globecom Workshops, GC Wkshps 2023

作者： Huo, Yiran Dong, Limeng Yan, Wanyu Cheng, Wei Li, Yong School of Electronics and Information Northwestern Polytechnical University Xi'an China Academy of Aerospace Solid Propulsion Technology Shaanxi Electronics Institute Xi'an China

ISBN: (纸本)9798350370218

Reconfigurable intelligent surface (RIS) draws great attention due to its unique nature of changing the uncontrollable electromagnetic environment, and it has been verified in the existing study that in cognitive radio (CR) system, without excessive interference to primary user (PU), RIS greatly helps enhancing the secure transmission performance of secondary user (SU). However, existing studies do not consider the security issue of both PU and SU in RIS assisted CR system, which remains a question worth studying. Hence, this paper considers a cooperative CR system, and we aim at jointly enhancing the transmission performance of both PU and SU under eavesdropping attacks on them. Specifically, a non-convex sum-rate maximization problem of PU and SU subject to secrecy outage probability constraints under imperfect channel state information (CSI) with statistical estimation error between RIS and eavesdropper is formulated. And an efficient successive approximation in combination of alternating optimization algorithm is proposed to tackle this problem so that a local optimal solution of beamformer at PU, SU as well as phase shift coefficient at RIS can be obtained. Numerical examples demonstrate the significant enhancement of performance on our algorithm compared with the benchmark scheme of random phase shift coefficient at RIS. © 2023 IEEE.

关键词： cognitive radio eavesdropping attack imperfect CSI optimization algorithm reconfigurable intelligent surface

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Optimized Intelligent Controller for Energy Storage based Microgrid towards Sustainable Energy Future

Optimized Intelligent Controller for Energy Storage based Mi...

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2023 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2023

作者： Abu, Sayem M. Hannan, M.A. Mansor, M. Ker, Pin Jern Yaw Long, Chua Universiti Tenaga Nasional Coe Department of Mechanical Engineering Kajang43000 Malaysia Sunway University School of Engineering and Technology Petaling Jaya Bandar Sunway47500 Malaysia Universiti Tenaga Nasional Coe Department of Electrical and Electronic Engineering Kajang43000 Malaysia

ISBN: (纸本)9781665471640

This study focuses on a sustainable microgrid-based hybrid energy system (HES), primarily focusing on analyzing the performance of the fuel cell and its impact on the overall HES into optimizing system performance. This system relies on a single renewable energy source, a photovoltaic (PV) system that is integrated with the energy storage system (ESS) including hydrogen-based fuel cell, battery, and supercapacitor for effective power management. The optimization of HES performance is achieved through fine-tuning of the proportional-integral (PI) controller using the particle swarm optimization (PSO) algorithm. The load profile utilized in the microgrid (MG) is characterized by a constant power output, ensuring a stable and uninterrupted supply of electricity from 6am to 6pm in a 24-hour time period. This approach is comparable to meeting the specific demands of industrial and critical facilities, such as manufacturing plants and hospitals, where continuous power is important. The selection of a constant load profile is benchmark with the alignment of Denham Hydrogen Demonstration Plant, Western Australia, enhancing the MG's overall reliability and validation into real-world application. Through simulation and analysis using MATLAB Simulink, the results demonstrate the remarkable impact of PSO on enhancing the fuel cell system's efficiency with air consumption, and fuel consumption reduction by utilization of 90% of the H2 to electrical energy. Significantly, the optimized total source power output enables seamless energy storage and intelligent load matching, leading to a stable and reliable grid power supply. This research study findings highlights the essential role of PSO in elevating sustainability and maximizing resource utilization within microgrid-based hybrid energy systems, establishing a pathway towards a greener and more sustainable energy future. © 2023 IEEE.

关键词： battery fuel cell hybrid energy storage integrated grid hydrogen optimization algorithm solar supercapacitor sustainable

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