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Cancer Detection Using a New Hybrid Method Based on Pattern Recognition in MicroRNAs Combining particle swarm optimization algorithm and Artificial Neural Network

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BIG DATA AND COGNITIVE COMPUTING 2024年第3期8卷 33页

作者： Molaei, Sepideh Cirillo, Stefano Solimando, Giandomenico Tarbiat Modares Univ Fac Elect & Comp Engn Tehran 14115111 Iran Univ Salerno Dept Comp Sci I-84084 Salerno Italy

MicroRNAs (miRNAs) play a crucial role in cancer development, but not all miRNAs are equally significant in cancer detection. Traditional methods face challenges in effectively identifying cancer-associated miRNAs due to data complexity and volume. This study introduces a novel, feature-based technique for detecting attributes related to cancer-affecting microRNAs. It aims to enhance cancer diagnosis accuracy by identifying the most relevant miRNAs for various cancer types using a hybrid approach. In particular, we used a combination of particle swarm optimization (PSO) and artificial neural networks (ANNs) for this purpose. PSO was employed for feature selection, focusing on identifying the most informative miRNAs, while ANNs were used for recognizing patterns within the miRNA data. This hybrid method aims to overcome limitations in traditional miRNA analysis by reducing data redundancy and focusing on key genetic markers. The application of this method showed a significant improvement in the detection accuracy for various cancers, including breast and lung cancer and melanoma. Our approach demonstrated a higher precision in identifying relevant miRNAs compared to existing methods, as evidenced by the analysis of different datasets. The study concludes that the integration of PSO and ANNs provides a more efficient, cost-effective, and accurate method for cancer detection via miRNA analysis. This method can serve as a supplementary tool for cancer diagnosis and potentially aid in developing personalized cancer treatments.

关键词： cancer diagnosis neural network particle swarm optimization algorithm microRNAs

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Research on hybrid strategy particle swarm optimization algorithm and its applications

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SCIENTIFIC REPORTS 2024年第1期14卷 1-15页

作者： Yao, Jicheng Luo, Xiaonan Li, Fang Li, Ji Dou, Jundi Luo, Hongtai Guilin Univ Elect Technol Sch Comp Sci & Informat Secur Guilin 541004 Peoples R China Guilin Univ Elect Technol Ctr Appl Math Guangxi GUET Guilin 541004 Peoples R China Guilin Univ Elect Technol Natl Local Joint Engn Res Ctr Satellite Nav & Loc Guilin 541004 Peoples R China

The increasing complexity and high-dimensional nature of real-world optimization problems necessitate the development of advanced optimization algorithms. Traditional particle swarm optimization (PSO) often faces challenges such as local optima entrapment and slow convergence, limiting its effectiveness in complex tasks. This paper introduces a novel Hybrid Strategy particle swarm optimization (HSPSO) algorithm, which integrates adaptive weight adjustment, reverse learning, Cauchy mutation, and the Hook-Jeeves strategy to enhance both global and local search capabilities. HSPSO is evaluated using CEC-2005 and CEC-2014 benchmark functions, demonstrating superior performance over standard PSO, Dynamic Adaptive Inertia Weight PSO (DAIW-PSO), Hummingbird Flight patterns PSO (HBF-PSO), Butterfly optimization algorithm (BOA), Ant Colony optimization (ACO), and Firefly algorithm (FA). Experimental results show that HSPSO achieves optimal results in terms of best fitness, average fitness, and stability. Additionally, HSPSO is applied to feature selection for the UCI Arrhythmia dataset, resulting in a high-accuracy classification model that outperforms traditional methods. These findings establish HSPSO as an effective solution for complex optimization and feature selection tasks.

关键词： particle swarm optimization algorithm Hybrid strategy Adaptive weight adjustment Reverse learning strategy Cauchy mutation mechanism Hook-Jeeves strategy Feature selection

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Application and Improvement of the particle swarm optimization algorithm in Source-Term Estimations for Hazardous Release

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ATMOSPHERE 2023年第7期14卷 1168-1168页

作者： Lu, Jinshu Huang, Mengqing Wu, Wenfeng Wei, Yonghui Liu, Chong Zhejiang Ocean Univ Dept Naval Architecture & Maritime Zhoushan 316022 Peoples R China

Hazardous gas release can pose severe hazards to the ecological environment and public safety. The source-term estimation of hazardous gas leakage serves a crucial role in emergency response and safety management practices. Nevertheless, the precision of a forward diffusion model and atmospheric diffusion conditions have a significant impact on the performance of the method for estimating source terms. This work proposes the particle swarm optimization (PSO) algorithm coupled with the Gaussian dispersion model for estimating leakage source parameters. The method is validated using experimental cases of the prairie grass field dispersion experiment with various atmospheric stability classes. The results prove the effectiveness of this method. The effects of atmospheric diffusion conditions on estimation outcomes are also investigated. The estimated effect in extreme atmospheric diffusion conditions is not as good as in other diffusion conditions. Accordingly, the Gaussian dispersion model is improved by adding linear and polynomial correction coefficients to it for its inapplicability under extreme diffusion conditions. Finally, the PSO method coupled with improved models is adapted for the source-term parameter estimation. The findings demonstrate that the estimation performance of the PSO method coupled with improved models is significantly improved. It was also found that estimated performances of source parameters of two correction models were significantly distinct under various atmospheric stability classes. There is no single optimal model;however, the model can be selected according to practical diffusion conditions to enhance the estimated precision of source-term parameters.

关键词： source-term parameter estimation forward dispersion model particle swarm optimization algorithm gas release atmospheric diffusion conditions

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Research on cutting stock optimization of rebar engineering based on building information modeling and an improved particle swarm optimization algorithm

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DEVELOPMENTS IN THE BUILT ENVIRONMENT 2023年 13卷

作者： Ren, Ke Jia, Lu Huang, Jiantao Wu, Min Nanchang Univ Sch Infrastruct Engn Nanchang 330031 Peoples R China Nanchang Univ Res Inst Urban Rural Construct & Dev Nanchang 330031 Peoples R China

Rebar is one of the most important construction materials in engineering projects. For reinforced concrete structures, rebar consumption accounts for about 16% of the total project cost. Total project cost could be significantly reduced by precise computation on the cutting length of rebars. In this paper, a novel two-stage framework of rebar cutting stock is proposed to produce minimum residual rate of rebar and the minimum number of cutting patterns. In the first stage, a fast and accurate rebar lofting technique based on building in-formation modeling (BIM) is designed to automatically generate rebar-cutting data. In the second stage, a greedy strategy-based adaptive particle swarm optimization algorithm (GAPSO) is developed to optimize the rebar-cutting scheme. Finally, the proposed framework is used in the reinforcement laying-off of a comprehensive indemnificatory housing project, and the result verified the practicability and efficiency of the framework.

关键词： Rebar cutting stock Building information modeling (BIM) optimization Parametric automatic lofting particle swarm optimization algorithm

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A Comprehensively Improved particle swarm optimization algorithm to Guarantee particle Activity

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RUSSIAN PHYSICS JOURNAL 2021年第5期64卷 866-875页

作者： Bi, Ya Lam, Anthony Quan, Huiqun Liu, Hui Wang, Cunfa Hubei Univ Econ Sch Business Adm Wuhan Peoples R China Huazhong Univ Sci & Technol Coll Publ Adm Wuhan Peoples R China Katholieke Univ Leuven Fac Econ & Business Leuven Belgium Wuhan Univ Technol Sch Management Wuhan Peoples R China Fujian Zhuozhi Project Investment Consulting Co L Wuhan Peoples R China

The particle swarm optimization (PSO) algorithm has certain disadvantages;for instance, the convergence viscosity of the algorithm is reduced in the post evolution phase, the optimization search efficiency is also reduced, the algorithm is easy to be inserted with a local extremum during the calculation of a complex problem of a high-dimensional multiple extremum, and the convergence thereof is low. To compensate for the PSO disadvantage, we propose a particle swarm optimization of the comprehensive improvement strategy, which is a simple particle swarm optimization with a dynamic adaptive hybridization of the extremum disturbance and the ecds-PSO algorithm. This new comprehensively improved particle swarm algorithm discards the particle velocity and reduces the PSO from the second-order to a first-order difference equation. The evolutionary process is controlled by the particle position variables only. The hybridization operation of increasing the extremum disturbance and introducing a genetic algorithm can accelerate the particles to overstep the local extremum. The mathematical derivation and the plurality of a comparative experiment provide the following information: the improved particle swarm optimization is a simple and effective optimization algorithm that can enhance the algorithm accuracy, the convergence viscosity and the ability of avoiding the local extremum, and effectively reduce the calculation complexity.

关键词： particle swarm optimization algorithm dynamic adaptive hybridization local extremum particle activity

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Solving fixed-charge transportation problem using a modified particle swarm optimization algorithm

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INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2021年第6期12卷 1073-1086页

作者： Singh, Gurwinder Singh, Amarinder IKG Punjab Tech Univ Jalandhar Kapurthala Punjab India BBSBEC Dept Appl Sci Fatehgarh Sahib Punjab India

particle swarm optimization (PSO) has emulated the social behaviour of some animals such as a flock of birds and a school of fish, searching for food. This communicative sociality when modelled as computational procedure has solved a wide range of complex problems. Over the years, PSO has undergone transformations and numerous variants have come up. In this paper, PSO has been hybridized with two new algorithms to solve the fixed charge transportation problem to minimize the transportation cost (variable and fixed) of delivering goods while satisfying supply/demand constraints. The method considers the reduction of objective function defined by Balinski, Adlakha et al., Yousefi et al. and is incorporated within the PSO. An independent approach of solving the problem on the basis of variable cost initially followed by addition of fixed cost has also been explored. It was observed that proposed PSO works best without reducing the objective function. The simulation results reveal a substantial gain of the proposed method in terms of its efficiency and effectiveness examined on different test problems. To validate the claims, the proposed PSO has also been compared with the solutions attained by other existing methods (either exact or heuristics).

关键词： Fixed-charge transportation problem Heuristic methods particle swarm optimization algorithm optimization techniques

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A parallel particle swarm optimization algorithm based on GPU/CUDA

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APPLIED SOFT COMPUTING 2023年 144卷

作者： Zhuo, Yanhong Zhang, Tao Du, Feng Liu, Ruilin Yangtze Univ Sch Informat & Math Jingzhou Hubei Peoples R China Jingchu Univ Technol Sch Math & Phys Jingmen Hubei Peoples R China

Parallel computing is the main way to improve the computational efficiency of metaheuristic algorithms for solving high-dimensional, nonlinear optimization problems. Previous studies have typically only implemented local parallelism for the particle swarm optimization (PSO) algorithm. In this study, we proposed a new parallel particle swarm optimization algorithm (GPU-PSO) based on the Graphics Processing Units (GPU) and Compute Unified Device Architecture (CUDA), which uses a combination of coarse-grained parallelism and fine-grained parallelism to achieve global parallelism. In addition, we designed a data structure based on CUDA features and utilized a merged memory access mode to further improve data-parallel processing and data access efficiency. Experimental results show that the algorithm effectively reduces the solution time of PSO for solving high-dimensional, large-scale optimization problems. The speedup ratio increases with the dimensionality of the objective function, where the speedup ratio is up to 2000 times for the high-dimensional Ackley function. & COPY;2023 Elsevier B.V. All rights reserved.

关键词： particle swarm optimization algorithm Parallel computing CUDA GPU function optimization [3] traveling salesman problem [4] wire

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Regional-modal optimization problems and corresponding normal search particle swarm optimization algorithm

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swarm AND EVOLUTIONARY COMPUTATION 2023年 78卷

作者： Yuan, Yi-li Hu, Chang-ming Li, Liang Mei, Yuan Wang, Xue-yan Xian Univ Architecture & Technol Coll Civil Engn Xian 710055 Shanxi Peoples R China Xian Univ Architecture & Technol Shaanxi Key Lab Geotech & Underground Space Engn Xian 710055 Shaanxi Peoples R China Xian Polytech Univ Coll Urban Planning & Municipal Engn Xian 710048 Shaanxi Peoples R China

Moavattd by me limit state auve kLSC) finding problem in reliability analysis. a new categmy of optinuyation problems refened to as the regional-modal optimization problems (RMOPs) was investigated in this paper. The most distinguishing feature of RMOPs is the continuity of its solutions. E-dsting optimization methods are not capable of capturing this feature and thus cannot produce acceptable results for RMOPs. Therefore, based on niching PSO a normal search particle swarm optimization (NSPSO) algorithm that can provide discrete optimal solutions for a continuous optimal region with arbitrary pie-specified density was developed. NSPSO is consisting of a normal search pattern and a multi-strategy fusion. Nomial searching is the core of NSPSO, in which each particle is guided by the normal vector of the region composed of its several best neighborhoods. Normal searching prevents the panicles from clustering and thus provide a basis for the solution diversity. Further, a multi strategy framework with three components was introduced to improve the performance of NSPSO. It inchides a dynamic particle repulsion strategy that improve the solution diversity, a panicle memory strategy to prevent local optimum, and an elite breeding strategy that was developed to increase the efficiency of the method. This framework gives NSPSO the ability to maintain the balance between exploitation and e ploration and thereby to realize the uniform disnibution and high coverage and diversity of the algorithm. Furthermore, the key parameters involved in NSPSO were analyzed thoroughly. The NSPSO is compared with ten state-of-theart multi-mode optimization algorithms in terms of twenty typical test functions with different properties that were constructed in this study. The experimental results demonstrate the superiority of our proposed algorithm over the state-of-the-art algorithms in solving RMOPs.

关键词： Regional-modal optimization problems particle swarm optimization algorithm Normal search Dynamic particle repulsion particle memory Density-based elite breeding

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Design and optimization of a TiO2/RGO-supported epoxy multilayer microwave absorber by the modified local best particle swarm optimization algorithm

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NANOTECHNOLOGY REVIEWS 2023年第1期12卷

作者： Karami, Mohammad Reza Jaleh, Babak Eslamipanah, Mahtab Nasri, Atefeh Rhee, Kyong Yop Bu Ali Sina Univ Fac Sci Dept Phys Hamadan 65174 Iran Kyung Hee Univ Dept Mech Engn Yongin 17104 South Korea

Microwave absorbers have many applications in medical, industrial, and military devices. Polymeric composites including carbon-based filler can be used as lightweight absorbers with high electromagnetic (EM) wave absorption performance. Hence, multilayer microwave absorbers were designed using titanium dioxide (TiO2)/reduced graphene oxide (RGO)/epoxy nanocomposites with different weight percentages manufactured using refluxing and annealing methods. The characterization of nanocomposite indicated thin layers of TiO2/RGO as divided sheets in epoxy. The EM properties of the nanocomposites were examined using the Nicolson-Ross-Weir (NRW) detection method. The S-parameters were measured using PNA-N5222A Microwave Network Analyzer. The multilayer absorber software was designed based on the modified local best particle swarm optimization algorithm by MATLAB software, in which the material and thickness of layers were optimized with two cost functions in X-band frequencies. The first cost function seeks to reach the best absorption bandwidth, and the second cost function seeks to reach the maximum average return loss (RL) of the frequency range of 8.2-12.4 GHz. A maximum bandwidth with an RL of less than -12.81 dB was obtained with a thickness of 2.4 mm. A maximum average RL of -22.1 dB was obtained with a thickness of 2.6 mm. The maximum absorption peak was observed with a thickness of 2.5 mm with -62.82 dB at a frequency of 10.86 GHz.

关键词： graphene oxide TiO2 nanoparticles multilayer microwave absorber particle swarm optimization algorithm

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A new boredom-aware dual-resource constrained flexible job shop scheduling problem using a two-stage multi-objective particle swarm optimization algorithm

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INFORMATION SCIENCES 2023年第1期643卷

作者： Shi, Jiaxuan Chen, Mingzhou Ma, Yumin Qiao, Fei Tongji Univ Shanghai Res Inst Intelligent Autonomous Syst Shanghai 201210 Peoples R China Tongji Univ Sch Econ & Management Shanghai 201804 Peoples R China Tongji Univ Sch Elect & Informat Engn Shanghai 201804 Peoples R China

Dual-resource constrained flexible job shop scheduling problem has become a hot research field in recent years. However, few studies have considered workers' boredom sensations when allo-cating resources and scheduling tasks, leading to inappropriate task allocation and negative effects such as efficiency reductions and absenteeism. Therefore, a new boredom-aware dual -resource constrained flexible job shop scheduling problem is investigated in this study, which considers the increase in workers' boredom caused by repetitive job assignments and constructs an efficiency function to characterize the impact of workers' boredom. For this problem, a bi-level lexicographic model, which takes the effective completion of all manufacturing tasks as the primary optimization objective and higher worker job satisfaction as the secondary optimization objective, is established. A two-stage multi-objective particle swarm optimization algorithm with a three-dimensional representation scheme is presented to solve this model. In this algorithm, a new position-updating mechanism and a local search strategy are devised to effectively evolve the solution, and a decomposition strategy with a self-adaptive neighborhood size and a boundary exploration mechanism are embedded to obtain a uniformly distributed Pareto front. Experi-mental results confirm the superiority of the presented model and algorithm.

关键词： Dual-resource constrained flexible job shop scheduling problem Workers ' boredom sensations Lexicographic method Two-stage multi -objective optimization algo rithm particle swarm optimization algorithm

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