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...
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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 particleswarmoptimization (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.
The increasing complexity and high-dimensional nature of real-world optimization problems necessitate the development of advanced optimizationalgorithms. Traditional particleswarmoptimization (PSO) often faces chal...
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The increasing complexity and high-dimensional nature of real-world optimization problems necessitate the development of advanced optimizationalgorithms. Traditional particleswarmoptimization (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 particleswarmoptimization (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 optimizationalgorithm (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.
particleswarmoptimization has been a popular and common met heuristic algorithm from its genesis time. However, some problems such as premature convergence, weak exploration ability and great number of iterations ha...
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particleswarmoptimization has been a popular and common met heuristic algorithm from its genesis time. However, some problems such as premature convergence, weak exploration ability and great number of iterations have been accompanied with the nature of this algorithm. Therefore, in this paper we proposed a novel classification for particles to organize them in a different way. This new method which is inspired from president election is called President Election particleswarmoptimization (PEPSO). This algorithm is trying to choose useful particles and omit functionless ones at initial steps of algorithm besides considering the effects of all generated particles to get a directed and fast convergence. Some preparations are also done to escape from premature convergence. To validate the applicability of our proposed PEPSO, it is compared with the other met heuristic algorithm including GAPSO, Logistic PSO, Tent PSO, and PSO to estimate the parameters of the controller for a hybrid power system. Results verify that PEPSO has a better reaction in worst conditions in finding parameters of the controller.
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 si...
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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.
The particleswarmoptimization (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 red...
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The particleswarmoptimization (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 particleswarmoptimization of the comprehensive improvement strategy, which is a simple particleswarmoptimization with a dynamic adaptive hybridization of the extremum disturbance and the ecds-PSO algorithm. This new comprehensively improved particleswarmalgorithm 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 particleswarmoptimization is a simple and effective optimizationalgorithm that can enhance the algorithm accuracy, the convergence viscosity and the ability of avoiding the local extremum, and effectively reduce the calculation complexity.
particleswarmoptimization (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 proced...
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particleswarmoptimization (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).
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 ...
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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 particleswarmoptimization (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 optimizationalgorithms 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.
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 abso...
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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.
This paper develops a concentration retrieval technique based on the particleswarmoptimization (PSO) algorithm, which is used for a calibration-free wavelength modulation spectroscopy system. As compared with the co...
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This paper develops a concentration retrieval technique based on the particleswarmoptimization (PSO) algorithm, which is used for a calibration-free wavelength modulation spectroscopy system. As compared with the commonly used technique based on the Levenberg-Marquardt (LM) algorithm, the PSO-based method is less dependent on the pre-characterization of the laser tuning parameters. We analyzed the key parameters affecting the performance of the PSO-based technique and determined their optimal parameter values through testing. Furthermore, we conducted a comparative analysis of the efficacy of two techniques in detecting C2H2 concentration. The results showed that the PSO-based concentration retrieval technique is about 63 times faster than the LM-based one in achieving the same accuracy. Within 5 s, the PSO-based technique can produce findings that are generally consistent with the values anticipated.
P300 is an important control system signal in the brain, so there is an urgent need and practical significance to work on the efficient classification of P300 event-related potentials. In this article, we design a con...
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ISBN:
(纸本)9798400707964
P300 is an important control system signal in the brain, so there is an urgent need and practical significance to work on the efficient classification of P300 event-related potentials. In this article, we design a convolutional neural network CNNnet based on chaotic adaptive particleswarmoptimization (CAPSO) algorithm for efficient and accurate detection and classification of P300 EEG signals. The chaotic adaptive particle swarm optimization algorithm uses Logistic chaotic mapping to initialize the initial position of particles, and adopts a dynamic adaptive weighting strategy. Compared with traditional particle swarm optimization algorithms, it can effectively improve the optimization speed and convergence speed of particles. The experimental results show that compared with other P300 detection neural networks and traditional particle swarm optimization algorithms, this algorithm has faster convergence speed and higher convergence accuracy, and can effectively avoid the problem of particleswarm falling into local optima.
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