In this paper, a novel adaptive geneticalgorithm (AGA) is presented for the optimization goals. The presented AGA is discussed and implemented on a power amplifier (PA), which is designed for 24 GHz and 5G applicatio...
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In this paper, a novel adaptive geneticalgorithm (AGA) is presented for the optimization goals. The presented AGA is discussed and implemented on a power amplifier (PA), which is designed for 24 GHz and 5G applications and fabricated in a 65-nm CMOS process. The PA is optimized by the AGA for the high power-added efficiency (PAE), i.e., optimum. In the AGA, we aimed to avoid the local optima and slow convergence rate that exist in the conventional genetic algorithm (CGA). In the AGA, we proposed a parameter tuning method to fine-tune the set of PA circuit component values for faster optimization of the PA to have a high PAE. The proposed AGA provides a significant speed in optimization process as compared to the CGA, and the execution time of the AGA is faster than that of the CGA. The proposed AGA is also verified, and its performance is compared with that of the CGA through multiple multidimensional mathematical benchmark functions. The PA performance parameters are measured, and the results showed that the optimized PA achieves a high gain of 29.9 dB. The P-sat of PA is measured as 14.21 dBm, and IIP3 is 13.8 dBm. The simulated result shows the optimized PA with the AGA has PAE of 49.7%, while that with the CGA has PAE of 47.5% at 24 GHz. The final measured PAE of AGA's optimized PA is 47.1%. The chip area of PA is 0.29 mm(2).
An adaptive technique adopting quantum geneticalgorithm （QGA） for antenna impedance tuning is presented. Three examples are given with different types of antenna impedance. The frequency range of the dual standards...
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An adaptive technique adopting quantum geneticalgorithm （QGA） for antenna impedance tuning is presented. Three examples are given with different types of antenna impedance. The frequency range of the dual standards is from 1.7 to 2.2 GHz. Simulation results show that the proposed tuning technique can achieve good accuracy of impedance matching and load power. The reflection coefficient and VSWR obtained are also very close to their ideal values. Comparison of the proposed QGA tuning method with conventional genetic algorithm based tuning method is Moreover, the proposed method can be useful for software wireless bands. also given, which shows that the QGA tuning algorithm is much faster. defined radio systems using a single antenna for multiple mobile and
Background: Biclustering has been utilized to find functionally important patterns in biological problem. Here a bicluster is a submatrix that consists of a subset of rows and a subset of columns in a matrix, and cont...
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Background: Biclustering has been utilized to find functionally important patterns in biological problem. Here a bicluster is a submatrix that consists of a subset of rows and a subset of columns in a matrix, and contains homogeneous patterns. The problem of finding biclusters is still challengeable due to computational complex trying to capture patterns from two-dimensional features. Results: We propose a Probabilistic COevolutionary Biclustering algorithm (PCOBA) that can cluster the rows and columns in a matrix simultaneously by utilizing a dynamic adaptation of multiple species and adopting probabilistic learning. In biclustering problems, a coevolutionary search is suitable since it can optimize interdependent subcomponents formed of rows and columns. Furthermore, acquiring statistical information on two populations using probabilistic learning can improve the ability of search towards the optimum value. We evaluated the performance of PCOBA on synthetic dataset and yeast expression profiles. The results demonstrated that PCOBA outperformed previous evolutionary computation methods as well as other biclustering methods. Conclusions: Our approach for searching particular biological patterns could be valuable for systematically understanding functional relationships between genes and other biological components at a genome-wide level.
Automated software testing allows testers and managers for generating the quality of test data during each phase of software development. Path coverage based testing is the most effective technique in structural testi...
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ISBN:
(纸本)9781538623138
Automated software testing allows testers and managers for generating the quality of test data during each phase of software development. Path coverage based testing is the most effective technique in structural testing. The major challenge in path coverage based testing is to generate the test data to cover complete path from beginning till end. Therefore, we require a novel automated test data generation method for the same. Various soft computing techniques are being used to generate the path coverage tests for search-based software testing. In this paper, we have focused on resolving the multi-objective optimization of coverage based test data by proposing Multi-Objective Ant Lion Optimization (MOALO) algorithm. Further, we have discussed that how the proposed algorithm enhance the path coverage with reduced number of tests. To validate the proposed algorithm, we have compared the obtained experimental results with random resting and conventional genetic algorithm's data. These results shows that proposed algorithm outperforms the existing algorithms.
Vehicular visible light communication is known as a promising way of inter-vehicle communication. Vehicular VLC can ensure the significant advancement of safety and efficiency in traffic. It has disadvantages, such as...
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Vehicular visible light communication is known as a promising way of inter-vehicle communication. Vehicular VLC can ensure the significant advancement of safety and efficiency in traffic. It has disadvantages, such as unexpected glare on drivers in moving conditions, i.e., non-line-of-sight link at night. While designing a receiver, the most important factor is to ensure the optimal quality of the received signal. Within this context, to achieve an optimal communication quality, it is necessary to find the optimal maximum signal strength. Hereafter, a new receiver design is focused on in this paper at the circuit level, and a novel micro geneticalgorithm is proposed to optimize the signal strength. The receiver can calculate the SNR, and it is possible to modify its structural design. The micro GA determines the alignment of the maximum signal strength at the receiver point rather than monitoring the signal strength for each angle. The results showed that the proposed scheme accurately estimates the alignment of the receiver, which gives the optimum signal strength. In comparison with the conventional GA, the micro GA results showed that the maximum received signal strength was improved by -1.7 dBm, -2.6 dBm for user Location 1 and user Location 2, respectively, which proves that the micro GA is more efficient. The execution time of the conventional GA was 7.1 s, while the micro GA showed 0.7 s. Furthermore, at a low SNR, the receiver showed robust communication for automotive applications.
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