The Role of Genetic Algorithm Selection Operators in Extending WSN Stability Period: A Comparative Study

A genetic algorithm (GA) contains a number of genetic operators that can be tweaked to improve the performance of specific implementations. Parent selection, crossover, and mutation are examples of these operators. One of the most important operations in GA is selection. The performance of GA in addressing the single-objective wireless sensor network stability period extension problem using various parent selection methods is evaluated and compared. In this paper, six GA selection operators are used: roulette wheel, linear rank, exponential rank, stochastic universal sampling, tournament, and truncation. According to the simulation results, the truncation selection operator is the most efficient operator in terms of extending the network stability period and improving reliability. The truncation operator outperforms other selection operators, most notably the well-known roulette wheel operator, by increasing the stability period by 25.8% and data throughput by 26.86%. Furthermore, the truncation selection operator outperforms other selection operators in terms of the network residual energy after each protocol round.

Using least angular regression to model the antibacterial potential of metronidazole complexes

Imidazole has anti-inflammatory, antituberculotic, antimicrobial, antimycotic, antiviral, and antitumor properties in the human body, to name a few. Metronidazole [1-(2-Hydroxyethyl)-2-methyl-5-nitroimidazole] is a widely used antiprotozoan and antibacterial medication. Using fourier transform infrared spectroscopy, the current study models the antibacterial activity of already synthesised Metronidazole (MTZ) complexes ($$MTZ-Benz$$ M T Z - B e n z , $$MTZ-Benz-Cu$$ M T Z - B e n z - C u , $$MTZ-Benz-Cu-Cl_2CHCOOH$$ M T Z - B e n z - C u - C l 2 C H C O O H , $$MTZ$$ MTZ , $$MTZ-Cu$$ M T Z - C u , $$MTZ-Cu-Cl_2CHCOOH$$ M T Z - C u - C l 2 C H C O O H , $$MTZ-Benz-Ag$$ M T Z - B e n z - A g , $$MTZ-Benz-Ag-Cl_2CHCOOH$$ M T Z - B e n z - A g - C l 2 C H C O O H , $$MTZ-Ag$$ M T Z - A g and $$MTZ-Ag-Cl_2CHCOOH$$ M T Z - A g - C l 2 C H C O O H ) against E. coli, B. bronceptica, S. epidermidis, B. pumilus and S. aureus. To characterise the Metronidazole complexes for antibacterial activity against 05 microbes, the least angular regression and least absolute shrinkage selection operators were used. Asymmetric Least Squares was used to correct the spectrum baseline. Least angular regression outperforms cross-validated root mean square error in the fitted models. Using Least angular regression, influential wavelengths that explain the variation in antibacterial activity of Metronidazole complexes were identified and mapped against functional groups.

Adaptively Allocating Constraint-Handling Techniques for Constrained Multi-objective Optimization Problems

For solving constrained multi-objective optimization problems (CMOPs), an effective constraint-handling technique (CHT) is of great importance. Recently, many CHTs have been proposed for solving CMOPs. However, no single CHT can outperform all kinds of CMOPs. This paper proposes an algorithm, namely, ACHT-M2M, which adaptively allocates the existing CHTs in an M2M framework for solving CMOPs. To be more specific, a CMOP is first decomposed into several constrained multi-objective optimization subproblems by ACHT-M2M. Each subproblem has a subpopulation in a subregion. CHT for each subregion is adaptively allocated according to a proposed composite performance measure. Population for the next generation is selected from subregions by selection operators with different CHTs and the obtained nondominated feasible solutions in each generation are used to update a predefined archive. ACHT-M2M assembles the advantages of different CHTs and makes them cooperate with each other. The proposed ACHT-M2M is finally compared with the other 12 representative algorithms on benchmark CMOPs and the experimental results further confirm the effectiveness of ACHT-M2M for solving CMOPs.

Penerapan Estimasi Posisi dan Tracking Wajah Pada Sistem Presensi Mahasiswa

The current presence system can be done with a computerized system, one of which is the face biometric system. This study focuses on the application of position estimation and tracking based on clustering on people's faces to determine the position in three dimensions. Position estimation can be obtained by making a kernel that is ready to be used to predict three-dimensional coordinates of faces based on two-dimensional coordinates of two images. Position estimation can be done by utilizing the Machine Learning algorithm family. In this study, Least Absolute Shrinkage and Selection Operators (LASSO) is used to perform the position estimation. Meanwhile, clustering in this study uses the K-Means algorithm. Based on the test results, the kernel error obtained in estimating the face location is 9.23 cm. The tracking accuracy of an object based on clustering is 100%.

Evaluating map specifications for automated generalization of settlements and road networks in small-scale maps

The presented research concerns the methodology for selecting settlements and road networks from 1:250 000 to 1:500 000 and 1:1 000 000 scales. The developed methodology is based on the provisions of the Regulation of the Ministry of Interior from 17 November 2011. The correctness of the generalization principles contained in the Regulation has not yet been verified. Thus this paper aims to fulfil this gap by evaluating map specifications concerning settlement and road network generalizations. The goal was to automate the selection process by using formalized cartographic knowledge. The selection operators and their parameters were developed and implemented in the form of a generalization model. The input data was the General Geographic Object Database (GGOD), whose detail level corresponds to 1:250 000 scale. The presented research is in line with works on the automation of GGOD generalization performed by the National Mapping Agency (NMA) in Poland (GUGiK). The paper makes the following contributions. First, the selection methodology contained in the Regulation was formalised and presented in the form of a knowledge base. Second, the models for the generalization process were developed. The developed methodology was evaluated by generalizing the settlements and roads in the test area. The results of the settlement and road network generalization for both 1:500 000 and 1:1 000 000 detail levels were compared with the maps designed manually by experienced cartographers.

Application of Genetic Algorithm on Optimal Pipeline Route Considering Complex Terrains and Obstacles

Gathering network, which is usually characterized by various and complex structure, takes a large proportion of the overall construction cost of gas field. Optimization of pipeline routes is an effective way to reduce the investment. In this paper, a novel model for optimal route of pipeline considering complex terrains and obstacles is proposed and solved by Genetic Algorithm. Minimizing the total investment is the object of this model. Since the construction costs under different terrains are different, the distance factor Li, slope factor Di and elastic factor Si are introduced into the objective function to represent the length of the pipeline, the gradient of the pipeline, and the fluctuation of terrain. In addition, the performance of the model is verified by taking three typical situations of different terrains and obstacles as examples. The results illustrate that the proposed model can address the optimal design of pipeline routes in complex terrains. Moreover, the effects of different genetic operators on solutions are investigated, including three selection operators and two crossover operators. The study provides a guideline for designing pipeline routes in complex terrains and is also applicable to the analogous problem.

Zeolite structure determination using genetic algorithms and geometry optimisation

The recently presented software zeoGAsolver is discussed, which is based on genetic algorithms, with domain-dependent crossover and selection operators that maintain the size of the population in successive iterations while improving the average fitness.