genetic algorithm

Optimization of Chordal Ultrasonic Flowmeters Design Using a Hybrid Genetic Algorithm

The paper proposes a solution to the relevant scientific and applied problem of optimizing the design of chordal multi-path ultrasonic flowmeters for measuring the flow rate of distorted flows. The object of the study is the integration accuracy of the velocity profile of distorted flows using numerical integration methods. The authors have developed a methodology for searching for the optimal design parameters of chordal multi-path ultrasonic flowmeters (location coordinates and weight coefficients of their acoustic paths) based on a hybrid genetic algorithm.

Using the Mountain Gazelle Optimizer to Construct Consistent Pairwise Comparison Matrices Within Acceptable Consistency Ratio Thresholds

The Analytic Hierarchy Process (AHP) fundamentally relies on the pairwise comparison matrix (PCM) as a core component in structuring multi-criteria decision-making problems, with applications spanning numerous disciplines.  This paper introduces a novel methodology for generating consistent PCMs that satisfy a predefined threshold for the consistency ratio (CR).  The proposed approach leverages the Mountain Gazelle Optimizer (MGO), a recent metaheuristic algorithm inspired by the social hierarchy and herd behavior of wild mountain gazelles.  Numerical experiments demons

Enhancing flood forecasting accuracy through improved SVM and ANFIS techniques

Extreme rainfall in upstream watersheds often results in the rise of river water levels, leading to severe flood disasters in the downstream catchment.  Therefore, monitoring river water levels and flow is crucial for flood forecasting in early warning systems and disaster risk reduction.

INVESTIGATION OF SENSOR NODE PLACEMENT ON A PLANE USING A GENETIC ALGORITHM

The study focuses on investigating the efficiency of a genetic algorithm-based sensor node placement method for a random topology. The primary objective is to identify a node configuration that minimizes the number of "blind spots" and ensures the most efficient coverage of a given area. Random node placement is characterized by the potential for each node to establish connections with other nodes, resulting in a complex search space. For this study, 25 nodes with identical sensing radii were analyzed.

PERFORMANCE ANALYSIS OF CNN-ENHANCED GENETIC ALGORITHM FOR TOPOLOGICAL OPTIMIZATION IN METAMATERIAL DESIGN

The Combination of Convolutional Neural Networks (CNN) and Genetic Algorithms (GA) provides a promising approach for topological optimization of complex lattice structures. Lattice structures are commonly used as base in the design of high-performance metamaterials. This paper presents a review of the effectiveness and efficiency of the CNN-GA method. We will examine the ability of the method to generate optimal complex structures while minimizing material usage. CNN is utilized mainly as an analysis instrument.

COMBINED APPROACH TO BUILDING OPTIMAL ROUTES FOR INDIVIDUAL TRIPS IN A MOBILE APPLICATION

The paper deals with building optimal routes for individual trips under the influence of many factors and possible changes in the input parameters (such as weather conditions, traffic congestion, etc). We have analyzed four classes of algorithms for solving the traveling salesperson problem and evaluated their applicability in a tourist mobile application. The software should be a mobile application since only a few travelers take computers or laptops but most of them carry smartphones.

SEARCH FOR A DATA TRANSMISSION ROUTE IN A WIRELESS SENSOR NETWORK USING A GENETIC ALGORITHM

The article is devoted to the application of a genetic algorithm for determining the optimal route in a wireless sensor network. The paper presents a classification of data routing strategies based on: the method of determining routes, network structure, network operations, and communication organiser. The genetic algorithm is classified as a multi-path routing strategy, since its use allows obtaining a set of routes.

COMPUTATIONAL COMPLEXITY EVALUATION OF A GENETIC ALGORITHM

The article is devoted to the estimation of computational complexity of a genetic algorithm as one of the key tools for solving optimisation problems. The theoretical aspects of computational complexity of algorithms and the interrelation of elements of a genetic algorithm are considered. The main types of computational complexity of algorithms are described: time, simple and asymptotic. Five basic rules for calculating the asymptotic complexity are given.

Використання генетичних алгоритмів для апроксимації функцій дійсними поліномами

Наведено метод апроксимації функцій поліномами з дійсними степенями, в якому підбір степеня здійснюється за допомогою генетичного алгоритму.

The method of approximation of functions by polynomials with real powers, which is the power of selection with a genetic algorithm.

A metaheuristic approach to improve consistency of the pairwise matrix in AHP

In this paper, we are interested in modifying inconsistent pairwise comparison matrix which is a critical step in the AHP methodology, where decision makers have to improve the consistency by revising the process.  To this end, we propose an improved genetic algorithm (GA) to allow decision makers to find an appropriate matrix and adjust the consistency of their judgment without loss of original comparison matrix.  Numerical results with different dimensions of matrices taken randomly show the effectiveness of these strategy to improve and identify the consistency of pa