Application of an Adaptive Multi-Population Parallel Genetic Algorithm with Constraints in Electromagnetic Tomography with Incomplete Projections

Abstract

Electromagnetic tomography technology (EMT) is widely used in underground energy exploration. Limited by objective conditions, the collected projection data of electromagnetic waves are sparse and incomplete. Therefore, a study of the tomographic inversion algorithm of EMT based on incomplete projection data has an important guiding significance for the exploitation of underground energy. As a global optimization probability search algorithm, the simple genetic algorithm (SGA) has been widely used in the process of tomographic inversion. However, SGA evolves through a single population, and the values of crossover and mutation probability are always fixed, so there are risks of premature convergence and poor local search ability. To improve the performance of the SGA, a new approach of adaptive multi-population parallel genetic algorithm (AMPGA) with constraints is proposed in this paper. First, the AMPGA makes full use of multi-group adaptive co-evolution to improve the local and global search ability of SGA and restrain the risk of premature convergence. Then, the introduction of prior information as a constraint makes the results clearer and more accurate. The proposed algorithm has been verified in a numerical experiment and field tests, and the results show that the proposed algorithm can well balance global and local search capabilities, which offers a more realistic and stable tomographic result.

Formation of Regression Model for Analysis of Complex Systems Using Methodology of Genetic Algorithms

“…In the case when the number of the input factors is not large, such problems are solved by the so called brute force method [16]. However, when there are a sufficiently large number of the input factors, solution to this problem can be obtained using the machine learning methods, for example, using the genetic algorithms [5,[17][18][19][20][21][22].…”

“…Let us consider the results of analysis for the H-class. According to the algorithm presented in Section I D, on the basis of the data corresponding to the H-class the following regression model was obtained: y 1 (t) = a (1) + a (2) x 5 (t)x 8 (t) + a (3) x 3 (t)x 11 (t) + +a (4) x 3 (t)x 5 (t) + a (5) x 8 (t)x 9 (t) + +a (6) x 11 (t) + a (7) x 1 (t)x 3 (t) + a (8) x 8 (t) + +a (9) x 9 (t) + a (10) x 4 (t)x 6 (t) + +a (11) x 3 (t)x 6 (t) + a (12) x 1 (t)x 8 (t) + +a (13) x 9 (t)x 11 (t) + a (14) x 11 (t) + +a (15) x 6 (t)x 7 (t) + a (16) x 10 (t)x 11 (t) + +a (17) x 1 (t) + a (18) x 6 (t)x 11 (t) + ( 11) +a (19) x 1 (t)x 2 (t) + a (20) x 6 (t)x 9 (t) + +a (21) x 9 (t)x 10 (t) + a (22) x 2 (t)x 3 (t) + +a (23) x 5 (t)x 6 (t) + a (24) x 1 (t)x 7 (t) + +a (25) x 2 (t)x 13 (t) + a (26) x 4 (t)x 11 (t) + +a (27) x 13 (t) + a (28) x 5 (t)x 11 (t) + +a (29) x 4 (t)x 9 (t) + a (30) x 3 (t) + +a (31) x 3 (t)x 8 (t) + a (32) x 9 (t)x 12 (t) + +a (33) x 7 (t) + a (34) x 10 (t)x 13 (t) + +a (35) x 2 (t)x 10 (t) + a (36) x 7 (t)x 13 (t) + +a (37) x 2 (t)x 9 (t) + a (38) x 5 (t)x 13 (t) + +a (39) x 7 (t)x 11 (t) + a (40) x 1 (t)x 5 (t) + +a (41) x 11 (t)x ...…”

Intelligent system for recruitment decision making using an alternative parallel-sequential genetic algorithm

“…Although HR managers need a powerful tool to efficiently perform mass recruitment, we propose an intelligent system working with a recruitment model and a sequential genetic algorithm (SeqGA) and a parallel genetic algorithm (PGA). The objective is to generate an intelligent recruitment solution for small and large datasets [10,11] because sequential and parallel genetic algorithms are among the effective methods used to solve many practical problems [12][13][14] in particular our recruitment model to have an optimal selection for ensuring a better compatibility with what the company is looking for.…”

Design and Optimization of a Finite Element Model for Electrical Resistance Tomography of Human Lungs

“…Based on the different electrical features of the object in the sensitivity field, the ET techniques could be divided into electromagnetic tomography (EMT), electrical capacitance tomography (ECT), electrical impedance tomography (EIT), and electrical resistance tomography (ERT). Based on the principle of electromagnetic induction, the EMT can reconstruct the distribution state of the magnetic permeability for the medium in the sensitivity field [1][2][3][4][5][6]; Based on the principle of capacitance sensitivity, the ECT can reconstruct the distribution state of the dielectric constant for the medium in the sensitivity field [7][8][9][10][11][12][13][14][15][16][17]; Based on the principle of impedance sensitivity, the EIT can reconstruct the distribution state of the complex admittance for the medium in the sensitivity field [18][19][20][21][22][23][24]; Based on the principle of resistance sensing, the ERT can reconstruct the distribution state of the dielectric resistivity/conductivity for the medium in the sensitivity field [25][26][27][28][29][30].…”