Comparative Study of Impacts of Typical Bio-Inspired Optimization Algorithms on Source Inversion Performance

Abstract

Accurate identification of source information (i.e., source strength and location) is crucial for the air pollution control or effective accidental response. Optimization inversion based on bio-inspired algorithms (BIOs) is an effective method for estimating source information. However, the impacts of different BIOs and the shared parameter of population size in BIOs on source inversion performance have not been revealed. Here the source inversion performance (i.e., accuracy and robustness) of six typical BIOs [i.e., bacterial foraging optimization algorithm (BFO), chicken swarm optimization algorithm (CSO), differential evolution algorithm (DE), genetic algorithm (GA), particle swarm optimization (PSO), and seeker optimization algorithm (SOA)], and their population sizes are evaluated based on the Prairie Grass dataset which covering different atmospheric conditions (i.e., Pasquill stability classes A, B, C, D, E, and F). Results indicated the population size has substantial influence on source inversion. The accuracy of all BIOs in source strength fluctuated greatly when the population size was small, whereas, tended to be stable as the population size increased. Overall, the BFO had the best accuracy with lowest deviations (74.5% for source strength and 29.7 m for location parameter x0), whereas SOA had the best robustness for all source parameters. Atmospheric conditions indicated an obvious influence on the inversion performance of the BIOs. The BFO and CSO performed the best with the lowest deviations [137.5 and 26.7% for unstable conditions (A, B, and C) and stable condition (E), respectively], all algorithms are comparable (67.4 ± 2.1%) in neutral condition (D), and BFO and CSO had the comparable performances (23.2 and 24.3%) and performed better under extremely stable condition (F). This study enhances the understanding of the factors influencing source inversion and provides a reference for the selection of appropriate bio-inspired algorithms and the reasonable setting of population size parameter for source inversion in practical environmental management.

Adaptive Radiation as an Autotuning Strategy for Genetic Algorithms on Dynamic Problems

“…The Particle Swarm Optimization (PSO) is based on the birds' behavior in search for food, and it works with few parameters [6]. Differential evolution generates new descendants by perturbing the solutions with scaled difference vectors, it also presents a small number of tunable components, being a good strategy to find an acceptable solution to a problem of source inversion in practical environmental management [7].…”

Application and Improvement of the Particle Swarm Optimization Algorithm in Source-Term Estimations for Hazardous Release

“…The precision of the forward dispersion model and the method employed to detect source information are critical elements influencing the accuracy of source-term estimations [3]. Most previous studies on source-term estimations have concentrated on the application of various methods [2,[4][5][6][7]. To date, the methods for source-term estimation can be broadly divided into indirect and direct methods.…”

“…However, when the initial values are not chosen properly, such algorithms tend to collapse into local minima, which cannot ensure convergence to the global optimal solution. Heuristic optimization algorithms, including genetic algorithms (GAs) [2,3,6,11,16], simulated annealing algorithms (SAs) [11,14], evolutionary strategies (ESs) [15], particle swarm optimization (PSO) [5,6,17,18], and chicken swarm optimization (CSO) [5], are widely utilized since they have no dependency on initial value selection and can obtain the global optimal solution. To evaluate effective methods for source-term estimations, Ma et al [11] compared the performances of GA, PS, SA, and NM algorithms and their coupled algorithms.…”

“…The test results showed that the performance of different algorithms for estimating different source parameters varied more significantly, with the difference in the relative error being around 12% for source strength and around 25.1% for source location. Mao et al [5] investigated the performance of various bionic optimization methods (BIOs) for source parameter estimations and found that the bacterial foraging optimization approach (BFO) had the best estimation accuracy for the source parameters. These studies demonstrate that the selection of algorithms affects the performance in estimating the source parameters.…”

“…These studies demonstrate that the selection of algorithms affects the performance in estimating the source parameters. The PSO algorithm, which has the advantages of rapid convergence, a simple structure, and few parameters to adjust, is applied to solve high-dimensional nonlinear problems and has been commonly employed for detecting source parameters [5,18,19]. Qiu et al [20] estimated the source parameters utilizing the PSO algorithm based on expectation maximization (EM).…”