Discovery of Transition Rules for Cellular Automata Using Artificial Bee Colony and Particle Swarm Optimization Algorithms in Urban Growth Modeling

Abstract

This paper presents an advanced method in urban growth modeling to discover transition rules of cellular automata (CA) using the artificial bee colony (ABC) optimization algorithm. Also, comparisons between the simulation results of CA models optimized by the ABC algorithm and the particle swarm optimization algorithms (PSO) as intelligent approaches were performed to evaluate the potential of the proposed methods. According to previous studies, swarm intelligence algorithms for solving optimization problems such as discovering transition rules of CA in land use change/urban growth modeling can produce reasonable results. Modeling of urban growth as a dynamic process is not straightforward because of the existence of nonlinearity and heterogeneity among effective involved variables which can cause a number of challenges for traditional CA. ABC algorithm, the new powerful swarm based optimization algorithms, can be used to capture optimized transition rules of CA. This paper has proposed a methodology based on remote sensing data for modeling urban growth with CA calibrated by the ABC algorithm. The performance of ABC-CA, PSO-CA, and CA-logistic models in land use change detection is tested for the city of Urmia, Iran, between 2004 and 2014. Validations of the models based on statistical measures such as overall accuracy, figure of merit, and total operating characteristic were made. We showed that the overall accuracy of the ABC-CA model was 89%, which was 1.5% and 6.2% higher than those of the PSO-CA and CA-logistic model, respectively. Moreover, the allocation disagreement (simulation error) of the simulation results for the ABC-CA, PSO-CA, and CA-logistic models are 11%, 12.5%, and 17.2%, respectively. Finally, for all evaluation indices including running time, convergence capability, flexibility, statistical measurements, and the produced spatial patterns, the ABC-CA model performance showed relative improvement and therefore its superiority was confirmed.

A hybrid modeling approach considering spatial heterogeneity and nonlinearity to discover the transition rules of urban cellular automata models

“…In recent years, intelligent algorithms have emerged endlessly, and learning models are widely applied to urban CA modeling. It can be found in many studies that the simulation accuracy of learning models is often higher than that of statistical models (Basse et al, 2014; Naghibi and Delavar, 2016; Feng and Liu, 2013; Zhang and Wang, 2022), which leads to the pursuit of learning models and the neglect of statistical models. Nowadays, the available earth system data and its transmission, processing, and application are increasing geometrically (Reichstein et al, 2019).…”

Leveraging Urban Growth Models (UGM) for Sustainable Urban Planning and Climate Resilient Cities: A Bibliometric Analysis

“…km to 9.6 million sq. km for Urmia, Iran [49] and mainland China [46] respectively. It's also found that most researchers have calibrated 10 to 20 years of spatial and non-spatial data and forecasted for 10 to 40 years having a resolution of 30 m by using neighborhood window sizes varying from 3x3 m [10] to 11x11 m. [50] Delicate resolution data and window size 5x5 have given satisfactory results, [12,51]…”

The modeling of effective parameters on public bus passengers’ boarding time prediction

“…The Artificial Bee Colony (ABC) algorithm is a one of the most used metaheuristic search algorithms for optimizing many different real world problems such as transportation, engineering design, scheduling analysis, cost optimization, and logistic planning, as well as the optimization method (Naghibi and Delavar, 2016). ABC is recently developed by Karaboga in 2005, and it was applied to different problems by many researchers (Karaboga, 2005;Karaboga and Basturk, 2008;Karaboga and Akay, 2011;Karaboga et al, 2014;Szeto and Jiang, 2012;Naghibi and Delavar, 2016;Sonmez et al, 2017;Yao et al, 2017). ABC algorithm is inspired by the intelligent foraging behavior of honey bees, and it is mainly based on foraging behaviors of honey bee swarms in their daily life, and the main steps and behaviors of the bees in the algorithm are given below (Figure 4).…”