Cellular automata (CA) models have increasingly been used to simulate land use/cover changes (LUCC). Metaheuristic optimization algorithms such as particle swarm optimization (PSO) and genetic algorithm (GA) have been recently introduced into CA frameworks to generate more accurate simulations. Although Markov Chain Monte Carlo (MCMC) is simpler than PSO and GA, it is rarely used to calibrate CA models. In this article, we introduce a novel multi-chain multi-objective MCMC (mc-MO-MCMC) CA model to simulate LUCC. Unlike the classical MCMC, the proposed mc-MO-MCMC is a multiple chains method that imports crossover operation from classical evolutionary optimization algorithms. In each new chain, after the initial one, the crossover operator generates the initial solution. The selection of solutions to be crossed over are made according to their fitness score. In this paper, we chose the example of New York City (USA) to apply our model to simulate three conflicting objectives of changes from non-urban to low-, medium- or high-density urban between 2001 and 2016 using USA National Land Cover Database (NLCD). Elevation, slope, Euclidean distance to highways and local roads, population volume and average household income are used as LUCC causative factors. Furthermore, to demonstrate the efficiency of our proposed model, we compare it with the multi-objective genetic algorithm (MO-GA) and standard single-chain multi-objective MCMC (sc-MO-MCMC). Our results demonstrate that mc-MO-MCMC produces accurate simulations of land use dynamics featured by faster convergence to the Pareto frontier comparing to MO-GA and sc-MO-MCMC. The proposed multi-objective cellular automata model should efficiently help to simulate a trade-off among multiple and, possibly, conflicting land use change dynamics at once.

元胞自动机（CA）模型已越来越多地用于模拟土地利用/覆盖变化（LUCC）。最近将诸如粒子群优化（PSO）和遗传算法（GA）的元启发式优化算法引入到CA框架中，以生成更准确的模拟。尽管Markov Chain Monte Carlo（MCMC）比PSO和GA更简单，但很少用于校准CA模型。在本文中，我们介绍了一种新颖的多链多目标MCMC（mc-MO-MCMC）CA模型来模拟LUCC。与经典MCMC不同，提出的mc-MO-MCMC是一种多链方法，从经典进化优化算法中导入了交叉操作。在每个新链中，在初始链之后，交叉算子会生成初始解。根据适合度来选择要交叉的溶液。在本文中，我们以美国纽约市为例，使用美国国家土地覆被模型来应用我们的模型来模拟从2001年到2016年从非城市到低密度，中密度或高密度城市的三个相互冲突的变化目标数据库（NLCD）。高程，坡度，到高速公路和当地道路的欧几里得距离，人口数量和平均家庭收入被用作LUCC的致病因素。此外，为了证明我们提出的模型的效率，我们将其与多目标遗传算法（MO-GA）和标准单链多目标MCMC（sc-MO-MCMC）进行了比较。我们的结果表明，与MO-GA和sc-MO-MCMC相比，mc-MO-MCMC可以对土地利用动态进行准确的模拟，其特征是与Pareto边界的收敛速度更快。所提出的多目标元胞自动机模型应有效地帮助模拟多个（可能是相互冲突的）土地利用变化动态之间的权衡。