## 1 Introduction  
In manufacturing industries, facility layout problems (FLP) are one of the most important issues among the various aspects of manufacturing system management. FLPs have a significant impact upon manufacturing costs, lead times, work processes and productivity [1]. The suitable placement of facilities contributes to reduction of the total operating costs by 20% to 50% [2]. Based on shapes and dimensions, FLPs can be divided into different categories. Among them, unequal area FLP (UA-FLP) has been attracting attention of many researchers because it has been applied to many fields such as industrial facility design, warehouse organization, school facility layout, and VLSI element placement [3; 4]. In most studies, the main goal for UA-FLP is to obtain the minimal operating costs [5]. However, UA-FLP has been proven to be an NP-hard combinatorial optimization problem. That is, when the number of facilities is increased, the computational time is exponentially increased [6]. As a sequence, during the last decades more and more research has proposed various metaheuristic approaches to solve UA-FLP by finding the approximate optimal solution in finite time [7; 8; 9; 10; 11; 12]. Among them, the most popular and widely used research is genetic algorithm (GA). GA, proposed by [PERSON] [13] and [PERSON] [14], is based on [PERSON]'s principles of natural selection and divergence [15]. GA is a global search algorithm, which has typical applications in different fields to solve optimization problems [16; 17; 18; 19; 20]. For instance, recently GA has been proven for its effectiveness in enhancing the opportunity to achieve a global optimal solution without falling into the local optimal solution to UA-FLP [11; 12]. However, approaches based on standard GAs usually have premature convergence, which means a population for an optimization problem converged too early to get optimal solution. Moreover, all the individuals in a population should be selected, evaluated, crossed, and mutated in each generation, which requires a long CPU execution time [21]. Many models of parallel GAs have been proposed to shorten the computation time in the literature. Two categories of parallel GAs are island model and global parallelization [22]. In recent work, [PERSON] et al. [22] compared four parallel architectures including multi-core CPUs, clusters, grids, and graphic process units (GPUs) for the execution of the two models of the genetic algorithms. In this study, we pay close attention to how to use GPU to parallelize island model of genetic algorithm (IMGA). Nowadays, modern GPUs have evolved into very powerful and flexible processors. Especially after CUDA (compute unified device architecture) platform were distributed, developing highly parallel GPU applications becomes much easier. GPUs are very well to address general problems that are suitable for data-parallel computations, including GA [23; 24; 25]. The IMGA, a parallel genetic algorithm model, can fully explore the computing power by either coarse or fine-grained parallelisms. It is worthwhile to study how to parallelize IMGA on GPUs to solve real-world optimization problems. Nevertheless, only few works have discussed this issue before. [PERSON] and [PERSON] [26] proposed three different general schemes for building efficient island models for GA on GPU. Their experiments indicated that GPU computing can not only speed up the search process, but also exploit parallelism to improve the quality of the obtained solutions. The authors strongly believed that the schemes of the fully distributed island model on GPU could be easily extended to large scale optimization problems. [PERSON] and [PERSON] [22] discussed the island model and global parallelization for evolutionary algorithm in detail. The methods of IMGA on GPU are based on the work reported in the work [26], and a common Weierstrass function is used to compare the performances between GPU and CPU.