## 1 Introduction  
Determining the best site and facility layout, especially for large construction projects, has always been a critical and challenging task for construction managers. Poor layout can lead to decreased workers' productivity, overloading of material transportation and handling, longer queues, and ultimately an increased budget. Hence, layout planning is important for project success because it has a direct impact on different kinds of construction operation performance, such as transportation costs, the flow of work, and productivity [1]. An ineffective layout can increase material handling costs by over 35%. Studies have suggested that an effective layout can enhance the general effectiveness of operations and lower operating expenses between 20% to 50% [2]. Accordingly, an increasing number of studies has focused on facility layout planning in numerous areas, including industrial engineering, construction management, and architecture. The construction site layout planning (CSLP) problem, from the perspective of an optimization problem, is commonly addressed as a quadratic assignment problem (QAP). In CSLP problem, \(n\) facilities are assigned to \(n\) locations with the assumption that every single facility requires the same space. Hence, it is possible to assign any facility to any site [3]. The CSLP is regarded as non-polynomial hard (NP-hard), where this kind of problem cannot be solved in polynomial time [4]. In the case of CSLP with a large number of facilities, it is hard to determine the optimal solution within an acceptable time frame [5]. Approximately, there are \(n!\) possible alternative solutions for CSLP with \(n\) facilities. For example, if the construction project has 10 facilities, there will be more than 3 million possible alternatives for the site layout. Many studies have been conducted to develop the methods for obtaining an optimal solution within a limited computational time. Several mathematical exact approaches and heuristic methods were built in early attempts to address the CSLP problem, including the branch-and-bound [6], dynamic programming [7], and integer linear programming [8]. Nevertheless, the exact approaches are often inadequate in tackling large problems, while the heuristic search techniques appear reasonable since they are an inexact method for obtaining near-optimal solutions. [PERSON] et al. [9] have presented a greedy heuristic approach for handling a facility layout problem. However, the quality solution produced by heuristic methods is often poor. This situation, because the heuristic method on many occasions is too greedy, results from the inability to escape the local optima, and consequently, failing to propose the global solution. The past decades have seen the rapid growth of metaheuristic algorithm applications in solving the CSLP problem due to their ability to solve complex and large optimization problems from various disciplines [10]. Most metaheuristic algorithms allow their searching mechanism to accept less preferable candidate solutions leading to a wider solution space coverage and possibly a better result in comparison to the traditional heuristic methods. Metaheuristic has been widely known as an effective tool for handling NP-hard problems [11; 12]. Examples of metaheuristic techniques applied to CSLP problem include artificial bee colony (ABC) [13], tabu search [14], ant colony optimization (ACO) [15], particle swarm optimization (PSO) [16; 17], and genetic algorithm (GA) [8; 18; 19]. As the project has become larger, more advanced technique development is needed to handle the complex layout design problem. Recently, [PERSON] and [PERSON] introduced a metaheuristic optimization algorithm, known as the symbiotic organisms search (SOS) [20]. It adopts the natural adaptive interactions among living organisms in one ecosystem. At first, the SOS was utilized for resolving the continuous optimization problems, such as engineering design problems and the complex benchmark functions.