Construction site layout (CSL) represents multi-criteria approach to solving problems which related to site planning and design. Arrange a set of predetermined facilities into appropriate locations is a difficult problem as there are many possible alternatives. Due to the high complexity of site layout problems, many algorithm based on metaheuristic methods have been developed to generate solutions for the problems. Previous metaheuristic methods such as particle swarm optimization (PSO), genetic algorithm (GA), differential evolution (DE), and firefly algorithm (FA), designate a computational method to optimize a problem, but these methods have their own drawbacks. To lessen those drawbacks, this study propose a new hybrid meta-heuristic model namely particle firefly differential algorithm (PFDA). This algorithm combines the advantages PSO, FA, and DE. This hybrid integrates the local search ability of PSO and global search ability of FA and DE. There are three phases in PFDA, first is PSO phase, which stores the best value and focus on exploitation. Second and third phase are proceed as parallel way, FA and DE. Both of them focus on exploration. This study compares the performance of PFDA with GA, PSO, FA, DE, bee algorithm (BA), and particle bee algorithm (PBA) for multidimensional benchmark function problems. Moreover, this study compares PFDA performance against original PSO, DE, FA, and the previous research works in site facility layout problems. The results show that PFDA's performance is better than those mentioned algorithms in the benchmark functions and outperforms the existing optimization algorithms in solving constructions site layout problem.

Construction site layout (CSL) represents multi-criteria approach to solving problems which related to site planning and design. Arrange a set of predetermined facilities into appropriate locations is a difficult problem as there are many possible alternatives. Due to the high complexity of site layout problems, many algorithm based on metaheuristic methods have been developed to generate solutions for the problems. Previous metaheuristic methods such as particle swarm optimization (PSO), genetic algorithm (GA), differential evolution (DE), and firefly algorithm (FA), designate a computational method to optimize a problem, but these methods have their own drawbacks. To lessen those drawbacks, this study propose a new hybrid meta-heuristic model namely particle firefly differential algorithm (PFDA). This algorithm combines the advantages PSO, FA, and DE. This hybrid integrates the local search ability of PSO and global search ability of FA and DE. There are three phases in PFDA, first is PSO phase, which stores the best value and focus on exploitation. Second and third phase are proceed as parallel way, FA and DE. Both of them focus on exploration. This study compares the performance of PFDA with GA, PSO, FA, DE, bee algorithm (BA), and particle bee algorithm (PBA) for multidimensional benchmark function problems. Moreover, this study compares PFDA performance against original PSO, DE, FA, and the previous research works in site facility layout problems. The results show that PFDA's performance is better than those mentioned algorithms in the benchmark functions and outperforms the existing optimization algorithms in solving constructions site layout problem.

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