Solving optimization problems in construction site layout planning based on quadratic problem solving method by using Ant Lion hybrid algorithm

Purpose This study aims to enhance the optimization of construction site layouts by addressing the shortcomings of existing meta-heuristic methods like Genetic Algorithms (GA) and Ant Lion Optimization (ALO). These traditional approaches often suffer from slow convergence and limited accuracy when tackling the complex Quadratic Assignment Problem (QAP). To overcome these challenges, this research introduces a novel hybrid algorithm, M-AL, which integrates Opposition-Based Learning (OBL), Uniform Mutation and Single-Point Crossover to improve efficiency and precision in construction site layout optimization. Design/methodology/approach The research develops the M-AL algorithm by combining the strengths of ALO with OBL, Uniform Mutation and Single-Point Crossover. The algorithm’s performance was rigorously tested on the QAP, a standard benchmark problem, and was compared to traditional GA and ALO methods. Computational experiments focused on evaluating the algorithm’s convergence speed and solution accuracy, providing a comprehensive assessment of its potential to optimize construction site layouts effectively under real-world constraints. Findings The M-AL hybrid algorithm demonstrated significant improvements over conventional meta-heuristic methods, including GA and ALO, in optimizing construction site layouts. The results show that M-AL achieves faster convergence and greater accuracy in solving the QAP, highlighting its superiority in managing the complexities of site layout planning. These findings suggest that M-AL is a powerful tool for construction managers, offering enhanced decision-making capabilities and more efficient resource allocation, leading to better project outcomes. Originality/value This research introduces a novel and scalable hybrid algorithm, M-AL, that addresses the inherent limitations of existing metaheuristic approaches. By integrating OBL, Uniform Mutation and Single-Point Crossover, M-AL provides a robust solution to the QAP. Its practical application in construction site layout optimization makes it a valuable asset for improving project efficiency, reducing costs and advancing overall construction management practices.