Chaotic Hybrid Meta-Heuristic Optimization Algorithm for Construction Site Layout Planning Problem

Abstract

In recent decades, site layout has been a major challenge for researchers in construction management. Recognized as an NP-complete problem, it resists exact solutions, particularly for medium to large-scale projects. Numerous studies have explored metaheuristic approaches to tackle this issue, yet there is a demand for novel methods that promise improved accuracy within shorter computational periods. In this study, a pioneering solution is introduced: a hybrid Ant Lion Optimizer Algorithm (ALO) and Aquila Optimizer Algorithm (AO) based on chaos theory tailored specifically for optimizing construction management tasks. The combination of ALO and the Aquila Optimizer harnesses the strengths of two distinct optimization strategies. ALO mimics the trapping behavior of antlions, striking a balance between exploration and exploitation for optimal outcomes. In contrast, the Aquila Optimizer replicates the dynamic hunting tactics of eagles, facilitating swift and adaptive search methods. By merging these approaches, the hybrid algorithm adeptly navigates complex problems, dynamically adapting to environmental changes. This collaborative synergy holds promise for efficient optimization across various domains. The chaotic hybrid algorithm (CH-ALOAO) utilizes interactive memory to store optimal solutions throughout the optimization process. Its performance is benchmarked against established metaheuristic algorithms regarding search capabilities, avoidance of suboptimal solutions, and convergence speed. The results undergo rigorous statistical analysis, with experimental data showcasing CH-ALOAO's superior performance in addressing construction management optimization challenges compared to its competitors.