Radio-detector minerals

Abstract

The tower crane layout (TCL) problem, a typical construction site layout (CSL) problem, is currently used in a wide range of construction projects and site conditions. The tower crane is a key facility used in the vertical and horizontal transportation of materials, particularly heavy prefabrication units such as steel beams, ready-mixed concrete, prefabricated elements, and large-panel formwork. Matching the location of tower cranes to material supply and engineering demands is a combinatorial optimization issue within the TCL problem that is difficult to resolve. Swarm intelligence (SI) is a popular artificial intelligence technique that is used widely to resolve complex optimization problems. Various SI-based algorithms have been developed that emulate the collective behavior of animals such as honey bees (bee algorithm, BA) and birds (particle swarm optimization, PSO). This study applies the particle bee algorithm (PBA), a hybrid swarm algorithm that integrates the respective advantages of honey bee and bird swarms, to the TCL problem. The performances of PBA, BA, and PSO are compared in terms of their effectiveness in resolving a practical TCL problem in construction engineering. Results show that the PBA performs better than both the BA and PSO algorithms.