Wolf-Bird Optimizer (WBO): A Novel Metaheuristic Algorithm for Building Information Modeling-based Resource Tradeoff

Abstract

In the animal kingdom, a mutually-beneficial ecosystemic coexistence and partnership in predation between wolves and ravens, known as the wolf-bird relationship, is observed in various cultures. The Wolf-Bird Optimizer (WBO), a novel metaheuristic algorithm inspired by this natural zoological relationship, is proposed. This method is developed based on the foraging behaviors of ravens and wolves, wherein the intelligence of ravens in finding prey and sending signals to wolves for assistance in hunting is considered. Furthermore, a framework for resource tradeoffs in project scheduling using metaheuristic algorithms and the Building Information Modeling (BIM) approach is established in this research. For statistical analysis, the algorithms are independently run 30 times with a preset stopping condition, enabling the calculation of descriptive statistical metrics such as mean, standard deviation (SD), and the required number of objective function evaluations. To ensure the statistical significance of the results, several inferential statistical methods, including the Kolmogorov-Smirnov, Wilcoxon, Mann-Whitney, and Kruskal-Wallis tests, are employed. Additionally, the capability of the proposed algorithm in solving resource tradeoff problems in four construction projects is assessed. The performance of the WBO algorithm is also evaluated in two benchmark construction projects, with the results indicating the algorithm's ability to produce competitive and exceptional outcomes regarding tradeoffs.