Differential Big Bang - Big Crunch algorithm for construction-engineering design optimization

The present study proposes the Differential Big Bang - Big Crunch (DBB-BC) algorithm. This new hybrid metaheuristic is designed to enhance the performance of the Big Bang-Big Crunch (BB-BC) algorithm. DBB-BC uses collaborative-combination hybridization to combine the BB-BC algorithm, Differential Evolution algorithm, and Neighborhood Search in order to improve the exploration and exploitation capabilities of the original BB-BC in finding global solutions. Subsequently, a number of unconstrained mathematical benchmark problems and seven practical design problems from the construction-engineering field are used to investigate the effectiveness and efficiency of DBB-BC. The results of this investigation confirm that the DBB-BC performs significantly better than the other algorithms that were tested in terms of optimal solution (efficacy) and required function evaluations (efficiency).