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Differential Big Bang - Big Crunch algorithm for construction-engineering design optimization
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DOI：10.1016/j.autcon.2017.10.019.png)
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Abstract
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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).
Keywords:
Metaheuristic
Big Bang Big Crunch
Benchmark functions
Hybrid method
Engineering design problems
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