| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 758535 | Communications in Nonlinear Science and Numerical Simulation | 2011 | 8 Pages |
This study proposes methods to improve the convergence of the novel optimization method, Big Bang–Big Crunch (BB–BC). Uniform population method has been used to generate uniformly distributed random points in the Big Bang phase. Chaos has been utilized to rapidly shrink those points to a single representative point via a center of mass in the Big Crunch phase. The proposed algorithm has been named as Uniform Big Bang–Chaotic Big Crunch (UBB–CBC). The performance of the UBB–CBC optimization algorithm demonstrates superiority over the BB–BC optimization for the benchmark functions.
Research highlights► Uniform population has been used in the Big Bang phase of BB–BC optimization. ► Chaos has been utilized in the Big Crunch phase of BB–BC optimization. ► These two approaches have been utilized in the BB–BC algorithm.
