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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.