The Big Bang–Big Crunch (BB–BC) optimization algorithm is a new optimization method that relies on the Big Bang and Big Crunch theory, one of the theories of the evolution of the universe. This method is among the heuristic population-based search procedures that incorporate random variation and selection, such as genetic algorithm (GA) and simulated annealing (SA). Alongside the main advantages of these methods, the problems resulting from the improper distribution of candidate solutions cannot be ignored,especially for high-dimensional functions. In this paper a method, namely Audze-Eglais’ approach, has been applied to produce population that increases accuracy via homogeneous candidate solutions. Numerical results demonstrate the efficiency of the improved BB-BC method compared to other heuristicalgorithm.

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