Optimization Algorithms, especially evolutionary algorithms, have gained wide acceptance among many disciplines such as electrical, control or industrial engineering. The ability to solve an objective or cost function with more unknown parameters than known equations, which make the problem unsolvable by means of deterministic approaches, is the main benefit of using evolutionary algorithms. In all cases optimization algorithms rely on starting with a completely random initial solution set then evolves this set towards better ones in respect to fitness or objective function iteratively. In this study, we have proven that starting from a unique point after a brief local and deterministic search instead of a pure random set is more beneficial in respect to fitness function evaluation count, or computation time. This approach, although it can be applied to any optimization algorithm, is a natural add-on to Big Bang - Big Crunch (BBBC) optimization method.