Evolutionary methods are well-known techniques for solving nonlinear constrained optimization problems. Due to the exploration\r\npower of evolution-based optimizers, population usually converges to a region around global optimum after several generations.\r\nAlthough this convergence can be efficiently used to reduce search space, in most of the existing optimization methods,\r\nsearch is still continued over original space and considerable time is wasted for searching ineffective regions. This paper proposes\r\na simple and general approach based on search space reduction to improve the exploitation power of the existing evolutionary\r\nmethods without adding any significant computational complexity. After a number of generations when enough exploration\r\nis performed, search space is reduced to a small subspace around the best individual, and then search is continued over this\r\nreduced space. If the space reduction parameters (red gen and red factor) are adjusted properly, reduced space will include global\r\noptimum. The proposed scheme can help the existing evolutionary methods to find better near-optimal solutions in a shorter\r\ntime. To demonstrate the power of the new approach, it is applied to a set of benchmark constrained optimization problems and\r\nthe results are compared with a previous work in the literature.
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