科研之友
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摘要
A common difficulty for global optimization method is that it is not easy to escape from the local optimal solution and therefore often does not find the global optimal solution. In order to overcome this drawback, first, a smoothing function is constructed that can eliminate all such local optimal solutions worse than the best solution found so far. Second, this smoothing function can keep the local optimal solutions unchanged in the region in which the values of the original function are not worse than its value at the best solution found so far. Third, by making use of the properties of the smoothing function, the uniform design search technique is properly combined into the algorithm, which will make the proposed algorithm converge much faster. Based on all these, a novel effective evolutionary algorithm for global optimization is proposed. At last, the numerical simulations for several standard benchmark problems are made and the simulation results show that the proposed algorithm is very effective.
