Swarm Intelligence Algorithm Based on Orthogonal Optimization

In order to overcome premature convergence and low performance of existing intelligent optimization algorithms, a population-based intelligent optimization with algorithm of orthogonal optimization is put forward for continue and discrete function in this paper. The orthogonal optimization based on the variance analysis and variance ratio analysis of orthogonal design is developed, which provides further searching direction and searching range of orthogonal experiment. Because the characteristic of orthogonal design is easy to find an interval that contains the best solution in one arrayed calculation, the algorithm of orthogonal intelligent optimization based on the analysis of variance ratio is able to reuse in the optimization searching. The simulation analysis for constraint satisfaction problem is performed successfully. Numerical result shows that the algorithm of orthogonal intelligent optimization is much better than other algorithms of existing intelligent optimization, which has less calculation amount, shorter searching time, more rapid speed and higher accuracy of optimization searching.