Swarm Intelligence Optimization Algorithm Based on Orthogonal Optimization

The shortcomings of existing intelligent optimization algorithms are easy to produce premature convergence, easy to fall into local optimal equilibrium states, and poor efficiency at evolutionary late stage. In order to overcome the above shortcomings, a variety of new strategies and approaches were put forward by researchers in various countries. Although the orthogonal design has been applied to intelligent optimization algorithms, the effect of optimization searching in orthogonal design has not displayed completely because it is limited to be used in initializing the swarm or to be used in optimization searching only before evolution. We discovered the method of confirmation for further searching direction and searching range of orthogonal optimization which is based on the variance analysis and variance ratio analysis of orthogonal design. Making use of the characteristic of orthogonal design which is easy to find an interval that contains the best solution in one arrayed calculation, we put forward an algorithm of orthogonal intelligent optimization based on the analysis of variance ratio which is able to be circulating in the optimization searching. The simulation analysis for six-hump camel back function is performed successfully. The 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.