A Hybrid Intelligent Optimization Algorithm of Fast Convergence

Abstract

A hybrid intelligent optimization algorithm based on quantum particle swarm is presented to solve the problem that the local search ability of traditional SFLA is poor and converges very slowly. The particle is quantized and introduced chaos mechanism in the algorithm in order to enhance the global search ability, using the escape strategy, the group is divided into three clusters and mutation operation on the cluster within individuals, not only improves the convergence speed and ensure the performance of the algorithm. Experiments show that the improved algorithm has the characteristics of strong optimization capability and performance is improved greatly in whether comparison of the baseline function or analysis of universal database, compared with the other two algorithms have obvious advantages.

Unsupervised Segmentation of Images Based on Shuffled Frog-Leaping Algorithm

“…According to the articles [11,12,[16][17][18], H parameter is a random number that varies between 0 and 1. To improve the performance of the proposed algorithm and to highlight the influence of this parameter on the results obtained, we varied H as shown in the following Table 4.…”