When the ve selected is deviated from the optimum value, the performance of ITIIF algorithm reduces greatly; while as for the static creative style cre_style = 0, the optimizing capacity of ITIIF algorithm is continuously improved with the increase of ��. In general, the SE(t)IQ(t) sellekchem value obtained when dynamic creative style parameter is applied is lower than that when static creative style parameter is applied. So it can be concluded that if the probable range of the optimum value of target can be predicted, it is proper to select dynamic creative style; if the range of target value cannot be confirmed, static creative style is more proper. At the same time, the global optimization capacity of ITIIF algorithm can be improved by increasing �� value. Commonly, �� is set in 0.6 to 0.9.5.2.
Comparison Test5.2.1. Comparison of Optimization Results To better display the performance of ITIIF algorithm, this paper adopts five testing functions to develop simulation tests. Moreover, the simulation results of ITIIF algorithm are compared with those of the currently used binary particle swarm and binary differential evolution algorithm. Table 1 lists the test functions.Table 1The test functions. To provide comparability, the evaluation indexes in this test employ mean best fitness (MBF) and standard deviation (SD). MBF reflects the accuracy that algorithm can achieve when iteration times are given. SD reflects the stability and robustness of algorithm. Table 2 shows the solution results of the testing functions by ITIIF algorithm, binary particle swarm algorithm, and binary differential evolution algorithm.
The values of MBF and SD are obtained by independently running each algorithm for 20 times, respectively. Table 2The solution results of the testing functions by ITIIF algorithm, binary particle swarm algorithm, and binary differential evolution algorithm.It can be seen from Table 2 that the ITIIF algorithm proposed in this study shows better optimization performances to the functions, regardless of unimodal function or multimodal function. Moreover, it achieves more ideal optimization effects in terms of solution accuracy and stability. This result shows that ITIIF algorithm has certain advantages in function optimization.5.2.2.
Performance Comparison The ITIIF algorithm proposed in this study is compared with the commonly used genetic algorithms (GA), estimate distribution algorithms Drug_discovery (EDAs), and ant colony optimization (ACO) in three aspects, which are average distance, average time, and average assessment value. The parameters settings of each algorithm are shown in Table 3.Table 3The parameters settings of each algorithm.The comparison results are presented in Figure 7. It can be obtained that ITIIF and EDAs show better optimization effects, while GA present the poorest optimization effect.