## 4 Test Simulation and Result Analysis  
### Analysis of Algorithm Convergence Performance  
The parameters of the CSO algorithm and ICSO algorithm are set. The population size is 20; the number of roosters, chicks and hens are 4, 2 and 14; the number of hens with mother-child relationship is 6; the maximum number of cycles is 300. The parameters of the PSO algorithm are set. The population size is 20; the maximum particle speed is 50; the minimum particle speed is \(-\)50; the maximum particle position is 500; the minimum particle position is \(-\)500; and the iteration number is 300. The algorithms are tested on the same platform. The main parameters of the computer used in the simulation experiment are as follows: 15 processor, CPU main frequency 2.5 GHz, memory capacity 2 GB, hard disk capacity 500 GB. Four test functions are used to test the algorithms. The range, optimal value and expression of the functions are as follows. The first test function is D1 (\(D1=\sum_{j=1}^{S}t_{j}^{2}\)), and the optimal value of the function is 0, which ranges from [\(-\)100, 100]; the second test function is D2 (\(D2=\sum_{j=1}^{S}|t_{i}|+\prod_{j=1}^{S}|t_{i}|\)), and the optimal value of the function is 0, which ranges from [\(-\)10, 10]; the third test function is D3 (\(D3=\sum_{j=1}^{S}{(t_{j}+0.5)}^{2}\)), and the optimal value of the function is 0, which ranges from [\(-\)100, 100]; the fourth test function is D4 (\(D4=\sum_{j=1}^{S}{j*t_{j}^{4}+random[0,1)}\)), and the optimal value of the function is 0, which ranges from [\(-\)100, 100]. The dimension of the function is 20. Each function is tested 10 times for the three algorithms. The test results are as follows. Table 1 shows the convergence accuracy and calculation cost of the three optimization algorithms. By analyzing the convergence results of the three algorithms, it is found that neither PSO nor CSO converge to 0 for the D1, D2 and D4 functions. Compared with CSO and ICSO, the convergence result of PSO is poor, which is caused by the fact that the ability of PSO to jump out of the minimum is poor. For D1 and D3, the ICSO converges to 0; for D2 and D4, the ICSO does not converge to 0, but the convergence value of the ICSO is significantly better than that of PSO and CSO. For computing cost, the running time spent by iteration of the PSO is the smallest. For the four test functions, the total time consumed by the PSO program iteration is significantly shorter than that of CSO and ICSO. This is because the structure of the PSO algorithm is simple and the cost of calculation is small. For the four functions, the total time consumed by the PSO for 40 optimizations is 20.93 s; the total time consumed by the CSO program for 40 optimizations is 31.86 s; the total time consumed by the ICSO for 40 optimizations is 40.38 s. The time consumed by running ICSO programs is nearly twice as long as the PSO; it is 26.74% more than the CSO. Compared with PSO and CSO, ICSO runs longer. Because chaotic search is introduced into ICSO algorithm, the computation cost of ICSO is increased. In order to analyze the convergence speed of the three algorithms, Figure 6 shows the convergence curves of the algorithms.