### TABLE I PARAMETERS OF THE PARTICLE SWARMS. Parameter Value

### Table 1. Parameters of the particle swarms.

### Table 1: Results of 30 independent runs on 8 benchmark tests using Augmented Lagrange Particle Swarm Optimization. Column 2 shows the number of particles np and column 3 the number of function calls nf. Details about the test functions can be found in the Appendix.

2005

"... In PAGE 6: ...(18), we maintain the magnitude of the penalty factors such that an e ective change in Lagrange multipliers is possible. This lower bound is formulated by rp;i 1 2 s j ij g;h : (20) Table1 summarizes the experimental results using ALPSO for solving eight constrained benchmark problems. All results show the average values of 30 independent runs on each test function.... In PAGE 7: ...with [13] the results from ALPSO are comparable or superior with less function evaluations required. The number of function evaluations listed in Table1 represents an upper limit where we stopped the optimization process. However, the best solution of each run was usually found much earlier.... ..."

### Table 4 Classification results with different reducts 1: Number of rules; 2: Classification accuracy POSAR CEAR DISMAR GAAR PSORSAR

"... In PAGE 25: ... So, all the particles have a powerful search capability, which can help the swarm avoid dead ends. The comparison of the number of decision rules and the classification accuracy with different reducts are shown in Table4... ..."

### Table 1: The number of updates made for each swarm particle (P). Each column represents the mo- ment (M) of update. Each row is reserved for a

in Algorithms

"... In PAGE 5: ... The quality of the second and the third particles is im- proved in the last two iterations. Table1 presents the number of updates performed for each swarm particle in a particular run of the proposed algorithm. Table 1: The number of updates made for each swarm particle (P).... ..."

### Table 1. Performance Characteristics of Different AM Implementations

1997

"... In PAGE 7: ...ficient, buffered writes in the SCI DSM only. Performance measurements on the UCSB SCI cluster show competitive performance behavior of the SCI AM system ( Table1 ). Our own implementation, depicted in the first row of Table 1, adds little over- head to the raw latency of 9.... ..."

Cited by 13

### Table 10 PSO searching process on Lung Iter Best Solution Fitness

"... In PAGE 35: ...35 Example 5 The process of the particle swarms searching for optimal solutions for dataset Lung is given in Table10 and Figure 7. Table 10 PSO searching process on Lung Iter Best Solution Fitness ... ..."

### Table 11 PSO searching process on DNA Iter Best Solution Fitness

"... In PAGE 36: ...36 Example 6 The process of the particle swarms searching for optimal solutions for dataset DNA is given in Table11 and Figure 8. Table 11 PSO searching process on DNA Iter Best Solution Fitness ... ..."

### Table 5: Comparisons of via number and runtime for the sequential optimization with steady-state analysis, the sequential optimization with transient analysis and the simultaneous optimization with transient analysis.

"... In PAGE 6: ... straints with targeted voltage violation Vmax of 0:2V , and then allocate vias to satisfy the thermal integrity constraints with tar- geted temperature Tmax of 52 C and considering the already stapled \power/ground quot; vias for heat removal. Table5 presents the results. The vias are over-designed when using steady-state analysis.... In PAGE 6: ... Finally, we compare the results using simultaneous optimization and sequen- tial optimization. On average, our simultaneous optimization fur- ther reduces 34% vias compared to the the sequential optimiza- tion by steady-state analysis in Table5 , and reduces 22:5% vias compared to the sequential optimization with transient analysis. 6.... ..."