### Table 12 Minimum zone values for the 3-D cylinder example

2004

"... In PAGE 69: ... The zone-fitting method proposed by Choi and Kurfess (1999a, 1999b) and the new zone-fitting algorithm fit the points in the tolerance zone, but the minimum zone values evaluated are different. Table12 gives the minimum zone values calculated by the two methods. The values given by the new zone-fitting algorithm are higher than that ... ..."

### Table 3: Mean Fitness Evaluations in Optimiser v. Optimiser Evolved Landscapes Particle Swarm Optimisation Differential Evolution Newton-Raphson (N-R) Evolutionary Strategy

2005

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

### Table 7. Control statistics for optimised variable topology controllers with component and plan fault-tolerance Fitness

"... In PAGE 7: ...8 Time (s) St r o k e ( mm) Figure 15. Step response for best evolved fault-tolerant controller (thick black curve shows the response without failure, and other curves show the response with each fault) Five GA runs were done using each of the fitness functions, and the fittest results are presented in Table7 and Figures 14-15. The results show that the controllers robust to plant failure do not perform as well as the other evolved controllers.... ..."

### Table 2. Comparison of particle swarms fea- ture selection (PSO-FS) and PCA, applied to model glucose content in soybean crops from hyperspectral data using neural net- works.

### Table 1 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. For com- parison, we have chosen problems P3 to P8 according to [13], where an Evolutionary Strategy for solving constrained problems is presented. The dimension of the search space, the number of particles and the maximum number of function evaluations are listed in columns 2 to 4. The known optimal solution fopt is given in column 5. We used cognitive and social scaling factor values of c1;2 = 0:8 for problem P1 to P5 and c1;2 = 0:4 for P6 to P8. For all benchmark tests the inertia factor was set to a constant value of w = 0:9 while the maximum number of basic PSO iterations was set to kmax = 3. The constraint toler- ances g;h = 10 4 are used for both equality and inequality constraints in all test runs. All experiments were performed in Matlab.

2005

"... In PAGE 6: ... All experiments were performed in Matlab. Table1 : 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.... 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 2- evaluation of the Fault tolerance using the SA-based approach

2002

"... In PAGE 6: ... For the evaluation of the fault tolerant approach, we used randomly generated instances solved using the simulated annealing approach. Table2 summarizes our results. The first two columns indicate the number of objects and the number of dimensions.... ..."

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