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A. A. A. A. A. A. A. A. A. A. 999762 Deb (2000) [5] CI algorithm Ray et al. [24] Hu et al. (2002) [8] He et al. (2007) [7] Dong et al. 99830 G03 Farmani et al. A. A. A. A. A. A. A. A. A. A. A. A. A. A. A. A. A. A. A. A. A. A. A. A. A. A. A. A. A. A. A. A. A. A. A. A. A. A. A. A. A. A. A. A. A. A. A. A. A. A. A. A. A. A. A. A. A. A. A. A. A. A. A. A. A. A. A. 00330 Coello et al. 00000 Coello et al. 000845 He et al. 00000 He et al. 05290 Kulkarni et al. 4 Statistical results of different methods solving spring design problem Methods Best Mean Worst Std.

00 5 10 15 20 25 30 35 40 45 Variations in Behavior (t) 50 55 (f) Variation in Function Evaluations and Time with respect to Variation in Behavior (t) Fig. 3 Influence of number of candidates (C), reduction factor (r) and variation in behavior (t) on CI algorithm performance increase in the value of reduction rate r larger solution space was available for exploration which when searched resulted into increased converged solution quality as well as associated computational cost. 00E-10 5 10 15 20 25 30 35 40 Variation in Behavior (t) 45 50 55 (l) Variation in Initial and Final Solution with respect to Variation in Behavior (t) Fig.

4 Statistical results of different methods solving spring design problem Methods Best Mean Worst Std. Arora (2004) [25] Coello (2000) [3] Coello et al. (2002) [16] Coello et al. (2004) [10] He et al. (2006) [26] He et al. (2007) [7] Kulkarni et al. A. A. A. A. 000062 over numerous experiments. Since the chosen set of parameters produced sufficiently robust results much effort was not spent in their fine-tuning. Hence, better performance may be obtained through different choice of parameters. The CI was incorporated with penalty function approach [1, 2] and tested by solving several well studied constrained problems.

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