Journal of Global Optimization
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Optimal manipulator parameter tolerance selection using evolutionary optimization technique
Engineering Applications of Artificial Intelligence
System design by constraint adaptation and differential evolution
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Opposition-Based Differential Evolution
IEEE Transactions on Evolutionary Computation
Accelerating Differential Evolution Using an Adaptive Local Search
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Parametric yield optimization for MOS circuit blocks
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
The ellipsoidal technique for design centering and region approximation
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Ellipsoidal method for design centering and yield estimation
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Convexity-based algorithms for design centering
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
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A sliding-level orthogonal differential evolution algorithm with a two-level orthogonal array (SLODEA2OA) is proposed for solving worst-case tolerance design problems. Tolerance affects system performance and leads to violate design constraints. By including a two-level orthogonal array, the proposed SLODEA2OA obtains robust optimal solutions that minimize the impact of parameter variations and that maintain compliance with a comprehensive constraint set. Two design examples are used for performance evaluation of the SLODEA2OA. The first is a 10-variable function, which includes linear, non-linear, quadratic, and polynomial forms to illustrate its general robustness and computational efficiency. The second example is a speed reducer design that involves seven variables and multiple non-linear engineering constraints. The SLODEA2OA is also compared with sliding-level orthogonal differential evolution algorithms with either three-level orthogonal array or two-level full-factorial design. Additionally, performance comparisons confirm that the proposed SLODEA2OA outperforms nature-inspired methods presented in the literature.