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Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. DOI: Rao Published Mathematics. References and Bibliography. Review Questions. View via Publisher. Save to Library. Create Alert. Launch Research Feed. Share This Paper. Background Citations. Methods Citations. Results Citations. Figures and Tables from this paper.
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Multi-objective design optimization of engineering systems: Uncertainty approach and practical applications. Highly Influenced. View 6 excerpts, cites background, methods and results. Elements of Mathematical Optimization. Modeling, finite element analysis, and optimization of Non-Pneumatic Tire NPT for the minimization of rolling resistance.
View 1 excerpt. Primer interfaced algorithm for non-linear optimization of engineering systems. Developing a fuzzy proportional—derivative controller optimization engine for engineering design optimization problems. View 4 excerpts, cites methods. Use of design optimization techniques in solving typical structural engineering related design optimization problems.
View 1 excerpt, cites background. A tutorial on geometric programming. View 2 excerpts, cites background. An adaptive optimization system applied to machine synthesis. Structural optimization by multilevel decomposition. Sensitivity of Optimum Solutions of Problem Parameters. An improved penalty function method for solving constrained parameter optimization problems.
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Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. DOI: Rao Published Mathematics. References and Bibliography.
MATRIX. STRUCTURAL ANALYSIS,. William. McGuire and Richard H. Gallagher, Wiley, New. York, No. of pages: Price: $ This.
This work proposes a metaphor-less and algorithm-specific parameter-less algorithm, named as self-adaptive population Rao algorithm, for solving the single-, multi-, and many-objective optimization problems. The proposed algorithm adapts the population size based on the improvement in the fitness value during the search process. The population is randomly divided into four sub-population groups.
Harish Garg 1 ,. Akay and D. Karaboga, Artificial bee colony algorithm for large-scale problems and engineering design optimization,, Journal of Intelligent Manufacturing , 23 ,
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