Download Problem Solving and Uncertainty Modeling through by Pratiksha Saxena, Dipti Singh, Millie Pant PDF

By Pratiksha Saxena, Dipti Singh, Millie Pant

Optimization suggestions have constructed right into a modern day resolution for real-world difficulties in quite a few industries. in order to enhance functionality and deal with problems with uncertainty, optimization examine turns into a subject matter of distinct curiosity throughout disciplines.

Problem fixing and Uncertainty Modeling via Optimization and smooth Computing Applications provides the most recent examine traits and advancements within the quarter of utilized optimization methodologies and smooth computing ideas for fixing advanced difficulties. Taking a multi-disciplinary method, this serious ebook is a necessary reference resource for engineers, managers, researchers, and post-graduate students.

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Additional info for Problem Solving and Uncertainty Modeling through Optimization and Soft Computing Applications

Sample text

1999). Simultaneous Optimization of Feeding Rate and Operation Parameters for Fed-Batch Fermentation Processes. Biotechnology Progress, 15(5), 949–952. , & Huang, F. (2010). Parameter analysis based on stochastic model for differential evolution algorithm. Applied Mathematics and Computation, 217(7), 3263–3273. , & Zhang, G. (2007). A Dynamic Clustering Based Differential Evolution Algorithm for Global Optimization. European Journal of Operational Research, 183(1), 56–73. 1016/j. , & Pant, M. (2014).

0e+00* 2. With Respect to Molecular Potential Energy Problem: The result and comparison to Molecular Potential Energy problems of the proposed algorithm with the basic DE algorithm are given in Table 5 for different dimension d= 15, 20 and 25. From Table 5, it is illustrated that the total NFE taken by CuDE (m= 20) are 33570, 58950 and 65600 for dimensions d= 15, 20 and 25 respectively, while NFE taken by CuDE (m= 30) are 46560, 77610 and 115400 and that by basic DE are 100675, Table 5. 6 sec A Cultivated Variant of Differential Evolution Algorithm for Global Optimization Figure 2.

A Dynamic Clustering Based Differential Evolution Algorithm for Global Optimization. European Journal of Operational Research, 183(1), 56–73. 1016/j. , & Pant, M. (2014). A Differential Evolution Approach for Solving Integer Programming Problems. Advances in Intelligent Systems and Computing, 413–424. , & Deep, K. (2015). A new guiding force strategy for differential evolution. International Journal of System Assurance Engineering and Management. 1007/s13198-014-0322-6 ENDNOTES 1 2 Optimization problem is the problem of searching the best solution from all the available solutions.

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