Titlebook: Handbook of Formal Optimization; Anand J. Kulkarni,Amir H. Gandomi Reference work 2024 Springer Nature Singapore Pte Ltd. 2024 Engineering

fiction

Boosting the Efficiency of Metaheuristics Through Opposition-Based Learning in Optimum Locating of Cfor solving complex engineering problems. This chapter provides a comprehensive review of the literature on the use of opposition strategies in metaheuristic optimization algorithms, discussing the benefits and limitations of this approach. An overview of the opposition strategy concept, its various

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NOMAD

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https://doi.org/10.1007/978-3-322-84190-2armers. In this context, the formulation of a mathematical programming model aligned with the real world and considering its uncertainties is highly important. This chapter aims to present an appropriate mathematical programming model for decision-making in determining cropping patterns and optimal

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https://doi.org/10.1007/978-3-322-81609-2ing the strategies and policies employed for the effective management of groundwater resources. The findings from this review highlighted a predominant reliance on demand management policies compared to supply management policies in the management of groundwater resources across various basins. In t

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胶水

https://doi.org/10.1007/978-3-322-84794-2erated in the previous step. After each clustering, using correct data labels, and cluster centroids, the Calinski-Harabasz (CH) index is calculated. Finally, after passing some iterations of GFO algorithm, the best number of clusters is reported. In this study, three categories of data are used to

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https://doi.org/10.1007/978-3-476-99005-1putational resources, transmission of information from previous environments, and handling diversity loss. Based on this classification, researchers can have a better understanding of how these components make evolutionary and swarm intelligence algorithms capable of addressing the challenges of dyn

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进步

https://doi.org/10.1007/978-3-7091-7743-3ost was increased by only 62%. Comparing the best designs and convergence rates, the SSA was more efficient for designing a rectangular combined footing. However, the ISSA produced lower mean, median, and standard deviation values than the SSA. Results indicate the ISSA generated better designs for