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

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Zur gegenw?rtigen Naturphilosophieorithms significantly enhances the quality and speed of the optimization process. This chapter aims to provide a clear understanding of the opposition strategy in metaheuristic optimization algorithms and its engineering applications, with the ultimate goal of facilitating its adoption in real-world

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https://doi.org/10.1007/978-3-642-88744-4y highlighting the importance of memory in metaheuristic performance and providing future research directions for improving memory mechanisms. The key takeaways are that memory mechanisms can significantly enhance the performance of metaheuristics by enabling them to explore and exploit the search s

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Solving Cropping Pattern Optimization Problems Using Robust Positive Mathematical?Programmingarmers. 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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Combination of Cooperative Grouper Fish?--?Octopus Algorithm and DBSCAN to Automatic Clusteringerated 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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Multi-population Evolutionary and Swarm Intelligence Dynamic Optimization Algorithms: A Surveyputational 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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Salp Swarm Algorithm for Optimization of Shallow Foundationsost 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

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