Metaheuristics in Water, Geotechnical and Transport Engineering

[asim99]

Metaheuristics in Water, Geotechnical and Transport Engineering

Author: Xin-She Yang, Amir Hossein Gandomi, Siamak Talatahari, Amir Hossein Alavi | Size: 9 MB | Format: PDF | Quality: Unspecified | Publisher: Elsevier | Year: 2013 | pages: 503 | ISBN: 9780123982964

Due to an ever-decreasing supply in raw materials and stringent constraints on conventional energy sources, demand for lightweight, efficient and low cost structures has become crucially important in modern engineering design. This requires engineers to search for optimal and robust design options to address design problems that are often large in scale and highly nonlinear, making finding solutions challenging. In the past two decades, metaheuristic algorithms have shown promising power, efficiency and versatility in solving these difficult optimization problems.

This book examines the latest developments of metaheuristics and their applications in water, geotechnical and transport engineering offering practical case studies as examples to demonstrate real world applications. Topics cover a range of areas within engineering, including reviews of optimization algorithms, artificial intelligence, cuckoo search, genetic programming, neural networks, multivariate adaptive regression, swarm intelligence, genetic algorithms, ant colony optimization, evolutionary multiobjective optimization with diverse applications in engineering such as behavior of materials, geotechnical design, flood control, water distribution and signal networks. This book can serve as a supplementary text for design courses and computation in engineering as well as a reference for researchers and engineers in metaheursitics, optimization in civil engineering and computational intelligence.

Provides detailed descriptions of all major metaheuristic algorithms with a focus on practical implementation
Develops new hybrid and advanced methods suitable for civil engineering problems at all levels
Appropriate for researchers and advanced students to help to develop their work

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[kowheng]

Size: 9 MB | Format: PDF

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