Metaheuristics: Outlines, MATLAB Codes and Examples (Hardcover)
Ali Kaveh
Sold by AussieBookSeller, Truganina, VIC, Australia
AbeBooks Seller since June 22, 2007
New - Hardcover
Condition: New
Quantity: 1 available
Add to basketSold by AussieBookSeller, Truganina, VIC, Australia
AbeBooks Seller since June 22, 2007
Condition: New
Quantity: 1 available
Add to basketHardcover. The book presents eight well-known and often used algorithms besides nine newly developed algorithms by the first author and his students in a practical implementation framework. Matlab codes and some benchmark structural optimization problems are provided. The aim is to provide an efficient context for experienced researchers or readers not familiar with theory, applications and computational developments of the considered metaheuristics. The information will also be of interest to readers interested in application of metaheuristics for hard optimization, comparing conceptually different metaheuristics and designing new metaheuristics. The book presents eight well-known and often used algorithms besides nine newly developed algorithms by the first author and his students in a practical implementation framework. Matlab codes and some benchmark structural optimization problems are provided. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Seller Inventory # 9783030040666
The book presents eight well-known and often used algorithms besides nine newly developed algorithms by the first author and his students in a practical implementation framework.
Matlab codes and some benchmark structural optimization problems are provided. The aim is to provide an efficient context for experienced researchers or readers not familiar with theory, applications and computational developments of the considered metaheuristics.
The information will also be of interest to readers interested in application of metaheuristics for hard optimization, comparing conceptually different metaheuristics and designing new metaheuristics.
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