Computational optimization, methods and algorithms
Full text is not in this repository.
Official URL: http://www.springerlink.com/content/978-3-642-2085...
Computational optimization is an important paradigm with a wide range of applications. In virtually all branches of engineering and industry, we almost always try to optimize something - whether to minimize the cost and energy consumption, or to maximize profits, outputs, performance and efficiency. In many cases, this search for optimality is challenging, either because of the high computational cost of evaluating objectives and constraints, or because of the nonlinearity, multimodality, discontinuity and uncertainty of the problem functions in the real-world systems. Another complication is that most problems are often NP-hard, that is, the solution time for finding the optimum increases exponentially with the problem size. The development of efficient algorithms and specialized techniques that address these difficulties is of primary importance for contemporary engineering, science and industry.
|Keywords (uncontrolled):||Design optimization - derivative-free optimization - engineering optimization - evolutionary algorithms - firefly algorithm - genetic algorithms - gradient-based method|
|Research Areas:||A. Middlesex University Schools and Centres > School of Science and Technology > Design Engineering and Mathematics|
|Deposited On:||15 Nov 2012 10:44|
|Last Modified:||27 Feb 2015 16:26|
Repository staff only: item control page
Full text downloads (NB count will be zero if no full text documents are attached to the record)
Downloads per month over the past year