By L. Shi, K. Rasheed (auth.), Yoel Tenne, Chi-Keong Goh (eds.)
In smooth technological know-how and engineering, laboratory experiments are changed by way of excessive constancy and computationally dear simulations. utilizing such simulations reduces expenses and shortens improvement occasions yet introduces new demanding situations to layout optimization procedure. Examples of such demanding situations contain restricted computational source for simulation runs, complex reaction floor of the simulation inputs-outputs, and etc.
Under such problems, classical optimization and research tools may possibly practice poorly. This motivates the applying of computational intelligence equipment resembling evolutionary algorithms, neural networks and fuzzy good judgment, which frequently practice good in such settings. this can be the 1st e-book to introduce the rising box of computational intelligence in pricey optimization difficulties. subject matters lined include:
- Dedicated implementations of evolutionary algorithms, neural networks and fuzzy logic.
- Reduction of costly reviews (modelling, variable-fidelity, health inheritance).
- Frameworks for optimization (model administration, complexity keep watch over, version selection).
- Parallelization of algorithms (implementation matters on clusters, grids, parallel machines).
- Incorporation of professional platforms and human-system interface.
- Single and multiobjective algorithms.
- Data mining and statistical analysis.
- Analysis of real-world situations (such as multidisciplinary layout optimization).
The edited ebook presents either theoretical remedies and real-world insights won through event, all contributed via best researchers within the respective fields. As such, it's a entire reference for researchers, practitioners, and advanced-level scholars attracted to either the idea and perform of utilizing computational intelligence for pricey optimization problems.
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Extra info for Computational Intelligence in Expensive Optimization Problems
2 Supersonic Aircraft Design Domain This domain concerns the conceptual design of supersonic transport aircraft. It is summarized briefly here and is described in more detail in . Fig. 14 shows a diagram of a typical airplane automatically designed by the software system. The GA attempts to find a good design for a particular mission by varying twelve of the aircraft conceptual design parameters over a continuous range of values. An optimizer evaluates candidate designs using a multidisciplinary simulator.
PPSN 2000. LNCS, vol. 1917, pp. 879–888. : The influence of migration sizes and intervals on island models. In: Proceedings of the 2005 conference on Genetic and evolutionary computation, pp. : Learning to be selective in genetic-algorithm-based design optimization. : Validating a model of colon colouration using an evolution strategy with adaptive approximations. , et al. ) GECCO 2004. LNCS, vol. 3103, pp. 1005–1016. : Decreasing the number of evaluations in evolutionary algorithms by using a meta-model of the fitness function.
The goal for this design is to use the least material to sustain a certain weight. It has four design variables x = (h, l,t, b); the definition of Welded Beam Design problem can be found in the appendix. 2444. We demonstrate it with three GA methods, GADO, GADO-R and ASAGA. GADO  stands for Genetic Algorithm for Design Optimization, a GA that has proved to Fig. 12 Welded Beam Structure 1 A Survey of Fitness Approximation Methods 21 Fig. 38116 be powerful for solving engineering design problems.