Sequential Search Algorithm

The structural response in term of drift and absolute accelerations are shown in Figure 8 when El Centro earthquake is considered using the sequential search algorithm (SS) with the maximum absolute acceleration (J3) and the interstory drift (J4) as objective functions. Both distributions obtained with the two indices have similar performances even if J3 will perform slightly better in accelerations and J4 slightly better in interstory drifts as expected. 

Figure 8. Structural response using sequential search algorithm (SS) with J3 and J4performance index respectively...

Garcia (2001), and Garcia and Soong (2002) developed the simplified sequential search algorithm method (SSSA) which is basically a simpli-... 

Malawer and Montana (14) have developed an efficient iterative, sequential single variable search algorithm which relies upon a polydisperse standard with known Mn and My values. A direct graphical proof of the algorithm was presented. 

In contrast to these interactive searches, which are of little appeal to those with large numbers of searches to carry out, there are available two batch-type searches which accept the complete spectrum of the unknown and examine all spectra in the file sequentially to find the best fits. These are the Biemann and PBM search algorithms. Spectra can be entered from a teletype, but in a more common arrangement, a user s data system can be connected to the network for this purpose and the unknown spectra can be down-loaded into the network computer for use in the search, which can be carried out at once, or, if preferred, overnight at 30% of the cost. 

In general, if it is feasible to carry out an exhaustive search, then that is to be recommended. As the sequential-replacement algorithm is fairly fast, it can always be used first to provide an indication of the maximum size of the subset that is likely... 

There are several direct search techniques for minimizing a function of one variable. The methods generally start from an initial estimate and sequentially move toward the minimum. Univariate or line search techniques play a major role in solving subproblems in more complex direct search algorithms. 

Over the past several years work has been underway on the second generation parallel structure search architecture. This architecture is a completely new hardware and software implementation which builds on the already proven architectural principles of the first generation system as well as taking advantage of inverted file searching rather than sequential searching to implement the screen search algorithm. 

Searching can be thought of as relatively simple in contrast with the many kinds of sorting algorithms. The simple sequential search proceeds stepwise through the data structure. If the item sought is present, it will be found. If it is present, the worst case is when it is last otherwise (that is, when it is not in the list) the search is both unsuccessful and costly in terms of the number of items examined. 

This method starts with an initial configuration of r dampers, denoted by /, and the corresponding objective function is denoted by /. Generally, Ig corresponds to the configuration obtained by either the sequential search method (SS) or any given pre-design method. In the WOBI search algorithms the objective function /is calculated... 

The main role of the ANN in the catalyst design and optimization is to establish an accurate pattern of relationship between the catalytic activity and the influencing factors for it, so as to forecast the experimental results. Based on the pattern of the establishments by ANN, the design and optimization of catalysts have to seek the optimization method, e.g.. Sequential Search, Genetic Algorithm and Ant Colony Algorithmsetc. 

Feature-selection techniques can be generally considered as a combination of both a search algorithm and a criterion function. The solution to feature-selection problem is offered by the search algorithm, which generates a subset of features and compares them on the basis of the criterion function. From a computational viewpoint, an exhaustive search for the optimal solution becomes intractable even for moderate values of features (Siedlecki and Sklansky 1988). Despite these apparent difficulties, many feature-selection approaches have been developed (Serpico and Bruzzone 1994). The sequential forward selection (SFS) and the sequential backward selection (SBS) techniques are the simplest suboptimal search strategies they can identify the best feature subset achievable by adding (to an empty set in SFS) or removing (from the complete set from SBS) one feature at a time, until the desired number of features is achieved. 

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