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Ant colony optimization algorithms were originally developed to solve discrete optimization problems, where the values assigned to variables of the solution are constrained by xed nite set of discrete values In order to apply ant colony algorithms to solve continuous optimization problems, the main problem is to determine a way to map the continuous space problem to a graph search problem A simple solution to this problem is to encode oating-point variables using binary string representations [523] If a oating-point variable is encoded using an n bit-string, the graph representation G = (V, E), contains 2n nodes two nodes per bit (one for each possible value, ie 0 and 1) A link exists between each pair of nodes Based on this representation, the discrete ACO algorithms can be used to solve the problem It is, however, the case that binary representation of oating-point values loses precision This section discusses an ACO algorithm for optimizing continuous spaces without discretizing the solution variables The rst ACO algorithm for continuous function optimization was developed by Bilchev and Parmee [67, 68] This approach focused on local optimization Wodrich and Bilchev [915] extended the local search algorithm to a global search algorithm, which was further improved by Jayaraman et al [414], Mathur et al [564] and Rajesh et al [701] This global search algorithm, referred to as continuous ACO (CACO), is described next The CACO algorithm performs a bi-level search, with a local search component to exploit good regions of the search space, and a global search component to explore bad regions With reference to Algorithm 178, the search is performed by nk ants, of which nl ants perform local searches and ng ants perform global searches The rst step of the algorithm is to create nr regions Each region represents a point in the continuous search space If xi denotes the i-th region, then xij U (xmin,j , xmax,j ) for each dimension j = 1, , nx and each region i = 1, , nr ; xmin,j and xmax,j are respectively the minimum and maximum values of the domain in the j-th dimension After initialization of the nr regions, the tness of each region is evaluated, where the tness function is simply the continuous function being optimized Let f (xi ) denote the tness of region xi The pheromone, i , for each region is initialized to one The global search identi es the ng weakest regions, and uses these regions to nd ng.

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No need for programming knowledge and barcode font; Other . Auto mode: encode Barcode library will decide the best ata mode for you. With PDF-417 Add-in for MS-Word, user may use "Data Mode" property in 2D tab of setting panel, and choose Text for example and click quot;update".Related: Barcode Generating .NET Winforms , Barcode Generation SSRS Library, Print Barcode SSRS .NET Winforms

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