Authors B. K. Lebedev, O. B. Lebedev, E. O. Lebedeva
Month, Year 04, 2018 @en
Index UDC 004.896
Abstract In this paper, a co-evolution algorithm based on the ant colony method has been proposed for solving the channel routing problem (CRP). The co-algorithm involves the parallel operation of a given number of subalgorithms of the ant colony, which use different, but isomorphic search strategies. The proposed approach allows you to organize a system of collective adaptation with a high degree of expedient behavior and convergence. The key problem that was solved in this paper is related to the development of principles for the interaction of subpopulations, which differ in strategies for finding interpretations of solutions in isomorphic functioning environments. Periodically, agents migrate from one subpopulation to another, transferring their experience. A list is used to interpret the CRP solution. In fact, each list is an indirect (numeric) coding scheme for a QCT solution. A decoder is an operator performing the laying of horizontal fragments according to the rules laid down in it, which allows one to go from an indirect (numerical) coding scheme for solving a problem to a phenotype. The list is decoded into a graphical representation of the solution (sketch) only using the appropriate strategy. The main goal is to find a list for which, using a sequential decoding procedure, fragments of chains are placed in the minimum number of trunks. To construct a constructive (built-in) procedure for finding a solution, the ant uses a modified left-end algorithm. Successive construction of a route is actually a process of sequentially placing horizontal fragments in highways. To enhance the convergence of the algorithm and the ability to exit from local optima, a combined estimate has been developed that characterizes the advantage of choosing a given vertex to include it in the generated route. The basis of the formulas for calculating the probability of the inclusion of a vertex in the formed route is based on heuristic considerations about the preferences that the vertex is part of the optimal route. The co-evolutionary approach provides a wider overview of the solution space and a higher probability of localizing the global extremum of the problem. Compared to existing algorithms, an improvement in the quality of solutions has been achieved up to 5 %.

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Keywords Swarm intelligence; optimization; co-evolutionary algorithm; ant algorithm; hybridization; subpopulation; channel routing; decoder.
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