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- W1977113315 abstract "In this paper, a novel nature-inspired optimization algorithm has been employed and the trained behaviour of dogs in detecting smell trails is adapted into computational agents for problem solving. The algorithm involves creation of a surface with smell trails and subsequent iteration of the agents in resolving a path. This algorithm can be applied in different computational constraints that incorporate path-based problems. Implementation of the algorithm can be treated as a shortest path problem for a variety of datasets. The simulated agents have been used to evolve the shortest path between two nodes in a graph. This algorithm is useful to solve NP-hard problems that are related to path discovery. This algorithm is also useful to solve many practical optimization problems. The extensive derivation of the algorithm can be enabled to solve shortest path problems." @default.
- W1977113315 created "2016-06-24" @default.
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- W1977113315 date "2015-02-07" @default.
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- W1977113315 title "Smell Detection Agent Based Optimization Algorithm" @default.
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- W1977113315 doi "https://doi.org/10.1007/s40031-014-0182-0" @default.
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