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- W2119091245 abstract "Ant colony optimization (ACO) allows fast near optimal solutions to be found. It is useful in industrial environments where computational resources and time are limited. Like several search methods, being trapped in local optima within a search space currently is still nontrivial difficulty for ACO. In order to relieve such a difficulty, this paper focuses on modifying ant colony optimization by performing a couple of operations: i) during runtime, adjusting the parameter Q <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>0</sub> , which relates to exploitation and exploration in the local search of ACO. ii) immediately getting away from the trap by reinitializing Q <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>0</sub> in a way of exploration. Modifying ACO by using genetic algorithm (GA) is one of several ways to accomplish i) and ii). Such a modification is called here ACO-GA. As well as GA, particle swarm optimization (PSO) could be applied in the modification of ACO in a similar manner. The latter modification is referred as ACO-PSO. The comparison is made using the NP-hard Traveling Salesperson Problem (TSP) which represents the fundamental principle of such optimizations as cost and time minimization in several industrial applications. The results show that the modifications of ACO surpass over the traditional ACO in terms of accuracy rate and iteration utilization. In the average of the TSP problems applied here, ACO-PSO generates 39.163% better than ACO-GA regarding the iteration consumption. One of the experiments also indicates that reinitializing Q <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>0</sub> may not help improving the solution. The evolution of Q <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>0</sub> is well enough to find the near optimum. In addition, it is also found that for the large number of nodes, the high exploration is required for achieving the minimum fitness or the least total distances." @default.
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- W2119091245 date "2008-06-01" @default.
- W2119091245 modified "2023-09-24" @default.
- W2119091245 title "Modifying Ant Colony Optimization" @default.
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- W2119091245 doi "https://doi.org/10.1109/smcia.2008.5045942" @default.
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