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- W3100699324 abstract "AbstractAnt colony optimization (ACO) algorithm has rapid convergence speed and competitive performance in multi-objective optimization. However, for complicated multi-objective problems (MOPs), it faces the issue of the local optimum trap, which hinders the global optimization of the multi-objective ACO. This paper proposes a multi-objective ACO algorithm with a local optimum avoidance (LOA) strategy, called MACO/D-LOA. The proposed strategy uses a matrix structure to record the paths that ants have visited. Then it controls the selected probability of the recorded paths. This mechanism enhances the exploration research capability of the multi-objective ACO while retaining the exploitation ability. Experimental results on typical test cases indicate that the proposed algorithm obtains competitive convergence results than state-of-the-art algorithms.KeywordsAnt colony optimizationMulti-objective optimizationDiscrete optimizationDecomposition strategy" @default.
- W3100699324 created "2020-11-23" @default.
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- W3100699324 date "2020-01-01" @default.
- W3100699324 modified "2023-09-27" @default.
- W3100699324 title "A Multi-objective Ant Colony Optimization Algorithm with Local Optimum Avoidance Strategy" @default.
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- W3100699324 doi "https://doi.org/10.1007/978-3-030-62460-6_38" @default.
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