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- W2726271831 abstract "Several researches have pointed the hybridization of metaheuristics as an effective way to deal with combinatorial optimization problems. Hybridization allows the combination of different techniques, exploiting the strengths and compensating the weakness of each of them. MAHM is a promising adaptive framework for hybridization of metaheuristics, originally designed for single objective problems. This framework is based on the concepts of intelligent agents and Particle Swarm. In this study we propose an extension of MAHM to the multi-objective scenario. The proposed framework is called MO-MAHM. To adapt MAHM to the multi-objective context, we redefine some concepts such as particle position and velocity. In this study the proposed framework is applied to the multi-objective Symmetric Travelling Salesman Problem. Four methods are hybridized: PAES, GRASP, NSGA-II and Anytime-PLS. Experiments with 12 bi-objective instances were performed and the results show that MO-MAHM is able to provide better non-dominated sets in comparison to the ones obtained by each of the hybridized algorithms." @default.
- W2726271831 created "2017-07-14" @default.
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- W2726271831 date "2017-06-01" @default.
- W2726271831 modified "2023-09-25" @default.
- W2726271831 title "MO-MAHM: A parallel Multi-agent Architecture for Hybridization of Metaheuristics for multi-objective problems" @default.
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- W2726271831 doi "https://doi.org/10.1109/cec.2017.7969363" @default.
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