Biogeography-based optimization (BBO) is a\nrelatively new heuristic method, where a population of\nhabitats (solutions) are continuously evolved and improved\nmainly by migrating features from high-quality solutions to\nlow-quality ones. In this paper we equip BBO with local\ntopologies, which limit that the migration can only occur\nwithin the neighborhood zone of each habitat. We develop\nthree versions of localized BBO algorithms, which use three\ndifferent local topologies namely the ring topology, the\nsquare topology, and the random topology respectively. Our\napproach is quite easy to implement, but it can effectively\nimprove the search capability and prevent the algorithm from\nbeing trapped in local optima. We demonstrate the effectiveness\nof our approach on a set of well-known benchmark\nproblems.We also introduce the local topologies to a hybrid\nDE/BBO method, resulting in three localized DE/BBO algorithms,\nand show that our approach can improve the performance\nof the state-of-the-art algorithm as well.
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