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Nearest neighbour cuckoo search algorithm with probabilistic mutation
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DOI：10.1016/j.asoc.2016.08.021.png)
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Abstract
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In this study, we present a nearest neighbour cuckoo search algorithm with probabilistic mutation, called NNCS. In the proposed approach, the nearest neighbour strategy is utilized to select guides to search for new solutions by using the nearest neighbour solutions instead of the best solution obtained so far. In the proposed strategy, we respectively employ a solution-based and a fitness-based similar metrics to select the nearest neighbour solutions for implementation. Furthermore, the probabilistic mutation strategy is used to control the new solutions learn from the nearest neighbour ones in partial dimensions only. In addition, the nearest neighbour strategy helps the best solution participate in searching too. Extensive experiments, which are carried on 20 benchmark functions with different properties, demonstrate the improvement in effectiveness and efficiency of the nearest neighbour strategy and the probabilistic mutation strategy. (C) 2016 Elsevier B.V. All rights reserved.
Keywords:
Cuckoo search algorithm
Nearest neighbour
Solution-based similar metric
Fitness-based similar metric
Probabilistic mutation
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