Novel biogeography-based optimization algorithm with hybrid migration and global-best Gaussian mutation. (English) Zbl 1481.90320
Summary: The Biogeography-Based Optimization algorithm and its variants have been used widely for optimization problems. To get better performance, a novel Biogeography-Based Optimization algorithm with Hybrid migration and global-best Gaussian mutation is proposed in this paper. Firstly, a linearly dynamic random heuristic crossover strategy and an exponentially dynamic random differential mutation one are presented to form a hybrid migration operator, and the former is used to get stronger local search ability and the latter strengthen the global search ability. Secondly, a new global-best Gaussian mutation operator is put forward to balance exploration and exploitation better. Finally, a random opposition learning strategy is merged to avoid getting stuck in local optima. The experiments on the classical benchmark functions and the complexity functions from CEC-2013 and CEC-2017 test sets, and the Wilcoxon, Bonferroni-Holm and Friedman statistical tests are used to evaluate our algorithm. The results show that our algorithm obtains better performance and faster running speed compared with quite a few state-of-the-art competitive algorithms. In addition, experimental results on Minimum Spanning Tree and \(K\)-means clustering optimization show that our algorithm can cope with these two problems better than the comparison algorithms.
MSC:
| 90C59 | Approximation methods and heuristics in mathematical programming |
| 68W50 | Evolutionary algorithms, genetic algorithms (computational aspects) |
Keywords:
evolutionary algorithm; biogeography-based optimization algorithm; migration; Gaussian mutation; minimum spanning tree; \(K\)-menas clusteringReferences:
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