Solving economic emission load dispatch problems using hybrid differential evolution
Applied Soft Computing
Analytical and numerical comparisons of biogeography-based optimization and genetic algorithms
Information Sciences: an International Journal
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part I
Adaptive population tuning scheme for differential evolution
Information Sciences: an International Journal
Global optimization using a multipoint type quasi-chaotic optimization method
Applied Soft Computing
Accelerated biogeography-based optimization with neighborhood search for optimization
Applied Soft Computing
Design of wide-beam antenna using dynamic multi-objective BBO/DE
International Journal of Computer Applications in Technology
Adaptive Memetic Differential Evolution with Global and Local neighborhood-based mutation operators
Information Sciences: an International Journal
Free Pattern Search for global optimization
Applied Soft Computing
An analysis of the migration rates for biogeography-based optimization
Information Sciences: an International Journal
Emergency railway wagon scheduling by hybrid biogeography-based optimization
Computers and Operations Research
Novel approaches for classification based on Cuckoo Search Strategy
International Journal of Hybrid Intelligent Systems
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Differential evolution (DE) is a fast and robust evolutionary algorithm for global optimization. It has been widely used in many areas. Biogeography-based optimization (BBO) is a new biogeography inspired algorithm. It mainly uses the biogeography-based migration operator to share the information among solutions. In this paper, we propose a hybrid DE with BBO, namely DE/BBO, for the global numerical optimization problem. DE/BBO combines the exploration of DE with the exploitation of BBO effectively, and hence it can generate the promising candidate solutions. To verify the performance of our proposed DE/BBO, 23 benchmark functions with a wide range of dimensions and diverse complexities are employed. Experimental results indicate that our approach is effective and efficient. Compared with other state-of-the-art DE approaches, DE/BBO performs better, or at least comparably, in terms of the quality of the final solutions and the convergence rate. In addition, the influence of the population size, dimensionality, different mutation schemes, and the self-adaptive control parameters of DE are also studied.