iDEbbo

This work releases the source code of iDEbbo (iDE/BBO: An Improved Biogeography-Based Optimization Algorithm with Differential Evolution).

We propose an iDE/BBO algorithm referring to the components from CPBBOCO algorithm [1] and the differential evolution (DE) theory.

To further improve the algorithm exploration ability and mitigate some weaknesses, our algorithm designs a hybrid migration method and a scalable direction mutation operation.

Empirical results demonstrate that our iDE/BBO can achieve the better algorithm performance by comparing with two BBO variants and three additional evolutionary algorithms.

To evaluate the performance of the proposed algorithm, 23 benchmarks are employed from F01 to F23. A more detailed description of these functions can be found in [2,3].

We mainly introduce one type of file and folder structure as follows:

Note that the detailed description of the source code is available after the manuscript is accepted.

Attention please:

Note that the detailed information is available after the manuscript is accepted.

[1] Shi K, Yu H, Fan G, Luo F. iCPBBOCO: A Combination Evaluation Algorithm Based on the Extensional BBO. In: Proceedings of International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), Chengdu, China; 2016. p. 717–723.

[2] Yao X, Liu Y, Lin G. Evolutionary programming made faster. IEEE Trans Evolutionary Computation. 1999;3(2):82–102. doi:10.1109/4235.771163.

[3] Liang JJ, Runarsson TP, Mezura-Montes E, Clerc M, Suganthan PN, Coello CAC, et al. Problem definitions and evaluation criteria for the CEC 2006 special session on constrained real-parameter optimization. International Journal of Computer Assisted Radiology and Surgery. 2006;41(8).