Heuristic Crossover Based on Biogeography-based Optimization

DOI

10.2991/emim-17.2017.69How to use a DOI?

Keywords

Biogeography-based optimization; Optimization; Gaussian mutation operator; Hybrid mutation

Abstract

Biogeography based optimization (BBO) is a new evolutionary optimization algorithm based on the science of biogeography for global optimization. In this paper, we proposed two extensions to BBO. First, we proposed a new migration operation based sinusoidal migration model with the heuristic crossover operator. We have presented three heuristic crossover operators, they are constant heuristic crossover operator, random heuristic crossover operator and dynamic heuristic crossover operator. Among them, the migration operation used random heuristic crossover operator (HCBBO) is optimal. Then, as we all know, the Gaussian mutation operator is optimal to settle unimodal function, the random mutation operator is optimal to settle multimodal function. Therefore, we have presented a stable mixture mutation approach based on an improved variant of BBO, it is a biogeography of hybrid with random mutation and Gauss mutation based optimization algorithm using sinusoidal migration model. Experiments have been conducted on 14 benchmark problems of a wide range of dimensions and diverse complexities. Simulation results and comparisons demonstrate the proposed HCBBO algorithm using sinusoidal migration model surpasses other improved BBO, the mixture BBO is stability than other algorithms from literatures in recent years when considering the quality of the solutions obtained.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Mengqing Feng
PY  - 2017/04
DA  - 2017/04
TI  - Heuristic Crossover Based on Biogeography-based Optimization
BT  - Proceedings of the 7th International Conference on Education, Management, Information and Mechanical Engineering (EMIM 2017)
PB  - Atlantis Press
SP  - 336
EP  - 341
SN  - 2352-538X
UR  - https://doi.org/10.2991/emim-17.2017.69
DO  - 10.2991/emim-17.2017.69
ID  - Feng2017/04
ER  -