Heuristic Crossover Based on Biogeography-based Optimization

摘要

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.