Abstract:

The biogeography-based optimization (BBO) is a new swarm intelligence algorithm. To improve the global searching ability, solve the prematurity of BBO, a heuristic mutation operator is designed, which based on the differential information among the population individuals. It makes up the lack of the heuristic information on Gauss, Cauchy mutation operators. And the nonlinear migration model was introduced to the BBO considering to the natural environment. Tests are carried out through four standard test functions on the standard BBO, GMBBO, CMBBO and HMBBO independently, the results shows that HMBBO has a preferable convergence rate and search accuracy. The application of gasoline blending scheduling shows that HMBBO is effective.

Key words: algorithm, optimization, simulation, gasoline blending scheduling

摘要：

汽油调合和调度优化问题中含有典型的非线性约束(NLP)问题。针对一般智能优化算法在解决此类优化问题中易陷于局部极值,提出了一种改进的生物地理学优化算法(HMBBO)。该算法设计了一种基于种群个体差异信息的启发式变异算子,弥补了Gauss变异、Cauchy变异算子缺乏启发式信息的不足,以解决原算法在局部搜索时易出现的早熟问题,提高算法的全局搜索能力,并且采用非线性物种迁移模型以适应不同的自然环境。采用4个测试函数进行仿真,结果表明:HMBBO算法与标准BBO算法、基于Gauss变异及基于Cauchy变异的BBO算法比较,其收敛速度和全局寻优能力有明显改善。汽油调合和调度优化实例表明,该算法能够快速有效地找到全局最优解。

关键词: 算法, 优化, 仿真, 汽油调合和调度优化