基于混合二次对立学习的生物地理优化算法  

Improved biogeography-based optimization algorithm using hybrid quasi-oppositional learning

作　　者：王磊[1] 贾砚池 

机构地区：[1]西南财经大学经济信息工程学院,成都610074 [2]西南财经大学天府学院,四川绵阳621000

出　　处：《计算机应用》2014年第11期3245-3249,共5页journal of Computer Applications

基　　金：中央高校基本科研业务费专项资金资助项目(JBK130924);四川省教育厅科学研究项目(14ZB0046)

摘　　要：针对生物地理优化(BBO)算法探索能力不强、收敛速度慢的缺点,提出一种基于混合二次对立学习的生物地理优化算法——HQBBO。首先,定义一种启发式的混合二次对立点,并从理论上证明其搜索效率优势;然后,提出混合二次对立学习算子,增强算法的全局探索能力,提高收敛速度;此外,还采用搜索域动态缩放策略和精英保留策略进一步提高寻优效率。对8个基准测试函数的仿真实验结果表明,所提算法在寻优精度和收敛速度上优于基本BBO算法和对立BBO算法(OBBO),表明其采用的混合二次对立学习算法对于其高收敛速度和全局探索能力是非常有效的。To deal with the problems of poor exploration capability and slow convergence speed in Biogeography-Based Optimization（ BBO） algorithm, a hybrid quasi-oppositional learning based BBO algorithm named HQBBO was proposed.Firstly, the definition of heuristic hybrid quasi-oppositional point was given and its advantage in searching efficiency was proven theoretically. Then, the hybrid quasi-oppositional learning operator was brought forward to enhance the exploration capability and accelerate convergence speed. Meanwhile, the dynamic scaling strategy of searching domain and the elitism preservation strategy were utilized to boost optimization efficiency further. Simulation results on eight benchmark functions illustrate that the proposed algorithm outperforms the basic BBO algorithm and the oppositional BBO（ OBBO） algorithm in terms of convergence accuracy and speed, which verifies the effectivity of hybird quasi-oppositional learning operator for improving the convergence speed and global exploring ability.

关 键 词：生物地理学优化算法 混合二次对立学习 搜索域动态缩放 精英保留策略 探索能力 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]