摘要
针对AEA算法的收敛稳定度不高、易早熟等问题,给出一种融合人工蜂群(ABC)算法的改进AEA算法SAEA算法。在达到一定的优化程度时,根据人工蜂群算法的思想,按概率选择个体,并对所选择个体进行有限次的优化更新。通过与ABC和AEA算法对22个标准测试函数试验结果进行比较,以及对超临界水氧化动态参数的估计表明,提出的混合算法具有良好的收敛性以及全局优化性能。该算法既保证了在寻优过程中的收敛性,确保种群向着目标方向进化,也增加了种群的多样性,避免过早收敛。
Considering the rate of successful minimization and accuracy, we provide a new improved Alopex-based Evolutionary Algo- rithm （AEA） based on artificial bee colony algorithm （ABC） , called SAEA. In the local research, we choose the member according to its function value and probability, then update it in limit numbers. Compared with ABC and AEA, the experimental results on 22 test functions indicate that the proposed hybrid algorithm has good convergence and global optimization performance. Furthermore, satisfac- tory results are obtained, applying it to estimate reaction kinetic parameters for Supercritical water oxidation （SCWO）. In a word, SAEA increases the population diversity and avoids premature convergence.
出处
《控制工程》
CSCD
北大核心
2014年第6期858-862,866,共6页
Control Engineering of China
基金
国家自然科学基金项目(21176072)
