摘要
针对人工蜂群算法早熟收敛问题,基于元胞自动机原理和人工蜂群算法,提出一种元胞人工蜂群算法.该算法将元胞演化和人工蜂群搜索相结合,利用元胞及其邻居的演化提高了种群多样性,避免陷入局部最优解.经一系列典型0-1规划问题实例的仿真实验和与其他算法对比,验证了本算法的效果和效率,获得了满意的结果.
To improve the premature convergence problem of artificial bee colony algorithm, a cellular artificial bee colony algorithm（CABA） is proposed. Based on the principle of cellular automata, the evolution rules of cellular and its neighbor are introduced into the algorithm to maintain the bee population＇ s diversity. The algorithm can effectively avoid the local optimal solution. Simulated tests of typical 0 - lproramming problems and comparisons with other algo- rithms show that CABA has fast convergence speed and good global optimization ability. The effectiveness and effi- ciency of CABA is validated.
出处
《数学理论与应用》
2014年第1期83-91,共9页
Mathematical Theory and Applications
基金
国家自然科学基金(70871081)
上海市一流学科建设项目(S1201YLXK)
上海市教育委员会科研创新项目(14YZ090)
高等学校博士学科点专项科研基金联合资助课题(20123120120005)
关键词
人工蜂群算法
0-1规划
元胞自动机
智能优化
Artificial bee colony algorithm 0 - 1 programming Cellular automata Intelligent optimization
