【摘要】 针对细菌觅食优化算法求解高维优化问题时不易跳出局部最优解的问题,引入趋向方向余弦向量和随时间变化的加速系数,控制细菌觅食优化算法的收敛精度和收敛速度,并将改进算法用于求解组合优化问题。依据细菌种群密度计算原则,设计了一种离散空间和连续空间之间相互转换的规则,同时用集合对细菌觅食优化算法中的算术运算符形式化描述。仿真试验结果表明:基于集合的细菌群优化算法避免了早熟现象,寻优结果优于蚁群算法且接近基于集合的粒子群算法。更多还原

【Abstract】 The conventional Bacterial Foraging Optimizer(BFO) is easy to get stuck in local minima in solving high dimensional optimization problems.This paper proposes an adaptive Bacterial Swarm Optimizer(BSO) with time-varying acceleration coefficients to solve the local-optimal problems of BFO.The proposed method,termed as ABSO-TVAC,is applied to optimize Combinatorial Optimization Problems(COPs).When dealing with COPs,according to the cell-to-cell signaling in E.coli swarm,a conversion rule between continuous space and discrete one is designed.All arithmetic operators in the velocity and position updating rules used in the ABSO-TVAC are described in a manner of set.Simulation shows that the proposed algorithm has the capability to avoid the premature problem,and it is superior to Ant Colony Optimization(ACO) and comparable to set-based particle swarm optimization.更多还原