一种混合的离散细菌菌落优化算法

为了利用细菌算法解决组合优化问题, 提出了一种混合的离散细菌菌落优化算法。根据现有细菌优化算法, 设计一种新的个体编码方式及进化模式, 通过设计种群的自适应调整因子增强个体活力, 并融合禁忌搜索算法, 克服算法易于陷入过早收敛的不足, 并与其他算法在Taillard标准调度测试问题集上比较实验, 验证了算法的有效性。仿真结果表明, 该算法可以搜索到问题的最优组合, 能够有效避免算法陷入局部最优, 取得了满意的结果。

Hybrid discrete bacterial colony optimization algorithm

This paper presented a hybrid discrete bacterial colony optimization algorithm to solve the combination optimization problems, and designed novel coding method and evolution mechanism for each individual. According to such coding way, every individual was assigned a discrete value that represented a feasible combination of the problems. So the whole searching behaviors of the colony were executed in the discrete space directly. In order to improve the performance of the individual, each bacterium was given an adaptive adjustment factor that could adaptively reinitialize the position of individuals when all of them gathered together. This algorithm added the taboo searching to enhance the local searching simultaneously. It verified the performance of the algorithm by some Taillard's benchmark problems. Simulation results show that all optimal solutions can be found in experiments and it can effectively avoid the search being trapped into local optimum and achieve satisfactory result.