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A chaotic local search based bacterial foraging algorithm and its application to a permutation flow-shop scheduling problem

机译：基于混沌局部搜索的细菌觅食算法及其在置换流水车间调度问题中的应用

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摘要

The permutation flow-shop scheduling problem (PFSP) is a typical combinational and non-deterministic polynomial-hard problem, with discrete solution space. In this paper, a novel bacterial foraging optimisation algorithm (BFO) has been proposed to solve the PFSP. Difficulties such as slow convergent speeds and entrapment in the local optimum were incurred by the original BFO algorithm in solving a high-dimensional combinatorial optimisation problem. In order to deal with these difficulties, a differential evolution operator and a chaotic search operator were each introduced into the original BFO algorithm to enhance the activity levels of the individual bacterium and to extend the local searching space. Theoretical analysis showed that the improved algorithm obtained more motility in chemotaxis and could converge to the global optimum with a probability of 1. Simulation results and comparisons to both continuous and combinatorial benchmark problems were used to demonstrate the effectiveness of this novel optimisation algorithm.
机译：置换流水车间调度问题（PFSP）是典型的组合式和非确定性多项式-硬问题，具有离散的求解空间。本文提出了一种新颖的细菌觅食优化算法（BFO）来求解PFSP。原始的BFO算法解决了高维组合优化问题时，出现了收敛速度慢和陷入局部最优等难题。为了解决这些困难，将差分进化算子和混沌搜索算子分别引入原始的BFO算法中，以增强单个细菌的活性水平并扩展局部搜索空间。理论分析表明，改进算法在趋化性上具有更大的运动性，并且可以收敛到全局最优性，概率为1。仿真结果以及对连续和组合基准问题的比较均证明了该新型优化算法的有效性。

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