混合果蝇算法及其在组合优化中的应用

为改善基本果蝇算法易陷入局部最优及早熟的缺陷,利用一种改进的果蝇算法来进行优化,利用免疫算法自我-非自我的抗原识别机制及免疫系统学习■记忆■遗忘的知识处理机制提高算法的搜索能力及算法精度。改进算法将在果蝇算法执行后期引入免疫反应,通过产生不同抗体来增强种群多样性,跳出局部最优。通过数值仿真及实际案例的对比结果表明,改进算法的寻优表现更加良好,为算法优化提供一种有效可行的方法和思路。

Mixed fruit fly algorithm and its application in combinatorial optimization

To overcome the defects of local optimum and precocity in basic fruit fly algorithm, an improved drosophila algorithm was presented in this paper for optimization. The basic idea is to improve the searching ability and accuracy of the algorithm by using the self-non-self antigen recognition mechanism of immune algorithm and the knowledge processing mechanism of learning-memory-forgetting in immune system. The immune response is introduced at the later stage of fruit fly algorithm implementation and the population diversity is enhanced by producing different antibodies to jump out of the local optimum. The results of numerical simulation and practical cases show that the improved algorithm performs better, and it provides an effective and feasible method and idea for algorithm optimization.