仿生算法及其在专家分配问题中的应用
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摘要
遗传算法和蚁群优化算法是两种最流行的仿生算法,前者以自然选择和遗传变异理论为基础,后者则是对蚂蚁觅食行为进行模拟而提出的一种仿生算法。本文对这两种算法进行了深入的研究,针对它们收敛速度慢、多样性差、容易早熟等不足,提出了几种改进的方法。此外,本文还将这两种算法成功应用到一个新的领域——专家分配问题。取得的成果和创新点如下:
提出了两种改进多峰值搜索能力的遗传算法。一种是改进局部搜索能力的小生境遗传算法。该算法在进化后期进行小生境境内的交叉与变异操作来取代其在整个解空间内的交叉变异,进行有针对性的局部搜索。它具有更高的求解精度,更快的收敛速度,是一种寻优能力、效率和可靠性更高的优化算法。另一种方法将小生境遗传算法和Hopfield神经网络有机的结合在一起,首先进行小生境遗传算法寻优,然后对所得具有全局多样性的解进行聚类分析,得到的聚类中心作为Hopfield网络的初始搜索点,最后利用Hopfield网络逐个寻优。该方法综合了Hopfield神经网络准确、快速和小生境遗传算法多样性的优点。
提出了一种蚁群优化算法和遗传算法的混合算法。该算法将遗传操作引入到了蚁群优化算法的每一次迭代后,利用遗传算法全局快速收敛的优点,来加快蚁群系统的收敛速度。并且通过遗传算法的变异机制,增强了蚁群系统跳出局部最优的能力。
提出了一种具有先验知识的蚁群优化算法。新算法将问题特征作为先验知识事先提取出来,并赋予蚁群优化算法中的精英蚂蚁以识别该固有特征的能力,以提高精英蚂蚁的搜索质量,进而使得新算法整体的求解能力得以提高。
在随着项目数量的迅速增长与研究范围的不断扩大,传统的分配方法和手工操作已经不能满足基金管理工作需要的前提下,本课题研究了专家分配问题,并结合专家分配问题的特点,设计了信息素指导下的遗传算子,使用遗传算法对其进行了求解。并进一步提出了蚁群优化算法求解专家分配问题的方法,实验取得了较好的效果。
Genetic algorithm (GA) and ant colony optimization (ACO) are two best popular bionical algorithms. GA is based on natural selection and evolution theory, and ACO simulates ants' behaviour of looking for food. This paper did much research on these two algorithms and proposed several new algorithms to improve their performance. Moreover this paper introduced them to a new discrete optimization area: expert assignment problem. The main works and innovative points are as follows:
Two improved GAs for multimodal optimization were proposed. One is a novel niche genetic algorithm (NGA) with local search ability. The new algorithm adopted the mechanism of crossover and mutation in niche population instead of the whole population during late iterations. The results used in Shubert function showed its superiority. The other is a hybrid algorithm of HGA and Hopfield Neural Network (HNN). A group of solutions with variety were obtained using HGA firstly, and then the solutions were partitioned into some clusters whose centroids were as the initial value of each HNN, and HNNs were run to obtain all minima. It made use of the advantages of both HGA and HNN, and appeared excellent characteristic in optimal problems of multimodal function.
A hybrid algorithm of GA and ACO were proposed. It added GA to ACO’every generation. Making use of GA’s advantage of whole quick convergence, ACO’convergence speed was quickened. And GA’s mutation mechanism improved the ability of ACO to avoid being premature.
A new intelligent ACO for traveling salesman problem (TSP) was proposed. The new algorithm extracted the intrinsic characteristic rule of TSP and then injected it into the elite of ants, which improved the elite ant’s capability to build a better solution and then made an improvement of ACO.
Combining with the property of the expert assignment problem, this paper designed the improved genetic operators and ACO operations, and proposed methods of solving expert assignment problem using GA, ACO and hybrid algorithms of GA and ACO.
提出了两种改进多峰值搜索能力的遗传算法。一种是改进局部搜索能力的小生境遗传算法。该算法在进化后期进行小生境境内的交叉与变异操作来取代其在整个解空间内的交叉变异,进行有针对性的局部搜索。它具有更高的求解精度,更快的收敛速度,是一种寻优能力、效率和可靠性更高的优化算法。另一种方法将小生境遗传算法和Hopfield神经网络有机的结合在一起,首先进行小生境遗传算法寻优,然后对所得具有全局多样性的解进行聚类分析,得到的聚类中心作为Hopfield网络的初始搜索点,最后利用Hopfield网络逐个寻优。该方法综合了Hopfield神经网络准确、快速和小生境遗传算法多样性的优点。
提出了一种蚁群优化算法和遗传算法的混合算法。该算法将遗传操作引入到了蚁群优化算法的每一次迭代后,利用遗传算法全局快速收敛的优点,来加快蚁群系统的收敛速度。并且通过遗传算法的变异机制,增强了蚁群系统跳出局部最优的能力。
提出了一种具有先验知识的蚁群优化算法。新算法将问题特征作为先验知识事先提取出来,并赋予蚁群优化算法中的精英蚂蚁以识别该固有特征的能力,以提高精英蚂蚁的搜索质量,进而使得新算法整体的求解能力得以提高。
在随着项目数量的迅速增长与研究范围的不断扩大,传统的分配方法和手工操作已经不能满足基金管理工作需要的前提下,本课题研究了专家分配问题,并结合专家分配问题的特点,设计了信息素指导下的遗传算子,使用遗传算法对其进行了求解。并进一步提出了蚁群优化算法求解专家分配问题的方法,实验取得了较好的效果。
Genetic algorithm (GA) and ant colony optimization (ACO) are two best popular bionical algorithms. GA is based on natural selection and evolution theory, and ACO simulates ants' behaviour of looking for food. This paper did much research on these two algorithms and proposed several new algorithms to improve their performance. Moreover this paper introduced them to a new discrete optimization area: expert assignment problem. The main works and innovative points are as follows:
Two improved GAs for multimodal optimization were proposed. One is a novel niche genetic algorithm (NGA) with local search ability. The new algorithm adopted the mechanism of crossover and mutation in niche population instead of the whole population during late iterations. The results used in Shubert function showed its superiority. The other is a hybrid algorithm of HGA and Hopfield Neural Network (HNN). A group of solutions with variety were obtained using HGA firstly, and then the solutions were partitioned into some clusters whose centroids were as the initial value of each HNN, and HNNs were run to obtain all minima. It made use of the advantages of both HGA and HNN, and appeared excellent characteristic in optimal problems of multimodal function.
A hybrid algorithm of GA and ACO were proposed. It added GA to ACO’every generation. Making use of GA’s advantage of whole quick convergence, ACO’convergence speed was quickened. And GA’s mutation mechanism improved the ability of ACO to avoid being premature.
A new intelligent ACO for traveling salesman problem (TSP) was proposed. The new algorithm extracted the intrinsic characteristic rule of TSP and then injected it into the elite of ants, which improved the elite ant’s capability to build a better solution and then made an improvement of ACO.
Combining with the property of the expert assignment problem, this paper designed the improved genetic operators and ACO operations, and proposed methods of solving expert assignment problem using GA, ACO and hybrid algorithms of GA and ACO.
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