Efficient Management and Application of Human Resources Based on Genetic Ant Colony Algorithm

Abstract

With the increasing demand of human resources, the cost of staffing and management is increasing, and it is difficult to dynamically allocate and adjust personnel among different parts. It is the key of intelligent management technology to realize efficient application and mining in human resource management. In the aspect of human resource allocation and management, this paper puts forward the efficient management and application of human resource based on the genetic ant colony algorithm. Firstly, this paper describes the process management and parameter application of the genetic algorithm and ant colony algorithm and manages the resource allocation and management process under the two algorithms. Secondly, several current test functions are applied to the genetic algorithm and ant colony algorithm to test the efficiency of the algorithm, which has obvious advantages in convergence efficiency. Finally, the paper uses the efficient management configuration of human resources for comprehensive application and management and applies the system uploading and downloading services on human resumes, respectively. The genetic ant colony algorithm has obvious advantages in efficiency. In human resource data matching, the genetic algorithm is slightly better than the ant colony algorithm in the case of relatively few data in the early stage, and the accuracy of the ant colony algorithm is slightly better than the genetic algorithm in the later stage. The ACO-GA algorithm is more consistent with the actual value, which not only ensures the stability but also ensures the accuracy of prediction, which is more in line with the actual needs.

Load Balancing Based on Firefly and Ant Colony Optimization Algorithms for Parallel Computing

“…The firefly load-balancing algorithm was proposed by Manisha et al It reduces the computational cycles and the degree of load imbalance while exhibiting better working performance [ 22 ]. Both the genetic ant colony algorithm proposed by Cheng Cheng et al [ 23 ] and the ACO focusing algorithm proposed by Skinderowicz Rafał [ 24 ] improved the ant colony algorithm to a new level and obtained better computational performance. Tang Bo et al proposed the idea of applying genetic algorithms to the mapping process of grid blocks and processors and then performing intelligent allocation [ 2 ], but genetic algorithms have more space for optimization than other algorithms.…”