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

Authors: Cheng Cheng; Yuguang Xu; Graciela Daniels
Journal: Journal of Sensors
 (2022)

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## 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.


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## Body

## 1. Introduction

The country and even the whole world are full of confidence in the future development of intelligent computing and vigorously recommend the integration of intelligent computing into people’s lives. At the same time, they hope that intelligent computing can solve the problem of uneven distribution and mismatch of resources. The allocation of human resources is also included. When the allocation of human resources is not handled properly, it will lead to a series of problems such as cost increase and brain drain. Therefore, it is necessary to make the process of human resources allocation more digital and technical. Human resources need to have high-efficiency performance at the same time of low cost, which is the premise of rationalization of allocation. This can show respect for talents. Intelligent computing can be said to be its savior in this respect, and they complement each other, which not only solves problems but also makes intelligent computing involved more widely.Traditional intelligent computing has not been broken through in the past. In today’s important fields [1], such as big data computing, traditional intelligent computing needs high manpower and material costs to study. For the emerging intelligent computing, it can make up for the defects of the traditional one. According to the investigation, in the aspect of quantum heuristics, the inclusion of intelligent computing brings advantages to this technology but also brings disadvantages and challenges to the future. It is necessary for researchers to study it further [2]. Because of its high efficiency in solving nonlinear problems, computational intelligence is proposed to solve such problems as power outages, so that power can be quickly resupplied and redundant parts can be unloaded [3]. It contains most algorithms. In order to make these methods more efficient, it is necessary to further study these methods. Different from traditional technologies, intelligent computing has more advantages, but it also has its own limitations, which will lead to limited use. Intelligent computing [4] is also used in power load specialty, and because of its importance, it has attracted the attention of most relevant personnel. Some researchers put forward a method of using model-free technology to test power load based on NN and FL. Through a series of case studies, the advantages and special performance of this method are demonstrated to the public under geographical environment and other conditions. One part of intelligent computing, artificial neural networks, is good at solving the problem of air pollution modeling. It will simulate the ozone layer with pollution parameters. After putting this model into real life, experiments show that this model is very powerful in dealing with such problems [5]. Intelligent computing can also be applied to electrical islanding detection technology [6]. Because the electrical islanding detection technology is not very efficient in solving problems in nonlinear systems, intelligent computing is introduced to improve the accuracy of solving problems, which can provide accurate data and solutions for relevant researchers. Intelligent computing is also used in computer Go [7]. In the world-class computer Go competition held in Taiwan, many Go masters want to compete with intelligent computing MoGo. MoGo, blessed by RAVE and UCT, has four high-performance attributes. From the outcome of the battle, we can see that intelligent computing can be used in computer Go to achieve a high level of Go battle, and its future development is immeasurable. Literature [8] at the same time, intelligent computing is getting higher and higher in solving problems related to biology, chemistry, and medicine, and its influence is getting bigger and bigger. Combining it with statistical methods, a new model is obtained. In the study of human resource allocation in large companies [9], it is found that human factors have the greatest impact. Generally speaking, in order to reduce the mismatch of resource allocation, personnel performance is generally controlled. Human resource allocation is the top priority in any project. In order to improve the allocation of human resources [10], a series of operations such as shortening project time and reducing costs have been taken. However, if the fundamental factors are not solved, these operations will be lost. The influence of human resource allocation on enterprises should not be underestimated [11]. However, although many schemes have been put forward, the problems still exist. Most of them are caused by not paying attention to the team. Therefore, some researchers have studied from various aspects and put forward a predictive model based on multilayer perceptron. Researchers put this model into practical experiments and finally get the conclusion that this model can improve the efficiency of human resource allocation. In the allocation of human resources, the most important criterion for the classification of personnel is personality, but the personality should be objective and complete [12]; otherwise, it is useless. Therefore, researchers propose to use high-performance algorithms to get more realistic data, which can minimize the error. In order to improve the utilization rate of scarce resources, researchers introduce decentralization factors and resource allocation with the help of software evaluation and portfolio planning and propose new allocation methods to solve the problem of improving the resource allocation of nonsmall R&D organizations [13]. In order to develop the human resource management of shelters, researchers propose to match the theory with people to get a model [14]. In this way, more influencing factors can be considered more comprehensively. The model is put into the experiment and finally found that there is a certain relationship between the working hours and the vacation time; so, in the shelter, the human resources and vacation need to be set reasonably. The allocation of human resources has also brought troubles to the country. Therefore, the state also attaches great importance to optimizing its allocation and improving its utilization rate [15]. The intelligent method is quoted in human resources. Most researchers aim at the low utilization rate and unreasonable resource allocation in the process of human resources. In the process of algorithm implementation, there is slow convergence. Therefore, the paper proposed the ACO-GA algorithm to solve the above problems.


## 2. Intelligent Computing Analysis

### 2.1. Basic Concepts

Intelligent computing is a success for people to transform data into logic. At the same time, it can enable human beings to cultivate their ability to think independently. Most algorithms are its members, but it does not go its own way but is based on calculation and continuously improved. The classification is shown in Figure1.Figure 1
Classification diagram of intelligent computing.


### 2.2. Genetic Algorithm

#### 2.2.1. Basic Concepts

Natural selection heredity is the foundation of this algorithm; that is, chromosomes are abstracted into body objects and then encoded. Only in this way can we search randomly and efficiently in this space. Among them, genetic operation is divided into selection, crossover, and variation.After the formation of the first generation population, according to the law of the survival of the strong in the theory of biological evolution, a series of more and more excellent approximate solutions are produced through iteration. Moderate function is a strict judge, and excellent individuals can pass its selection. Then, these excellent people will choose, cross, and mutate. They have become completely different from before, and they are a brand-new population. The genetic algorithm flow is shown in Figure2.Figure 2
Flow chart of the genetic algorithm.The genetic algorithm is divided into patternH. The formula of general mathematical expression is as follows:
(1)MHt+1≥MHtfHtft1−Pc×δHL−11−PmOH,(2)fHt=∑i∈HfiMHt.MHt+1 is the expected number of pattern H in population after t+1 iteration, Pc is the crossing probability, Pm is the mutation probability, ft is the average fitness value, fHt is the average fitness value of population model H, fi is the i-th individual fitness value, and δH is the pattern definition distance, that is, the distance between the initial and last determined positions in pattern H.


#### 2.2.2. Related Issues

In the aspect of human resource allocation, the algorithm is not perfect. If the two are combined, the matching resources needed by the enterprise organization at first can have idealized results. The reason is that the convergence speed of the optimal solution is fast in the early stage of the algorithm. With the continuous operation, in the later stage of the algorithm, the efficiency of human resource allocation plummeted due to the increase of allocation times and the decrease of convergence speed. The two are not suitable and need to be improved.


### 2.3. Ant Colony Algorithm

#### 2.3.1. Basic Concepts

When the number of ants iskk=1,2,⋯,m and the number of cities is i,j=1,2,⋯,n, i and j, the distance between them is d. And set the initialization pheromone concentration τij0=τ0. The flow chart of the ant colony algorithm is shown in Figure 3.Figure 3
Flow chart of the ant colony algorithm.Aftert iterations, the probability Pijkt formula for the k-th ant to successfully move from city i to j is as follows:
(3)Pijkt=τijtα⋅ηijtβ∑s∈Jkiτistα⋅ηistβj∈Jki,0j∉Jki.α is the relative importance of pheromone, β is the relative importance of heuristic factors, τijt is the pheromone concentration of city i and j paths after t iterations, ηijt is the heuristic function, the heuristic degree that ants can smoothly from city i to j in the t iteration, and Jki is a collection of cities that ants can visit next.The heuristic factor is calculated by the following formula:(4)ηij=1dij.The update formula of pheromone concentration in intercity path is as follows:(5)τijt+1=1−ρτijt+Δτij,(6)Δτij=∑k=1nΔτijk.ρ is the volatilization coefficient of pheromone, and the value range is (0, 1), Δτij is the pheromone increment of edges i and j in this iteration, and Δτijk: in this iteration, the amount of pheromones left on edges i and j is related to the k-th ant.ForΔτij, there are three calculation models:The formula for the ant cycle system is as follows:(7)Δτijk=QLk.Lk is the length of the path taken by ant k.The formula for the ant quantity system is as follows:(8)Δτijk=Qdij.The formula for the ant density system is as follows:(9)Δτijk=Q.Q is a normal number, which represents the total number of pheromones produced by ants after this iteration.


#### 2.3.2. Related Issues

The influence of the ant colony algorithm on human resource allocation is just opposite to the new genetic algorithm. In the early stage of the algorithm, due to pheromone initialization, the ant’s optimization ability is reduced. This means that in the allocation of human resources, in the early stage of allocation, human resource information cannot be rationalized, which will lead to reduced efficiency, increased costs, and brain drain. With many iterations, the pheromone concentration increases, which greatly increases the ant’s optimization ability. This will greatly increase the matching efficiency in human resource allocation.


## 2.1. Basic Concepts

Intelligent computing is a success for people to transform data into logic. At the same time, it can enable human beings to cultivate their ability to think independently. Most algorithms are its members, but it does not go its own way but is based on calculation and continuously improved. The classification is shown in Figure1.Figure 1
Classification diagram of intelligent computing.


## 2.2. Genetic Algorithm

### 2.2.1. Basic Concepts

Natural selection heredity is the foundation of this algorithm; that is, chromosomes are abstracted into body objects and then encoded. Only in this way can we search randomly and efficiently in this space. Among them, genetic operation is divided into selection, crossover, and variation.After the formation of the first generation population, according to the law of the survival of the strong in the theory of biological evolution, a series of more and more excellent approximate solutions are produced through iteration. Moderate function is a strict judge, and excellent individuals can pass its selection. Then, these excellent people will choose, cross, and mutate. They have become completely different from before, and they are a brand-new population. The genetic algorithm flow is shown in Figure2.Figure 2
Flow chart of the genetic algorithm.The genetic algorithm is divided into patternH. The formula of general mathematical expression is as follows:
(1)MHt+1≥MHtfHtft1−Pc×δHL−11−PmOH,(2)fHt=∑i∈HfiMHt.MHt+1 is the expected number of pattern H in population after t+1 iteration, Pc is the crossing probability, Pm is the mutation probability, ft is the average fitness value, fHt is the average fitness value of population model H, fi is the i-th individual fitness value, and δH is the pattern definition distance, that is, the distance between the initial and last determined positions in pattern H.


### 2.2.2. Related Issues

In the aspect of human resource allocation, the algorithm is not perfect. If the two are combined, the matching resources needed by the enterprise organization at first can have idealized results. The reason is that the convergence speed of the optimal solution is fast in the early stage of the algorithm. With the continuous operation, in the later stage of the algorithm, the efficiency of human resource allocation plummeted due to the increase of allocation times and the decrease of convergence speed. The two are not suitable and need to be improved.


## 2.2.1. Basic Concepts

Natural selection heredity is the foundation of this algorithm; that is, chromosomes are abstracted into body objects and then encoded. Only in this way can we search randomly and efficiently in this space. Among them, genetic operation is divided into selection, crossover, and variation.After the formation of the first generation population, according to the law of the survival of the strong in the theory of biological evolution, a series of more and more excellent approximate solutions are produced through iteration. Moderate function is a strict judge, and excellent individuals can pass its selection. Then, these excellent people will choose, cross, and mutate. They have become completely different from before, and they are a brand-new population. The genetic algorithm flow is shown in Figure2.Figure 2
Flow chart of the genetic algorithm.The genetic algorithm is divided into patternH. The formula of general mathematical expression is as follows:
(1)MHt+1≥MHtfHtft1−Pc×δHL−11−PmOH,(2)fHt=∑i∈HfiMHt.MHt+1 is the expected number of pattern H in population after t+1 iteration, Pc is the crossing probability, Pm is the mutation probability, ft is the average fitness value, fHt is the average fitness value of population model H, fi is the i-th individual fitness value, and δH is the pattern definition distance, that is, the distance between the initial and last determined positions in pattern H.


## 2.2.2. Related Issues

In the aspect of human resource allocation, the algorithm is not perfect. If the two are combined, the matching resources needed by the enterprise organization at first can have idealized results. The reason is that the convergence speed of the optimal solution is fast in the early stage of the algorithm. With the continuous operation, in the later stage of the algorithm, the efficiency of human resource allocation plummeted due to the increase of allocation times and the decrease of convergence speed. The two are not suitable and need to be improved.


## 2.3. Ant Colony Algorithm

### 2.3.1. Basic Concepts

When the number of ants iskk=1,2,⋯,m and the number of cities is i,j=1,2,⋯,n, i and j, the distance between them is d. And set the initialization pheromone concentration τij0=τ0. The flow chart of the ant colony algorithm is shown in Figure 3.Figure 3
Flow chart of the ant colony algorithm.Aftert iterations, the probability Pijkt formula for the k-th ant to successfully move from city i to j is as follows:
(3)Pijkt=τijtα⋅ηijtβ∑s∈Jkiτistα⋅ηistβj∈Jki,0j∉Jki.α is the relative importance of pheromone, β is the relative importance of heuristic factors, τijt is the pheromone concentration of city i and j paths after t iterations, ηijt is the heuristic function, the heuristic degree that ants can smoothly from city i to j in the t iteration, and Jki is a collection of cities that ants can visit next.The heuristic factor is calculated by the following formula:(4)ηij=1dij.The update formula of pheromone concentration in intercity path is as follows:(5)τijt+1=1−ρτijt+Δτij,(6)Δτij=∑k=1nΔτijk.ρ is the volatilization coefficient of pheromone, and the value range is (0, 1), Δτij is the pheromone increment of edges i and j in this iteration, and Δτijk: in this iteration, the amount of pheromones left on edges i and j is related to the k-th ant.ForΔτij, there are three calculation models:The formula for the ant cycle system is as follows:(7)Δτijk=QLk.Lk is the length of the path taken by ant k.The formula for the ant quantity system is as follows:(8)Δτijk=Qdij.The formula for the ant density system is as follows:(9)Δτijk=Q.Q is a normal number, which represents the total number of pheromones produced by ants after this iteration.


### 2.3.2. Related Issues

The influence of the ant colony algorithm on human resource allocation is just opposite to the new genetic algorithm. In the early stage of the algorithm, due to pheromone initialization, the ant’s optimization ability is reduced. This means that in the allocation of human resources, in the early stage of allocation, human resource information cannot be rationalized, which will lead to reduced efficiency, increased costs, and brain drain. With many iterations, the pheromone concentration increases, which greatly increases the ant’s optimization ability. This will greatly increase the matching efficiency in human resource allocation.


## 2.3.1. Basic Concepts

When the number of ants iskk=1,2,⋯,m and the number of cities is i,j=1,2,⋯,n, i and j, the distance between them is d. And set the initialization pheromone concentration τij0=τ0. The flow chart of the ant colony algorithm is shown in Figure 3.Figure 3
Flow chart of the ant colony algorithm.Aftert iterations, the probability Pijkt formula for the k-th ant to successfully move from city i to j is as follows:
(3)Pijkt=τijtα⋅ηijtβ∑s∈Jkiτistα⋅ηistβj∈Jki,0j∉Jki.α is the relative importance of pheromone, β is the relative importance of heuristic factors, τijt is the pheromone concentration of city i and j paths after t iterations, ηijt is the heuristic function, the heuristic degree that ants can smoothly from city i to j in the t iteration, and Jki is a collection of cities that ants can visit next.The heuristic factor is calculated by the following formula:(4)ηij=1dij.The update formula of pheromone concentration in intercity path is as follows:(5)τijt+1=1−ρτijt+Δτij,(6)Δτij=∑k=1nΔτijk.ρ is the volatilization coefficient of pheromone, and the value range is (0, 1), Δτij is the pheromone increment of edges i and j in this iteration, and Δτijk: in this iteration, the amount of pheromones left on edges i and j is related to the k-th ant.ForΔτij, there are three calculation models:The formula for the ant cycle system is as follows:(7)Δτijk=QLk.Lk is the length of the path taken by ant k.The formula for the ant quantity system is as follows:(8)Δτijk=Qdij.The formula for the ant density system is as follows:(9)Δτijk=Q.Q is a normal number, which represents the total number of pheromones produced by ants after this iteration.


## 2.3.2. Related Issues

The influence of the ant colony algorithm on human resource allocation is just opposite to the new genetic algorithm. In the early stage of the algorithm, due to pheromone initialization, the ant’s optimization ability is reduced. This means that in the allocation of human resources, in the early stage of allocation, human resource information cannot be rationalized, which will lead to reduced efficiency, increased costs, and brain drain. With many iterations, the pheromone concentration increases, which greatly increases the ant’s optimization ability. This will greatly increase the matching efficiency in human resource allocation.


## 3. Optimization Algorithm

The time trend of the overall evolution rate of the two algorithms is shown in Figure4.Figure 4
Time situation diagram of overall evolution rate.As can be seen from the figure, the disadvantages of the two are just the opposite. Therefore, this paper proposes to improve the two first and then combines them to complement each other. In this way, a new algorithm, namely, genetic ant colony algorithm, can be obtained. The algorithm can absorb the advantages of the former two, and ensure that the convergence speed does not decrease with iteration, and is more stable. The purpose of this algorithm for human resource allocation is to keep the efficiency of human resource allocation efficient and stable.


### 3.1. Improved Genetic Algorithm

For resource scheduling, assumingpopulationsize=popsize, subtasks=n, and schedulableresources=m, then the total length of parameters = n, then the range of each individual is [1, m]. The formula in the form of individual X is as follows:
(10)X=x1,x2,⋯,xnxt∈R,i=1,2,⋯n.In order to compare the length of time required for information tasks in human resource allocation, the formula is as follows:(11)totaltimeI=maxj=1m∑i=1nTij,(12)Tij=Ttlengthvmjmips+Ttinputfilesizevmjbw+Ttoutputfilesizevmjbw+Ttwait_time,(13)vmjmips=pe_numj×pe_mips.vmjmips is the CPU computing power, pe_numj is the number of virtual machine processors, pe_mips is the virtual machine vmj processor speed, vmjbw is the wideband processing capability of virtual machine vmj, and Ttwait_time is the waiting time of subtask i.In human resources, in order to get suitable resource information individuals, the formula of their fitness function is as follows:(14)f1j=1totaltimeI×uLB,(15)uLB=∑j=1m∑i=1nTijm×totaltimeI.uLB is the balance the load factor, which is proportional to the utilization of resources.
(16)f2j=1∑j=1mtaski,j−n/m2/m1<i<popsize,(17)fj=ωf1j+1−ωf2j0<ω<1,in this whichtaski,j is the task to which the virtual machine j of the individual i is assigned.The formula of selection probability is as follows:(18)Pi=fi∑j=1mfj.The formula of crossover probability is as follows:(19)Pc=Pc1−Pc1−Pc2f′−favefmax−favef′≥fave,Pc1f′<fave,Pc1=0.9,Pc2=0.6.fmax is the maximum fitness value in the current population, fave is the average value of fitness in the current population, and f′, compared with two cross individuals, is the maximum fitness value.The mutation probability formula is as follows:(20)Pm=Pm1−Pm1−Pm2f′−favefmax−favef′≥fave,Pm1f′<fave,Pm1=0.1,Pm2=0.001.


### 3.2. Improved Ant Colony Algorithm

In conventional algorithms, the randomness of ants is not high when they move, which will lead to problems when ants search for similar optimal resource information. In order to solve this problem, new rules have been formulated. The formula is as follows:(21)j=argmaxτijtα⋅ηijtβρ≤ρ0,Pρ>ρ0.ρ is the uniformly distributed random values with a value range of [0, 1], and ρ0 is the constant value.The formula of HR multiservice quality requirement function related to pheromone update rule is as follows:(22)FI=φ1totaltimeij−totaltimemintotaltimemax−totaltimemin+φ2costij−costmincostmax−costmin.totaltimeij is the expected task execution time, totaltimemax is the maximum expected task execution time, totaltimemin is the minimum expected task execution time, costij is the expected task execution cost, costmax is the Maximum cost of expected task execution, costmin is the minimum cost of expected task execution, and φ1,φ2 are the time weight coefficient and cost weight coefficient.Time weight coefficientφ1 and cost weight coefficient φ2 are in [0, 1], andφ1+φ2=1.When calculating the local pheromone increment, the ant updates the resource every time it completes the resource allocation. The formula is as follows:(23)Δτijt=const1FI.When calculating the global pheromone increment, each ant completes the resource allocation once before updating. The formula is as follows:(24)Δτijt=const2FIbest.Andτmin≤τijt≤τmax is specified, so as to avoid the stagnation of the algorithm caused by the disparity of path pheromones.In the ant colony algorithm, the distance between two cities is the same as heuristic information. However, this cannot guarantee the optimal solution; so, we need to refer to the load model to improve the expression of heuristic information. The heuristic factor function is expressed as follows:(25)ηij=1∑i=1taskTilength+Tilength/vmjmips.Tilength is the length of task i, and task is the number of tasks that have been run on virtual machine j.


### 3.3. Genetic Ant Colony Algorithm

Whengnow<gmax, compare gratio,gpop, and when the comparison result is gpop<gradio and occurs many times, the algorithm begins to transform.gnow is the current number of iterations, gmax is the maximum number of iterationsgmin is the minimum number of iterations, gratio is the minimum evolution rate, and gpop is the progeny evolution rate.When the optimal solution of ant colony algorithm is =τG, the pheromone value of the ant colony algorithm is τS=τmin+τG at the initial stage. The algorithm flow is shown in Figure 5.Figure 5
Flow chart of the genetic ant colony algorithm.


## 3.1. Improved Genetic Algorithm

For resource scheduling, assumingpopulationsize=popsize, subtasks=n, and schedulableresources=m, then the total length of parameters = n, then the range of each individual is [1, m]. The formula in the form of individual X is as follows:
(10)X=x1,x2,⋯,xnxt∈R,i=1,2,⋯n.In order to compare the length of time required for information tasks in human resource allocation, the formula is as follows:(11)totaltimeI=maxj=1m∑i=1nTij,(12)Tij=Ttlengthvmjmips+Ttinputfilesizevmjbw+Ttoutputfilesizevmjbw+Ttwait_time,(13)vmjmips=pe_numj×pe_mips.vmjmips is the CPU computing power, pe_numj is the number of virtual machine processors, pe_mips is the virtual machine vmj processor speed, vmjbw is the wideband processing capability of virtual machine vmj, and Ttwait_time is the waiting time of subtask i.In human resources, in order to get suitable resource information individuals, the formula of their fitness function is as follows:(14)f1j=1totaltimeI×uLB,(15)uLB=∑j=1m∑i=1nTijm×totaltimeI.uLB is the balance the load factor, which is proportional to the utilization of resources.
(16)f2j=1∑j=1mtaski,j−n/m2/m1<i<popsize,(17)fj=ωf1j+1−ωf2j0<ω<1,in this whichtaski,j is the task to which the virtual machine j of the individual i is assigned.The formula of selection probability is as follows:(18)Pi=fi∑j=1mfj.The formula of crossover probability is as follows:(19)Pc=Pc1−Pc1−Pc2f′−favefmax−favef′≥fave,Pc1f′<fave,Pc1=0.9,Pc2=0.6.fmax is the maximum fitness value in the current population, fave is the average value of fitness in the current population, and f′, compared with two cross individuals, is the maximum fitness value.The mutation probability formula is as follows:(20)Pm=Pm1−Pm1−Pm2f′−favefmax−favef′≥fave,Pm1f′<fave,Pm1=0.1,Pm2=0.001.


## 3.2. Improved Ant Colony Algorithm

In conventional algorithms, the randomness of ants is not high when they move, which will lead to problems when ants search for similar optimal resource information. In order to solve this problem, new rules have been formulated. The formula is as follows:(21)j=argmaxτijtα⋅ηijtβρ≤ρ0,Pρ>ρ0.ρ is the uniformly distributed random values with a value range of [0, 1], and ρ0 is the constant value.The formula of HR multiservice quality requirement function related to pheromone update rule is as follows:(22)FI=φ1totaltimeij−totaltimemintotaltimemax−totaltimemin+φ2costij−costmincostmax−costmin.totaltimeij is the expected task execution time, totaltimemax is the maximum expected task execution time, totaltimemin is the minimum expected task execution time, costij is the expected task execution cost, costmax is the Maximum cost of expected task execution, costmin is the minimum cost of expected task execution, and φ1,φ2 are the time weight coefficient and cost weight coefficient.Time weight coefficientφ1 and cost weight coefficient φ2 are in [0, 1], andφ1+φ2=1.When calculating the local pheromone increment, the ant updates the resource every time it completes the resource allocation. The formula is as follows:(23)Δτijt=const1FI.When calculating the global pheromone increment, each ant completes the resource allocation once before updating. The formula is as follows:(24)Δτijt=const2FIbest.Andτmin≤τijt≤τmax is specified, so as to avoid the stagnation of the algorithm caused by the disparity of path pheromones.In the ant colony algorithm, the distance between two cities is the same as heuristic information. However, this cannot guarantee the optimal solution; so, we need to refer to the load model to improve the expression of heuristic information. The heuristic factor function is expressed as follows:(25)ηij=1∑i=1taskTilength+Tilength/vmjmips.Tilength is the length of task i, and task is the number of tasks that have been run on virtual machine j.


## 3.3. Genetic Ant Colony Algorithm

Whengnow<gmax, compare gratio,gpop, and when the comparison result is gpop<gradio and occurs many times, the algorithm begins to transform.gnow is the current number of iterations, gmax is the maximum number of iterationsgmin is the minimum number of iterations, gratio is the minimum evolution rate, and gpop is the progeny evolution rate.When the optimal solution of ant colony algorithm is =τG, the pheromone value of the ant colony algorithm is τS=τmin+τG at the initial stage. The algorithm flow is shown in Figure 5.Figure 5
Flow chart of the genetic ant colony algorithm.


## 4. Simulation Experiment

### 4.1. Algorithm Testing

Setpopsize=100,gmax=150,gmax=50,gradio=0.5%, the maximum number of iterations is 120, and ρ=0.3,α=β=1. The overall parameter sets the task length ∈1000,10000 and the total number of tasks ∈30,330 and 200≤mips≤1000,bw=1000Mb.The data pairs obtained through experiments are shown in Table1.Table 1
Comparison of execution time and execution cost data.
Number of tasksTypeGenetic algorithmAnt colony algorithmGenetic ant colony algorithm30Execution time303030Execution cost45452560Execution time504540Execution cost76755290Execution time635550Execution cost10010188120Execution time816460Execution cost162161109150Execution time1187770Execution cost177177146180Execution time1469185Execution cost220221175210Execution time1629590Execution cost250248208240Execution time184117107Execution cost288290247270Execution time216128124Execution cost321323268300Execution time239130128Execution cost361360300330Execution time250130130Execution cost426425339Execution cost index human resource allocation in the system uses the cost needed to reflect the human resource system allocation of scientific and optimal degree. The lower the execution cost, the higher the optimization degree of human resources allocation, and the relatively reasonable cost.An alignment of execution times is shown in Figure6.Figure 6
Comparison of the execution time length of the three algorithms.The pair of execution costs is shown in Figure7.Figure 7
Comparison of the execution costs of the three algorithms.It can be seen from this that the GAACO algorithm is superior to the other two algorithms in terms of execution time and execution cost. It can be clearly seen that with the increase of the number of tasks, the execution time and cost of the ACO-GA algorithm are lower and lower than those of the other two algorithms, which shows that when the number of tasks increases gradually, the running speed of ACO-GA is better and better than those of the other two algorithms, that is, the ACO-GA algorithm has the characteristics of high efficiency, stability, and low cost.


### 4.2. Experiments and Results

#### 4.2.1. Establishing the Objective Function

Establish the database of human resource allocation and digitize the resumes of relevant personnel, that is, change the characteristic attributes of personnel into vector features, such as transforming resumesA and B into A=a1,a2,a3,B=b1,b2,b3. The distance between the vectors they transform is the similarity between the two resumes, and the formula is as follows:
(26)S=∑i=1na1−b12.The formula for judging employee grades is as follows:(27)SA,B,C=∑i=1nfA,B×Ci.fA,B is the functional relationship between files A and B, that is, similarity, and Ci is the similarity distribution weight.At the end, the formula for matching the similarity of data is as follows:(28)D=∑i=1nSmn−S11×Ci1/2.Smn is the maximum value of weight characteristic, and S11 is the minimum value of weight attribute,


#### 4.2.2. Simulation Experiment and Result Analysis

Suppose a total of 10 resumes have been uploaded to the database, the data are uploaded and run in the order from small to small. Record the uploading speed and running cost of these 10 data under the three algorithms and compare them. The data sizes are shown in Table2.Table 2
Resume data size.
NumberSize125 KB230 KB335 KB440 KB545 KB650 KB755 KB860 KB965 KB1070 KBThe data of upload speed and running cost obtained by experiments of the three algorithms are shown in Table3.Table 3
Data on upload speed and operating cost.
NumberTypeGenetic algorithmAnt colony algorithmGenetic ant colony algorithm1Upload speed90.385.398.7Operating cost0.0030.0040.00082Upload speed64.283.295.2Operating cost0.0070.0040.0013Upload speed84.983.090.8Operating cost0.0050.0050.0034Upload speed69.174.888.4Operating cost0.0090.0060.0045Upload speed55.668.283.9Operating cost0.0120.0080.0056Upload speed67.151.380.7Operating cost0.0090.0110.0067Upload speed66.360.475.3Operating cost0.0100.0130.0068Upload speed55.656.972.6Operating cost0.0140.0140.0089Upload speed49.450.170.7Operating cost0.0170.0160.00810Upload speed45.847.970.2Operating cost0.0200.0180.008The data upload speed pair is shown in Figure8.Figure 8
Comparison of data upload speed.The data running cost pair is shown in Figure9.Figure 9
Comparison chart of data operation cost.After testing and comparing the running speed and cost of the algorithms, this paper proposes to test the predicted values of the three algorithms and compare them with the actual values according to the objective function of human resource allocation similarity set above. Set up each post standard information vector characterization, compare the above 10 human resources data with post standard data, calculate similar values, and compare them. Comparative data are shown in Table4.Table 4
Comparison of similarity between predicted value and actual value of algorithm.
NumberActual valueGeneticAnt colonyGenetic ant colony180%70%68%75%278%67%65%75%385%71%70%83%475%68%70%77%583%75%77%81%680%65%70%83%786%70%71%85%876%65%65%77%979%64%66%80%1075%60%62%77%The similarity comparison data diagram is shown in Figure10.Figure 10
Similarity comparison data graph.It can be seen that in human resource data matching, the accuracy of the first two is not ideal. When the data is relatively small in the early stage, it can be seen that the genetic algorithm is slightly better than the ant colony algorithm, and the accuracy of the ant colony algorithm is slightly better than the genetic algorithm in the later stage. On the other hand, 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 demand. The next research work needs to analyze the execution efficiency of the intelligent optimization algorithm in different stages, aiming at the problems of local convergence and insufficient execution in different stages. Further expand the human resource optimization program, from the implementation system and online analysis programs to analyze the problems in human resources.


## 4.1. Algorithm Testing

Setpopsize=100,gmax=150,gmax=50,gradio=0.5%, the maximum number of iterations is 120, and ρ=0.3,α=β=1. The overall parameter sets the task length ∈1000,10000 and the total number of tasks ∈30,330 and 200≤mips≤1000,bw=1000Mb.The data pairs obtained through experiments are shown in Table1.Table 1
Comparison of execution time and execution cost data.
Number of tasksTypeGenetic algorithmAnt colony algorithmGenetic ant colony algorithm30Execution time303030Execution cost45452560Execution time504540Execution cost76755290Execution time635550Execution cost10010188120Execution time816460Execution cost162161109150Execution time1187770Execution cost177177146180Execution time1469185Execution cost220221175210Execution time1629590Execution cost250248208240Execution time184117107Execution cost288290247270Execution time216128124Execution cost321323268300Execution time239130128Execution cost361360300330Execution time250130130Execution cost426425339Execution cost index human resource allocation in the system uses the cost needed to reflect the human resource system allocation of scientific and optimal degree. The lower the execution cost, the higher the optimization degree of human resources allocation, and the relatively reasonable cost.An alignment of execution times is shown in Figure6.Figure 6
Comparison of the execution time length of the three algorithms.The pair of execution costs is shown in Figure7.Figure 7
Comparison of the execution costs of the three algorithms.It can be seen from this that the GAACO algorithm is superior to the other two algorithms in terms of execution time and execution cost. It can be clearly seen that with the increase of the number of tasks, the execution time and cost of the ACO-GA algorithm are lower and lower than those of the other two algorithms, which shows that when the number of tasks increases gradually, the running speed of ACO-GA is better and better than those of the other two algorithms, that is, the ACO-GA algorithm has the characteristics of high efficiency, stability, and low cost.


## 4.2. Experiments and Results

### 4.2.1. Establishing the Objective Function

Establish the database of human resource allocation and digitize the resumes of relevant personnel, that is, change the characteristic attributes of personnel into vector features, such as transforming resumesA and B into A=a1,a2,a3,B=b1,b2,b3. The distance between the vectors they transform is the similarity between the two resumes, and the formula is as follows:
(26)S=∑i=1na1−b12.The formula for judging employee grades is as follows:(27)SA,B,C=∑i=1nfA,B×Ci.fA,B is the functional relationship between files A and B, that is, similarity, and Ci is the similarity distribution weight.At the end, the formula for matching the similarity of data is as follows:(28)D=∑i=1nSmn−S11×Ci1/2.Smn is the maximum value of weight characteristic, and S11 is the minimum value of weight attribute,


### 4.2.2. Simulation Experiment and Result Analysis

Suppose a total of 10 resumes have been uploaded to the database, the data are uploaded and run in the order from small to small. Record the uploading speed and running cost of these 10 data under the three algorithms and compare them. The data sizes are shown in Table2.Table 2
Resume data size.
NumberSize125 KB230 KB335 KB440 KB545 KB650 KB755 KB860 KB965 KB1070 KBThe data of upload speed and running cost obtained by experiments of the three algorithms are shown in Table3.Table 3
Data on upload speed and operating cost.
NumberTypeGenetic algorithmAnt colony algorithmGenetic ant colony algorithm1Upload speed90.385.398.7Operating cost0.0030.0040.00082Upload speed64.283.295.2Operating cost0.0070.0040.0013Upload speed84.983.090.8Operating cost0.0050.0050.0034Upload speed69.174.888.4Operating cost0.0090.0060.0045Upload speed55.668.283.9Operating cost0.0120.0080.0056Upload speed67.151.380.7Operating cost0.0090.0110.0067Upload speed66.360.475.3Operating cost0.0100.0130.0068Upload speed55.656.972.6Operating cost0.0140.0140.0089Upload speed49.450.170.7Operating cost0.0170.0160.00810Upload speed45.847.970.2Operating cost0.0200.0180.008The data upload speed pair is shown in Figure8.Figure 8
Comparison of data upload speed.The data running cost pair is shown in Figure9.Figure 9
Comparison chart of data operation cost.After testing and comparing the running speed and cost of the algorithms, this paper proposes to test the predicted values of the three algorithms and compare them with the actual values according to the objective function of human resource allocation similarity set above. Set up each post standard information vector characterization, compare the above 10 human resources data with post standard data, calculate similar values, and compare them. Comparative data are shown in Table4.Table 4
Comparison of similarity between predicted value and actual value of algorithm.
NumberActual valueGeneticAnt colonyGenetic ant colony180%70%68%75%278%67%65%75%385%71%70%83%475%68%70%77%583%75%77%81%680%65%70%83%786%70%71%85%876%65%65%77%979%64%66%80%1075%60%62%77%The similarity comparison data diagram is shown in Figure10.Figure 10
Similarity comparison data graph.It can be seen that in human resource data matching, the accuracy of the first two is not ideal. When the data is relatively small in the early stage, it can be seen that the genetic algorithm is slightly better than the ant colony algorithm, and the accuracy of the ant colony algorithm is slightly better than the genetic algorithm in the later stage. On the other hand, 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 demand. The next research work needs to analyze the execution efficiency of the intelligent optimization algorithm in different stages, aiming at the problems of local convergence and insufficient execution in different stages. Further expand the human resource optimization program, from the implementation system and online analysis programs to analyze the problems in human resources.


## 4.2.1. Establishing the Objective Function

Establish the database of human resource allocation and digitize the resumes of relevant personnel, that is, change the characteristic attributes of personnel into vector features, such as transforming resumesA and B into A=a1,a2,a3,B=b1,b2,b3. The distance between the vectors they transform is the similarity between the two resumes, and the formula is as follows:
(26)S=∑i=1na1−b12.The formula for judging employee grades is as follows:(27)SA,B,C=∑i=1nfA,B×Ci.fA,B is the functional relationship between files A and B, that is, similarity, and Ci is the similarity distribution weight.At the end, the formula for matching the similarity of data is as follows:(28)D=∑i=1nSmn−S11×Ci1/2.Smn is the maximum value of weight characteristic, and S11 is the minimum value of weight attribute,


## 4.2.2. Simulation Experiment and Result Analysis

Suppose a total of 10 resumes have been uploaded to the database, the data are uploaded and run in the order from small to small. Record the uploading speed and running cost of these 10 data under the three algorithms and compare them. The data sizes are shown in Table2.Table 2
Resume data size.
NumberSize125 KB230 KB335 KB440 KB545 KB650 KB755 KB860 KB965 KB1070 KBThe data of upload speed and running cost obtained by experiments of the three algorithms are shown in Table3.Table 3
Data on upload speed and operating cost.
NumberTypeGenetic algorithmAnt colony algorithmGenetic ant colony algorithm1Upload speed90.385.398.7Operating cost0.0030.0040.00082Upload speed64.283.295.2Operating cost0.0070.0040.0013Upload speed84.983.090.8Operating cost0.0050.0050.0034Upload speed69.174.888.4Operating cost0.0090.0060.0045Upload speed55.668.283.9Operating cost0.0120.0080.0056Upload speed67.151.380.7Operating cost0.0090.0110.0067Upload speed66.360.475.3Operating cost0.0100.0130.0068Upload speed55.656.972.6Operating cost0.0140.0140.0089Upload speed49.450.170.7Operating cost0.0170.0160.00810Upload speed45.847.970.2Operating cost0.0200.0180.008The data upload speed pair is shown in Figure8.Figure 8
Comparison of data upload speed.The data running cost pair is shown in Figure9.Figure 9
Comparison chart of data operation cost.After testing and comparing the running speed and cost of the algorithms, this paper proposes to test the predicted values of the three algorithms and compare them with the actual values according to the objective function of human resource allocation similarity set above. Set up each post standard information vector characterization, compare the above 10 human resources data with post standard data, calculate similar values, and compare them. Comparative data are shown in Table4.Table 4
Comparison of similarity between predicted value and actual value of algorithm.
NumberActual valueGeneticAnt colonyGenetic ant colony180%70%68%75%278%67%65%75%385%71%70%83%475%68%70%77%583%75%77%81%680%65%70%83%786%70%71%85%876%65%65%77%979%64%66%80%1075%60%62%77%The similarity comparison data diagram is shown in Figure10.Figure 10
Similarity comparison data graph.It can be seen that in human resource data matching, the accuracy of the first two is not ideal. When the data is relatively small in the early stage, it can be seen that the genetic algorithm is slightly better than the ant colony algorithm, and the accuracy of the ant colony algorithm is slightly better than the genetic algorithm in the later stage. On the other hand, 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 demand. The next research work needs to analyze the execution efficiency of the intelligent optimization algorithm in different stages, aiming at the problems of local convergence and insufficient execution in different stages. Further expand the human resource optimization program, from the implementation system and online analysis programs to analyze the problems in human resources.


## 5. Conclusion

The ACO-GA algorithm is improved and fused by the basic algorithm. At the beginning of the design, it was designed to make it take the strengths of the two and promote the weaknesses of the two. Through experiments, it is found that this method can really keep the convergence speed at a high level in the early and late stages and keep a high stability. Therefore, in the human resource allocation module, the ACO-GA algorithm is a successful example of improvement.



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*Source: 9903319-2022-08-01.xml*