Pub Date : 2021-05-21DOI: 10.1109/ICIST52614.2021.9440587
Shalin Tong, Jie Zhong, Bowen Li
Note that state transition space of Boolean networks is determined by both network structure (node’s coupling relationships) and logical functions (update rules for nodes). This paper studies structural controllability of Boolean control networks (BCNs) under partial information, where only part of information about connections among nodes are known. Then, using semi-tensor product of matrices and algebraic forms of BCNs, two types of structural controllability are presented according to different cases of logical functions. Subsequently, certain sufficient and necessary criteria are established for structurally controllablility of BCNs with partial information. Finally, a numerical example is provided to illustrate the effectiveness of the obtained theoretical results.
{"title":"Structural Controllability of Boolean Control Networks with known nodes coupling relationships","authors":"Shalin Tong, Jie Zhong, Bowen Li","doi":"10.1109/ICIST52614.2021.9440587","DOIUrl":"https://doi.org/10.1109/ICIST52614.2021.9440587","url":null,"abstract":"Note that state transition space of Boolean networks is determined by both network structure (node’s coupling relationships) and logical functions (update rules for nodes). This paper studies structural controllability of Boolean control networks (BCNs) under partial information, where only part of information about connections among nodes are known. Then, using semi-tensor product of matrices and algebraic forms of BCNs, two types of structural controllability are presented according to different cases of logical functions. Subsequently, certain sufficient and necessary criteria are established for structurally controllablility of BCNs with partial information. Finally, a numerical example is provided to illustrate the effectiveness of the obtained theoretical results.","PeriodicalId":371599,"journal":{"name":"2021 11th International Conference on Information Science and Technology (ICIST)","volume":"252 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134218504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-05-21DOI: 10.1109/ICIST52614.2021.9440602
Wenqiang Ji, Qifu Qu, Junhua Gu, Meng Wang, Yiwei Zhao
This paper studies the synchronization problem for nonlinear neural network systems (NNSs) via T-S fuzzy models. Under a convex optimization framework, an improved asymptotic stability condition is obtained to ensure the synchronization of the fuzzy drive NNS with the response NNS. By introducing several auxiliary matrix multipliers, increased freedom are involved and the conservativeness can be further reduced. Simulation studies are given to show the effectiveness of the proposed method.
{"title":"Improved Synthesis and Analysis Results on Synchronization of T-S Fuzzy Neural Network Systems","authors":"Wenqiang Ji, Qifu Qu, Junhua Gu, Meng Wang, Yiwei Zhao","doi":"10.1109/ICIST52614.2021.9440602","DOIUrl":"https://doi.org/10.1109/ICIST52614.2021.9440602","url":null,"abstract":"This paper studies the synchronization problem for nonlinear neural network systems (NNSs) via T-S fuzzy models. Under a convex optimization framework, an improved asymptotic stability condition is obtained to ensure the synchronization of the fuzzy drive NNS with the response NNS. By introducing several auxiliary matrix multipliers, increased freedom are involved and the conservativeness can be further reduced. Simulation studies are given to show the effectiveness of the proposed method.","PeriodicalId":371599,"journal":{"name":"2021 11th International Conference on Information Science and Technology (ICIST)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132999027","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-05-21DOI: 10.1109/ICIST52614.2021.9440555
Junzheng Li, Dong Pang, Yu Zheng, Xinyi Le
Discrete manufacturing is becoming a popular modality, which places a higher demand on the flexibility of the production line. Traditional assembly lines require extensive manual design and cannot meet the need for flexibility. Due to the rise of reinforcement learning, we suspect that modern algorithms are crucial to further improve the flexibility of assembly. In this paper, we propose a digital twin enhanced assembly method with deep reinforcement learning. A digital twin model of the assembly line is first built. Then, the deep deterministic policy gradient based reinforcement learning agent is trained on the digital twin model. The simulation of the reinforcement learning environment is based on a mixture of simulation engine and real signals. Thus, we can balance the training efficiency and the simulation accuracy. Finally, to validate our proposed method, peg-in-hole assembly experiments were conducted and good results were observed.
{"title":"Digital Twin Enhanced Assembly Based on Deep Reinforcement Learning","authors":"Junzheng Li, Dong Pang, Yu Zheng, Xinyi Le","doi":"10.1109/ICIST52614.2021.9440555","DOIUrl":"https://doi.org/10.1109/ICIST52614.2021.9440555","url":null,"abstract":"Discrete manufacturing is becoming a popular modality, which places a higher demand on the flexibility of the production line. Traditional assembly lines require extensive manual design and cannot meet the need for flexibility. Due to the rise of reinforcement learning, we suspect that modern algorithms are crucial to further improve the flexibility of assembly. In this paper, we propose a digital twin enhanced assembly method with deep reinforcement learning. A digital twin model of the assembly line is first built. Then, the deep deterministic policy gradient based reinforcement learning agent is trained on the digital twin model. The simulation of the reinforcement learning environment is based on a mixture of simulation engine and real signals. Thus, we can balance the training efficiency and the simulation accuracy. Finally, to validate our proposed method, peg-in-hole assembly experiments were conducted and good results were observed.","PeriodicalId":371599,"journal":{"name":"2021 11th International Conference on Information Science and Technology (ICIST)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123929805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-05-21DOI: 10.1109/ICIST52614.2021.9440620
J. Bai, Rui Han, Chengan Guo
The latest medical research results and clinical practice have showed that the effectiveness of existing anti-cancer drugs is highly dependent on the genomic characteristics of patients, which means that the efficacy of the same anti-cancer drugs may be very different for different patients even if they are suffering from the same cancer disease, since they usually have different genomic features. How to select appropriate anti-cancer drugs for different cancer patients is a frontier topic and challenge in the field of precision oncology. In this paper, we design a hybrid convolutional neural network (CNN) to predict the responses of anti-cancer drugs, in which the network is constructed with two input CNN branches and two output CNN+FC (full connected) branches. One input branch is to extract the genomic feature from the input data of a cancer patient’s gene expression, mutation or copy number variations, and the other input branch is to extract the molecular fingerprint feature from the chemical structure data of the drug to be used for curing the cancer. In addition, attention mechanism is introduced to weight the two features according to their importance, the two weighted features are then concatenated into one vector and sent to the two output branches. For the two output branches, one is to predict the IC50 values and the other is to predict the sensitivity (or insensitivity) of cancer cell lines to anti-cancer drugs. Furthermore, the whole network system is optimized through an end-to-end training process with the joint loss function composed of two output losses. By this way, the excellent ability of CNNs in deep feature extraction and computation can be better utilized so as to better predict the IC50 and sensitivity and insensitivity of the cancer cells to anticancer drugs. Experimental results obtained in the paper show that the proposed method outperforms the existing state of the art methods in terms of the accuracy, sensitivity, and other key performance indexes.
{"title":"A Hybrid Convolutional Network for Prediction of Anti-cancer Drug Response","authors":"J. Bai, Rui Han, Chengan Guo","doi":"10.1109/ICIST52614.2021.9440620","DOIUrl":"https://doi.org/10.1109/ICIST52614.2021.9440620","url":null,"abstract":"The latest medical research results and clinical practice have showed that the effectiveness of existing anti-cancer drugs is highly dependent on the genomic characteristics of patients, which means that the efficacy of the same anti-cancer drugs may be very different for different patients even if they are suffering from the same cancer disease, since they usually have different genomic features. How to select appropriate anti-cancer drugs for different cancer patients is a frontier topic and challenge in the field of precision oncology. In this paper, we design a hybrid convolutional neural network (CNN) to predict the responses of anti-cancer drugs, in which the network is constructed with two input CNN branches and two output CNN+FC (full connected) branches. One input branch is to extract the genomic feature from the input data of a cancer patient’s gene expression, mutation or copy number variations, and the other input branch is to extract the molecular fingerprint feature from the chemical structure data of the drug to be used for curing the cancer. In addition, attention mechanism is introduced to weight the two features according to their importance, the two weighted features are then concatenated into one vector and sent to the two output branches. For the two output branches, one is to predict the IC50 values and the other is to predict the sensitivity (or insensitivity) of cancer cell lines to anti-cancer drugs. Furthermore, the whole network system is optimized through an end-to-end training process with the joint loss function composed of two output losses. By this way, the excellent ability of CNNs in deep feature extraction and computation can be better utilized so as to better predict the IC50 and sensitivity and insensitivity of the cancer cells to anticancer drugs. Experimental results obtained in the paper show that the proposed method outperforms the existing state of the art methods in terms of the accuracy, sensitivity, and other key performance indexes.","PeriodicalId":371599,"journal":{"name":"2021 11th International Conference on Information Science and Technology (ICIST)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124167654","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-05-21DOI: 10.1109/ICIST52614.2021.9440615
Yuyang Zhao
This paper focuses on output controllability of a specific kind of mix-valued logic control networks (MLCNs) via semi-tensor product method. First, we introduced the definition for output controllability of a specific MLCN, of which the updating of outputs is determined by both inputs and states via logical rules. Second, propositions of the number of different control sequences steering a MLCN from a given initial state to a destination output in a given number of time steps are derived. Consequently, criteria for the output controllability are obtained by construsting the output controllability matrix. Finally, a hydrogeological example is presented to verify the obtained results.
{"title":"Output Controllability of Mix-valued Logic Control Networks","authors":"Yuyang Zhao","doi":"10.1109/ICIST52614.2021.9440615","DOIUrl":"https://doi.org/10.1109/ICIST52614.2021.9440615","url":null,"abstract":"This paper focuses on output controllability of a specific kind of mix-valued logic control networks (MLCNs) via semi-tensor product method. First, we introduced the definition for output controllability of a specific MLCN, of which the updating of outputs is determined by both inputs and states via logical rules. Second, propositions of the number of different control sequences steering a MLCN from a given initial state to a destination output in a given number of time steps are derived. Consequently, criteria for the output controllability are obtained by construsting the output controllability matrix. Finally, a hydrogeological example is presented to verify the obtained results.","PeriodicalId":371599,"journal":{"name":"2021 11th International Conference on Information Science and Technology (ICIST)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130199992","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-05-21DOI: 10.1109/ICIST52614.2021.9440589
Lu Wang, Huidong Wang, Chuanzheng Bai
K-means is a well-known prototype based clustering algorithm for its simplicity and efficiency. However, most k-means methods assume different classes are represented by one prototype, which makes a limit of k-means algorithms. Recently, multi-prototype clustering methods have been raised to tackle this problem, which composed of two stages: split stage and merge stage. For multi-prototype algorithms, a proper prototype number plays a vital role in the algorithm performance and it is generally given by users in a trial and error way. In this paper, a new incremental k-means clustering algorithm is designed to determine the propriate prototype number automatically. Firstly, a new indicator is presented to judge whether the number of prototype is appropriate in the split stage. Secondly, a new merge indicator is defined according to the distance formula from datapoint to hyperplane in the merge stage. Finally, simulation results on 8 datasets illustrate the effectiveness and superiority of the proposed algorithm.
{"title":"A new multi-prototype based clustering algorithm","authors":"Lu Wang, Huidong Wang, Chuanzheng Bai","doi":"10.1109/ICIST52614.2021.9440589","DOIUrl":"https://doi.org/10.1109/ICIST52614.2021.9440589","url":null,"abstract":"K-means is a well-known prototype based clustering algorithm for its simplicity and efficiency. However, most k-means methods assume different classes are represented by one prototype, which makes a limit of k-means algorithms. Recently, multi-prototype clustering methods have been raised to tackle this problem, which composed of two stages: split stage and merge stage. For multi-prototype algorithms, a proper prototype number plays a vital role in the algorithm performance and it is generally given by users in a trial and error way. In this paper, a new incremental k-means clustering algorithm is designed to determine the propriate prototype number automatically. Firstly, a new indicator is presented to judge whether the number of prototype is appropriate in the split stage. Secondly, a new merge indicator is defined according to the distance formula from datapoint to hyperplane in the merge stage. Finally, simulation results on 8 datasets illustrate the effectiveness and superiority of the proposed algorithm.","PeriodicalId":371599,"journal":{"name":"2021 11th International Conference on Information Science and Technology (ICIST)","volume":"14 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128815813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-05-21DOI: 10.1109/ICIST52614.2021.9440567
Junqi Zhang, Peng Zu, Huan Liu
Swarm robotics is widely studied in multi-target search problem because of its low cost and adaptability in dangerous environments. But current multi-target search strategies have the problem of searching the same area repeatedly and are difficult to search the undetected area effectively. This paper proposes a learning automata-based multi-target search strategy (LAS). The strategy divides the search space into multiple cells and initializes each cell with an equal search probability. The probability distribution of cells is learned and updated by a learning automaton and employed to assign robots to search cells. If a robot detects the presence of a target in an assigned cell, it uses the simulated annealing algorithm to search the exact location of the target. The experimental results demonstrate that the proposed strategy significantly improves the search efficiency compared with the state-of-the-art methods.
{"title":"Learning Automata-Based Multi-target Search Strategy Using Swarm Robotics","authors":"Junqi Zhang, Peng Zu, Huan Liu","doi":"10.1109/ICIST52614.2021.9440567","DOIUrl":"https://doi.org/10.1109/ICIST52614.2021.9440567","url":null,"abstract":"Swarm robotics is widely studied in multi-target search problem because of its low cost and adaptability in dangerous environments. But current multi-target search strategies have the problem of searching the same area repeatedly and are difficult to search the undetected area effectively. This paper proposes a learning automata-based multi-target search strategy (LAS). The strategy divides the search space into multiple cells and initializes each cell with an equal search probability. The probability distribution of cells is learned and updated by a learning automaton and employed to assign robots to search cells. If a robot detects the presence of a target in an assigned cell, it uses the simulated annealing algorithm to search the exact location of the target. The experimental results demonstrate that the proposed strategy significantly improves the search efficiency compared with the state-of-the-art methods.","PeriodicalId":371599,"journal":{"name":"2021 11th International Conference on Information Science and Technology (ICIST)","volume":"128 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127969885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-05-21DOI: 10.1109/ICIST52614.2021.9440645
Jian Yang, Yuhui Shi
Coordinated motion control in swarm robotics aims to ensure the coherence of members in space, i.e., the robots in a swarm perform coordinated movements to maintain spatial structures. This problem can be modeled as a tracking control problem, in which individuals in the swarm follow a target position with the consideration of specific relative distance or orientations. To keep the communication cost low, the PID controller can be utilized to achieve the leader-follower tracking control task without the information of leader velocities. However, the controller’s parameters need to be optimized to adapt to situations changing, such as the different swarm population, the changing of the target to be followed, and the anti-collision demands, etc. In this letter, we apply a modified Brain Storm Optimization (BSO) algorithm to an incremental PID tracking controller to get the relatively optimal parameters adaptively for leader-follower formation control for swarm robotics. Simulation results show that the proposed method could reach the optimal parameters during robot movements. The flexibility and scalability are also validated, which ensures that the proposed method can adapt to different situations and be a good candidate for coordinated motion control for swarm robotics in more realistic scenarios.
{"title":"Adaptive Coordinated Motion Control for Swarm Robotics Based on Brain Storm Optimization","authors":"Jian Yang, Yuhui Shi","doi":"10.1109/ICIST52614.2021.9440645","DOIUrl":"https://doi.org/10.1109/ICIST52614.2021.9440645","url":null,"abstract":"Coordinated motion control in swarm robotics aims to ensure the coherence of members in space, i.e., the robots in a swarm perform coordinated movements to maintain spatial structures. This problem can be modeled as a tracking control problem, in which individuals in the swarm follow a target position with the consideration of specific relative distance or orientations. To keep the communication cost low, the PID controller can be utilized to achieve the leader-follower tracking control task without the information of leader velocities. However, the controller’s parameters need to be optimized to adapt to situations changing, such as the different swarm population, the changing of the target to be followed, and the anti-collision demands, etc. In this letter, we apply a modified Brain Storm Optimization (BSO) algorithm to an incremental PID tracking controller to get the relatively optimal parameters adaptively for leader-follower formation control for swarm robotics. Simulation results show that the proposed method could reach the optimal parameters during robot movements. The flexibility and scalability are also validated, which ensures that the proposed method can adapt to different situations and be a good candidate for coordinated motion control for swarm robotics in more realistic scenarios.","PeriodicalId":371599,"journal":{"name":"2021 11th International Conference on Information Science and Technology (ICIST)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129616965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-05-21DOI: 10.1109/ICIST52614.2021.9440636
Q. Ge, Guangbing Zhang, Xiaofeng Zhang
EEG signal records the nerve activity in the brain, which is of great significance for the diagnosis and treatment of epilepsy. Effective automatic diagnosis method for epilepsy interictal period and ictal period can predict epilepsy and prevent the hurt to the body. In this paper, an automatic epilepsy detection method is proposed based on support vector machine classifier which use the sample entropy and standard deviation features selected by the reliefF algorithm from the components of EEG signals using empirical mode decomposition and variational mode decomposition. The epilepsy EEG database of Bonn University is used to evaluate the method. The experimental results show that proposed method can distinguish the epilepsy EEG signal between interictal period and ictal period in terms of sensitivity, specificity, precision, and accuracy. The best classification accuracy is up to 97.00% using support vector machine classifier with fine gaussian kernel function based on selected features.
{"title":"Automatic detection of Epilepsy based on EMD-VMD feature components and ReliefF algorithm","authors":"Q. Ge, Guangbing Zhang, Xiaofeng Zhang","doi":"10.1109/ICIST52614.2021.9440636","DOIUrl":"https://doi.org/10.1109/ICIST52614.2021.9440636","url":null,"abstract":"EEG signal records the nerve activity in the brain, which is of great significance for the diagnosis and treatment of epilepsy. Effective automatic diagnosis method for epilepsy interictal period and ictal period can predict epilepsy and prevent the hurt to the body. In this paper, an automatic epilepsy detection method is proposed based on support vector machine classifier which use the sample entropy and standard deviation features selected by the reliefF algorithm from the components of EEG signals using empirical mode decomposition and variational mode decomposition. The epilepsy EEG database of Bonn University is used to evaluate the method. The experimental results show that proposed method can distinguish the epilepsy EEG signal between interictal period and ictal period in terms of sensitivity, specificity, precision, and accuracy. The best classification accuracy is up to 97.00% using support vector machine classifier with fine gaussian kernel function based on selected features.","PeriodicalId":371599,"journal":{"name":"2021 11th International Conference on Information Science and Technology (ICIST)","volume":"142 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115460128","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-05-21DOI: 10.1109/ICIST52614.2021.9440635
Qiang Peng, Husheng Wu, Qiming Zhu
High-dimensional complex function optimization is a significant problem in engineering applications. Wolf pack algorithm (WPA) has a good performance in the optimization of high-dimensional complex functions, however in solving high-dimensional, multi-peak complex optimization problems, there are still some disadvantages, such as low precision and ease to fall into local optimum. Thus, this paper proposes an interactive wandering wolf pack algorithm (IWWPA). IWWPA uses an interactive wandering strategy based on differential evolution algorithm to enhance the global exploration ability of scout wolf; adopts adaptive striding step length, centripetal siege strategy and optimizes the termination condition of calling behavior, which improves the efficiency of the algorithm; in the late stage of the iteration, the Gaussian-Cauchy combined mutation operator is introduced to avoid the algorithm from falling into the local optimum and "premature". In the paper, the convergence of the algorithm is analyzed by using Markov process, and then IWWPA and 6-population intelligent algorithm are used to test 14 benchmark functions and 4 variable dimension test functions in 500 and 1000 dimensions. The simulation results show that the improved algorithm has better accuracy and speed performance in solving high-dimensional complex functions.
{"title":"An interactive wandering Wolf Pack algorithm for solving High-dimensional complex functions","authors":"Qiang Peng, Husheng Wu, Qiming Zhu","doi":"10.1109/ICIST52614.2021.9440635","DOIUrl":"https://doi.org/10.1109/ICIST52614.2021.9440635","url":null,"abstract":"High-dimensional complex function optimization is a significant problem in engineering applications. Wolf pack algorithm (WPA) has a good performance in the optimization of high-dimensional complex functions, however in solving high-dimensional, multi-peak complex optimization problems, there are still some disadvantages, such as low precision and ease to fall into local optimum. Thus, this paper proposes an interactive wandering wolf pack algorithm (IWWPA). IWWPA uses an interactive wandering strategy based on differential evolution algorithm to enhance the global exploration ability of scout wolf; adopts adaptive striding step length, centripetal siege strategy and optimizes the termination condition of calling behavior, which improves the efficiency of the algorithm; in the late stage of the iteration, the Gaussian-Cauchy combined mutation operator is introduced to avoid the algorithm from falling into the local optimum and \"premature\". In the paper, the convergence of the algorithm is analyzed by using Markov process, and then IWWPA and 6-population intelligent algorithm are used to test 14 benchmark functions and 4 variable dimension test functions in 500 and 1000 dimensions. The simulation results show that the improved algorithm has better accuracy and speed performance in solving high-dimensional complex functions.","PeriodicalId":371599,"journal":{"name":"2021 11th International Conference on Information Science and Technology (ICIST)","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130736781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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