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Trend anomaly detection is the practice of comparing and analyzing current and historical data trends to detect real-time abnormalities in online industrial data-streams. It has the advantages of tracking a concept drift automatically and predicting trend changes in the shortest time, making it important both for algorithmic research and industry. However, industrial data streams contain considerable noise that interferes with detecting weak anomalies. In this paper, the fastest detection algorithm “sliding nesting” is adopted. It is based on calculating the data weight in each window by applying variable weights, while maintaining the method of trend-effective integration accumulation. The new algorithm changes the traditional calculation method of the trend anomaly detection score, which calculates the score in a short window. This algorithm, SNWFD–DS, can detect weak trend abnormalities in the presence of noise interference. Compared with other methods, it has significant advantages. An on-site oil drilling data test shows that this method can significantly reduce delays compared with other methods and can improve the detection accuracy of weak trend anomalies under noise interference.
Guang Li; Jing Liang; Caitong Yue. Research on the Fastest Detection Method for Weak Trends under Noise Interference. Entropy 2021, 23, 1093 .
AMA StyleGuang Li, Jing Liang, Caitong Yue. Research on the Fastest Detection Method for Weak Trends under Noise Interference. Entropy. 2021; 23 (8):1093.
Chicago/Turabian StyleGuang Li; Jing Liang; Caitong Yue. 2021. "Research on the Fastest Detection Method for Weak Trends under Noise Interference." Entropy 23, no. 8: 1093.
In the past few years, a wealth of sample-specific network construction methods and structural network control methods has been proposed to identify sample-specific driver nodes for supporting the Sample-Specific network Control (SSC) analysis of biological networked systems. However, there is no comprehensive evaluation for these state-of-the-art methods. Here, we conducted a performance assessment for 16 SSC analysis workflows by using the combination of 4 sample-specific network reconstruction methods and 4 representative structural control methods. This study includes simulation evaluation of representative biological networks, personalized driver genes prioritization on multiple cancer bulk expression datasets with matched patient samples from TCGA, and cell marker genes and key time point identification related to cell differentiation on single-cell RNA-seq datasets. By widely comparing analysis of existing SSC analysis workflows, we provided the following recommendations and banchmarking workflows. (i) The performance of a network control method is strongly dependent on the up-stream sample-specific network method, and Cell-Specific Network construction (CSN) method and Single-Sample Network (SSN) method are the preferred sample-specific network construction methods. (ii) After constructing the sample-specific networks, the undirected network-based control methods are more effective than the directed network-based control methods. In addition, these data and evaluation pipeline are freely available on https://github.com/WilfongGuo/Benchmark_control.
Wei-Feng Guo; Xiangtian Yu; Qian-Qian Shi; Jing Liang; Shao-Wu Zhang; Tao Zeng. Performance assessment of sample-specific network control methods for bulk and single-cell biological data analysis. PLOS Computational Biology 2021, 17, e1008962 .
AMA StyleWei-Feng Guo, Xiangtian Yu, Qian-Qian Shi, Jing Liang, Shao-Wu Zhang, Tao Zeng. Performance assessment of sample-specific network control methods for bulk and single-cell biological data analysis. PLOS Computational Biology. 2021; 17 (5):e1008962.
Chicago/Turabian StyleWei-Feng Guo; Xiangtian Yu; Qian-Qian Shi; Jing Liang; Shao-Wu Zhang; Tao Zeng. 2021. "Performance assessment of sample-specific network control methods for bulk and single-cell biological data analysis." PLOS Computational Biology 17, no. 5: e1008962.
Feature selection is an essential preprocessing step in data mining and machine learning. A feature selection task can be treated as a multi-objective optimization problem which simultaneously minimizes the classification error and the number of selected features. Many existing feature selection approaches including multi-objective methods neglect that there exists multiple optimal solutions in feature selection. There can be multiple different optimal feature subsets which achieve the same or similar classification performance. Furthermore, when using evolutionary multi-objective optimization for feature selection, a crowding distance metric is typically used to play a role in environmental selection. However, some existing calculations of crowding metrics based on continuous/numeric values are inappropriate for feature selection since the search space of feature selection is discrete. Therefore, this paper proposes a new environmental selection method to modify the calculation of crowding metrics. The proposed approach is expected to help a multi-objective feature selection algorithm to find multiple potential optimal feature subsets. Experiments on sixteen different datasets of varying difficulty show that the proposed approach can find more diverse feature subsets, achieving the same classification performance without deteriorating performance regarding hypervolume and inverted generational distance.
Peng Wang; Bing Xue; Jing Liang; Mengjie Zhang. Improved Crowding Distance in Multi-objective Optimization for Feature Selection in Classification. Augmented Cognition. Enhancing Cognition and Behavior in Complex Human Environments 2021, 489 -505.
AMA StylePeng Wang, Bing Xue, Jing Liang, Mengjie Zhang. Improved Crowding Distance in Multi-objective Optimization for Feature Selection in Classification. Augmented Cognition. Enhancing Cognition and Behavior in Complex Human Environments. 2021; ():489-505.
Chicago/Turabian StylePeng Wang; Bing Xue; Jing Liang; Mengjie Zhang. 2021. "Improved Crowding Distance in Multi-objective Optimization for Feature Selection in Classification." Augmented Cognition. Enhancing Cognition and Behavior in Complex Human Environments , no. : 489-505.
Solving constrained multiobjective optimization problems brings great challenges to an evolutionary algorithm, since it simultaneously requires the optimization among several conflicting objective functions and the satisfaction of various constraints. Hence, how to adjust the tradeoff between objective functions and constraints is crucial. In this article, we propose a dynamic selection preference-assisted constrained multiobjective differential evolutionary (DE) algorithm. In our approach, the selection preference of each individual is suitably switching from the objective functions to constraints as the evolutionary process. To be specific, the information of objective function, without considering any constraints, is extracted based on Pareto dominance to maintain the convergence and diversity by exploring the feasible and infeasible regions; while the information of constraint is used based on constrained dominance principle to promote the feasibility. Then, the tradeoff in these two kinds of information is adjusted dynamically, by emphasizing the utilization of objective functions at the early stage and focusing on constraints at the latter stage. Furthermore, to generate the promising offspring, two DE operators with distinct characteristics are selected as components of the search algorithm. Experiments on four test suites including 56 benchmark problems indicate that the proposed method exhibits superior or at least competitive performance, in comparison with other well-established methods.
Kunjie Yu; Jing Liang; Boyang Qu; Yong Luo; Caitong Yue. Dynamic Selection Preference-Assisted Constrained Multiobjective Differential Evolution. IEEE Transactions on Systems, Man, and Cybernetics: Systems 2021, PP, 1 -12.
AMA StyleKunjie Yu, Jing Liang, Boyang Qu, Yong Luo, Caitong Yue. Dynamic Selection Preference-Assisted Constrained Multiobjective Differential Evolution. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 2021; PP (99):1-12.
Chicago/Turabian StyleKunjie Yu; Jing Liang; Boyang Qu; Yong Luo; Caitong Yue. 2021. "Dynamic Selection Preference-Assisted Constrained Multiobjective Differential Evolution." IEEE Transactions on Systems, Man, and Cybernetics: Systems PP, no. 99: 1-12.
In multiobjective optimization, the relationship between decision space and objective space is generally assumed to be a one-to-one mapping, but it is not always the case. In some problems, different variables have the same or similar objective value, which means a many-to-one mapping. In this situation, there is more than one Pareto Set (PS) mapping to the same Pareto Front (PF) and these problems are called multimodal multiobjective problems. This paper proposes a multimodal multiobjective differential evolution algorithm to solve these problems. In the proposed method, the difference vector is generated taking the diversity in both decision and objective space into account. The way to calculate crowding distance is quite different from the others. In the crowding distance calculation process, all the selected individuals are taken into account instead of considering each Pareto rank separately. The crowding distance in decision space is replaced with the weighted sum of Euclidean distances to its neighbors. In the environmental selection process, not all the individuals in top ranks are selected, because some of them may be very crowded. Instead, the potential solutions in the bottom rank are given a chance to evolve. With these operations, the proposed algorithm can maintain multiple PSs of multimodal multiobjective optimization problems and improve the diversity in both decision and objective space. Experimental results show that the proposed method can achieve high comprehensive performance.
Caitong Yue; P.N. Suganthan; Jing Liang; Boyang Qu; Kunjie Yu; Yongsheng Zhu; Li Yan. Differential evolution using improved crowding distance for multimodal multiobjective optimization. Swarm and Evolutionary Computation 2021, 62, 100849 .
AMA StyleCaitong Yue, P.N. Suganthan, Jing Liang, Boyang Qu, Kunjie Yu, Yongsheng Zhu, Li Yan. Differential evolution using improved crowding distance for multimodal multiobjective optimization. Swarm and Evolutionary Computation. 2021; 62 ():100849.
Chicago/Turabian StyleCaitong Yue; P.N. Suganthan; Jing Liang; Boyang Qu; Kunjie Yu; Yongsheng Zhu; Li Yan. 2021. "Differential evolution using improved crowding distance for multimodal multiobjective optimization." Swarm and Evolutionary Computation 62, no. : 100849.
Increasing the accuracy and intelligence of short-term load forecasting system can improve modern power systems management and economic power generation. In recent decades, the optimized machine learning methods have been widely used in load forecasting problems because of their predictability with higher accuracy and robustness. However, most related researches only use evolutionary algorithms for parameters fine-tuning and ignore the evolutionary algorithm based decision-making support and the matching relation between the used evolutionary algorithm and machine learning method, which greatly limit the improvement of forecasting system. To dissolve the above issues, a data-driven evolutionary ensemble learning forecasting model is proposed in this paper. Firstly, a novel multimodal evolutionary algorithm based on comprehensive weighted vector angle and shift-based density estimation is proposed. Secondly, based on the proposed multimodal evolutionary algorithm, an intelligent decision-making support scheme including predictive performance evaluation, model properties analysis, structure and fusion strategy optimization, and optimal model preference selection is designed to improve the random vector functional link network based ensemble learning model and boost the forecasting accuracy. Thirdly, experimental studies on 15 test problems with up to 6000 decision variables are conducted to validate the excellent optimization ability of the proposed evolutionary algorithm. Finally, the proposed evolutionary ensemble learning method is compared with 6 other representative forecast methods on real-world short-term load forecasting datasets from Australia, Great Britain, and Norway. The experiment results verify the superiority and applicability of the proposed method.
Yi Hu; Boyang Qu; Jie Wang; Jing Liang; Yanli Wang; Kunjie Yu; Yaxin Li; Kangjia Qiao. Short-term load forecasting using multimodal evolutionary algorithm and random vector functional link network based ensemble learning. Applied Energy 2021, 285, 116415 .
AMA StyleYi Hu, Boyang Qu, Jie Wang, Jing Liang, Yanli Wang, Kunjie Yu, Yaxin Li, Kangjia Qiao. Short-term load forecasting using multimodal evolutionary algorithm and random vector functional link network based ensemble learning. Applied Energy. 2021; 285 ():116415.
Chicago/Turabian StyleYi Hu; Boyang Qu; Jie Wang; Jing Liang; Yanli Wang; Kunjie Yu; Yaxin Li; Kangjia Qiao. 2021. "Short-term load forecasting using multimodal evolutionary algorithm and random vector functional link network based ensemble learning." Applied Energy 285, no. : 116415.
When solving constrained multiobjective optimization problems by evolutionary algorithm, the key challenge is how to achieve the balance among convergence, diversity, and feasibility. To deal with this challenge, a purpose-directed two-phase multiobjective differential evolution (PDTP-MDE) algorithm is developed in this paper. The main idea of PDTP-MDE is that the whole evolution process is divided into two sequential phases according to the expected purpose of each stage. To be specific, the first phase aims at keeping the balance between convergence and diversity, while the feasibility is taken as an auxiliary indicator. In this way, the population is capable of exploring different potential areas and avoiding to be trapped into local ones, thus providing more information about convergence and diversity for the later evolution process. Afterwards, the second phase mainly tends to maintain feasibility and diversity by selecting and using some promising infeasible solutions according to the population evolution status. In addition, an archive is maintained after each phase to preserve the superior feasible Pareto solutions found so far. By the above processes, the feasible Pareto front with well convergence and well diversity is obtained. The comprehensive experiments on 42 benchmark problems from three test suites demonstrate the superiority and competitiveness of the proposed PDTP-MDE, in comparison with other state-of-the-art constrained multiobjective evolutionary algorithms.
Kunjie Yu; Jing Liang; Boyang Qu; Caitong Yue. Purpose-directed two-phase multiobjective differential evolution for constrained multiobjective optimization. Swarm and Evolutionary Computation 2020, 60, 100799 .
AMA StyleKunjie Yu, Jing Liang, Boyang Qu, Caitong Yue. Purpose-directed two-phase multiobjective differential evolution for constrained multiobjective optimization. Swarm and Evolutionary Computation. 2020; 60 ():100799.
Chicago/Turabian StyleKunjie Yu; Jing Liang; Boyang Qu; Caitong Yue. 2020. "Purpose-directed two-phase multiobjective differential evolution for constrained multiobjective optimization." Swarm and Evolutionary Computation 60, no. : 100799.
Multimodal Multi-objective Optimization Problems (MMOPs) refer to the problems that have multiple Pareto-optimal solution sets in decision space corresponding to the same or similar Pareto-optimal front in objective space. These problems require the optimization algorithm to locate multiple Pareto Sets (PSs). This paper proposes a differential evolution algorithm based on the clustering technique and an elite selection mechanism to solve MMOPs. In this algorithm, a Clustering-based Special Crowding Distance (CSCD) method is designed to calculate the comprehensive crowding degree in decision and objective spaces. Subsequently, a distance-based elite selection mechanism (DBESM) is introduced to determine the learning exemplars of various individuals. New individuals are generated around the exemplars to obtain a well-distributed population in both decision and objective spaces. To test the performance of the proposed algorithm, extensive experiments on the suit of CEC'2019 benchmark functions have been conducted. The results indicate that the proposed method has superior performance compared with other commonly used algorithms.
Jing Liang; Kangjia Qiao; Caitong Yue; Kunjie Yu; Boyang Qu; Ruohao Xu; Zhimeng Li; Yi Hu. A clustering-based differential evolution algorithm for solving multimodal multi-objective optimization problems. Swarm and Evolutionary Computation 2020, 60, 100788 .
AMA StyleJing Liang, Kangjia Qiao, Caitong Yue, Kunjie Yu, Boyang Qu, Ruohao Xu, Zhimeng Li, Yi Hu. A clustering-based differential evolution algorithm for solving multimodal multi-objective optimization problems. Swarm and Evolutionary Computation. 2020; 60 ():100788.
Chicago/Turabian StyleJing Liang; Kangjia Qiao; Caitong Yue; Kunjie Yu; Boyang Qu; Ruohao Xu; Zhimeng Li; Yi Hu. 2020. "A clustering-based differential evolution algorithm for solving multimodal multi-objective optimization problems." Swarm and Evolutionary Computation 60, no. : 100788.
Ensemble learning is a system that combines a set of base learners to improve the performance in machine learning, where accuracy and diversity of base learners are two important factors. However, these two factors are usually contradictory. To address this problem, in this paper, we propose a novel ensemble learning algorithm based on fitness Euclidean-distance ratio differential evolution, to train the neural network ensemble. FEFERR_ELA employs a multimodal evolutionary algorithm that is capable of producing diverse solutions to search for optimal solutions corresponding to parameters of base learners, where each optimal solution leads to one trained model. A dynamic ensemble selection scheme is applied to select appropriate individuals for the ensemble. The proposed algorithm is evaluated on several benchmark problems and compared with some related ensemble learning models. The experimental results demonstrate that the proposed algorithm outperforms the related works and can produce the neural network ensembles with better generalization.
Jing Liang; Yunpeng Wei; Boyang Qu; Caitong Yue; Hui Song. Ensemble learning based on fitness Euclidean-distance ratio differential evolution for classification. Natural Computing 2020, 20, 77 -87.
AMA StyleJing Liang, Yunpeng Wei, Boyang Qu, Caitong Yue, Hui Song. Ensemble learning based on fitness Euclidean-distance ratio differential evolution for classification. Natural Computing. 2020; 20 (1):77-87.
Chicago/Turabian StyleJing Liang; Yunpeng Wei; Boyang Qu; Caitong Yue; Hui Song. 2020. "Ensemble learning based on fitness Euclidean-distance ratio differential evolution for classification." Natural Computing 20, no. 1: 77-87.
Different algorithms and strategies behave disparately for different types of problems. In practical problems, we cannot grasp the nature of the problem in advance, so it is difficult for the engineers to choose a proper method to solve the problem effectively. In this case, the strategy selection task based on fitness landscape analysis comes into being. This paper gives a preliminary study on mutation strategy selection on the basis of fitness landscape analysis for continuous real-parameter optimization based on differential evolution. Some fundamental features of the fitness landscape and the components of standard differential evolution algorithm are described in detail. A mutation strategy selection framework based on fitness landscape analysis is designed. Some different types of classifiers which are applied to the proposed framework are tested and compared.
Jing Liang; Yaxin Li; Boyang Qu; Kunjie Yu; Yi Hu. Mutation Strategy Selection Based on Fitness Landscape Analysis: A Preliminary Study. Communications in Computer and Information Science 2020, 284 -298.
AMA StyleJing Liang, Yaxin Li, Boyang Qu, Kunjie Yu, Yi Hu. Mutation Strategy Selection Based on Fitness Landscape Analysis: A Preliminary Study. Communications in Computer and Information Science. 2020; ():284-298.
Chicago/Turabian StyleJing Liang; Yaxin Li; Boyang Qu; Kunjie Yu; Yi Hu. 2020. "Mutation Strategy Selection Based on Fitness Landscape Analysis: A Preliminary Study." Communications in Computer and Information Science , no. : 284-298.
Automated chromosome classification is a vital task in cytogenetics and has been a common pattern recognition problem. Numerous attempts were made in the past decade years to characterize chromosomes for the intention of medical and cancer cytogenetics research. This paper proposes a graphic geometry-based approach for medial axis extraction and Competitive Extreme Learning Machine Teams (CELMT) with further correction method for chromosome classification. The initial two medial axis points are determined firstly according to the length of the intercept line in different directions, and then the complete medial axis for feature extraction is drawn based on the initial two points. After that, a base classifier ELMi, j is trained to differentiate a pair class chromosome (i, j), a total number of 276 classifiers are trained. Each base classifier will give a label and the final label will be determined by majority voting and further correction rules. Based on the experiment results, the method proposed in this paper can precisely extract the medial axis and extract the features to recognize the chromosome, the classification accuracy by using CELMT can achieve an average value of 96.23% and the running time is much shorter than the other classification algorithms.
Jie Wang; Chaohao Zhao; Jing Liang; Caitong Yue; Xiangyang Ren; Ke Bai. Chromosome Medial Axis Extraction Method Based on Graphic Geometry and Competitive Extreme Learning Machines Teams (CELMT) Classifier for Chromosome Classification. Communications in Computer and Information Science 2020, 550 -564.
AMA StyleJie Wang, Chaohao Zhao, Jing Liang, Caitong Yue, Xiangyang Ren, Ke Bai. Chromosome Medial Axis Extraction Method Based on Graphic Geometry and Competitive Extreme Learning Machines Teams (CELMT) Classifier for Chromosome Classification. Communications in Computer and Information Science. 2020; ():550-564.
Chicago/Turabian StyleJie Wang; Chaohao Zhao; Jing Liang; Caitong Yue; Xiangyang Ren; Ke Bai. 2020. "Chromosome Medial Axis Extraction Method Based on Graphic Geometry and Competitive Extreme Learning Machines Teams (CELMT) Classifier for Chromosome Classification." Communications in Computer and Information Science , no. : 550-564.
Ensemble learning is an important element in machine learning. However, two essential tasks, including training base classifiers and finding a suitable ensemble balance for the diversity and accuracy of these base classifiers, are need to be achieved. In this paper, a novel ensemble method, which utilizes a multimodal multiobjective differential evolution (MMODE) algorithm to select feature subsets and optimize base classifiers parameters, is proposed. Moreover, three methods including minimum error ensemble, all Pareto sets ensemble, and error reduction ensemble are employed to construct ensemble classifiers for executing classification tasks. Experimental results on several benchmark classification databases evidence that the proposed algorithm is valid.
Jie Wang; Bo Wang; Jing Liang; Kunjie Yu; Caitong Yue; Xiangyang Ren. Ensemble Learning via Multimodal Multiobjective Differential Evolution and Feature Selection. Communications in Computer and Information Science 2020, 439 -453.
AMA StyleJie Wang, Bo Wang, Jing Liang, Kunjie Yu, Caitong Yue, Xiangyang Ren. Ensemble Learning via Multimodal Multiobjective Differential Evolution and Feature Selection. Communications in Computer and Information Science. 2020; ():439-453.
Chicago/Turabian StyleJie Wang; Bo Wang; Jing Liang; Kunjie Yu; Caitong Yue; Xiangyang Ren. 2020. "Ensemble Learning via Multimodal Multiobjective Differential Evolution and Feature Selection." Communications in Computer and Information Science , no. : 439-453.
Solving and optimizing combinatorial problems require high computational power because of their exponential growth and requirement of high processing power. During this study, a hybrid algorithm (Genetic Ant Colony Optimization Algorithm) is proposed in comparison with standard algorithm (Ant Colony Optimization Algorithm). Further the parameters for Ant Colony Optimization Algorithm are instinctively tuned to different levels of all heuristics to obtain suboptimal level, then multiple crossovers and mutation operators are used alongside those selected parameters while generating results with hybrid algorithm. The main emphasis of the proposed algorithm is the selection and tuning of parameters, which is extremely influential in this case. The algorithm has been tested on six benchmarks of TSPLIB. The results were compared with standard ACO algorithm, the hybrid algorithm outperformed the standard ACO algorithm.
Usman Ashraf; Jing Liang; Aleena Akhtar; Kunjie Yu; Yi Hu; Caitong Yue; Abdul Mannan Masood; Muhammad Kashif. Meta-heuristic Hybrid Algorithmic Approach for Solving Combinatorial Optimization Problem (TSP). Communications in Computer and Information Science 2020, 622 -633.
AMA StyleUsman Ashraf, Jing Liang, Aleena Akhtar, Kunjie Yu, Yi Hu, Caitong Yue, Abdul Mannan Masood, Muhammad Kashif. Meta-heuristic Hybrid Algorithmic Approach for Solving Combinatorial Optimization Problem (TSP). Communications in Computer and Information Science. 2020; ():622-633.
Chicago/Turabian StyleUsman Ashraf; Jing Liang; Aleena Akhtar; Kunjie Yu; Yi Hu; Caitong Yue; Abdul Mannan Masood; Muhammad Kashif. 2020. "Meta-heuristic Hybrid Algorithmic Approach for Solving Combinatorial Optimization Problem (TSP)." Communications in Computer and Information Science , no. : 622-633.
In the expensive structural optimization, the data-driven surrogate model has been proven to be an effective alternative to physical simulation (or experiment). However, the static surrogate-assisted evolutionary algorithm (SAEA) often becomes powerless and inefficient when dealing with different types of expensive optimization problems. Therefore, how to select high-reliability surrogates to assist an evolutionary algorithm (EA) has always been a challenging task. This study aimed to dynamically provide an optimal surrogate for EA by developing a brand-new SAEA framework. Firstly, an adaptive surrogate model (ASM) selection technology was proposed. In ASM, according to different integration criteria from the strategy pool, elite meta-models were recombined into multiple ensemble surrogates in each iteration. Afterward, a promising model was adaptively picked out from the model pool based on the minimum root of mean square error (RMSE). Secondly, we investigated a novel ASM-based EA framework, namely ASMEA, where the reliability of all models was updated in real-time by generating new samples online. Thirdly, to verify the performance of the ASMEA framework, two instantiation algorithms are widely compared with several state-of-the-art algorithms on a commonly used benchmark test set. Finally, a real-world antenna structural optimization problem was solved by the proposed algorithms. The results demonstrate that the proposed framework is able to provide a high-reliability surrogate to assist EA in solving expensive optimization problems.
Mingyuan Yu; Xia Li; Jing Liang. A dynamic surrogate-assisted evolutionary algorithm framework for expensive structural optimization. Structural Optimization 2019, 61, 711 -729.
AMA StyleMingyuan Yu, Xia Li, Jing Liang. A dynamic surrogate-assisted evolutionary algorithm framework for expensive structural optimization. Structural Optimization. 2019; 61 (2):711-729.
Chicago/Turabian StyleMingyuan Yu; Xia Li; Jing Liang. 2019. "A dynamic surrogate-assisted evolutionary algorithm framework for expensive structural optimization." Structural Optimization 61, no. 2: 711-729.
This paper proposes a self-organized speciation based multi-objective particle swarm optimizer (SS-MOPSO) to locate multiple Pareto optimal solutions for solving multimodal multi-objective problems. In the proposed method, the speciation strategy is used to form stable niches and these niches/subpopulations are optimized to search and maintain Pareto-optimal solutions in parallel. Moreover, a self-organized mechanism is proposed to improve the efficiency of the species formulation as well as the performance of the algorithm. To maintain the diversity of the solutions in both the decision and objective spaces, SS-MOPSO is incorporated with the non-dominated sorting scheme and special crowding distance techniques. The performance of SS-MOPSO is compared with a number of the state-of-the-art multi-objective optimization algorithms on fourteen test problems. Moreover, the proposed SS-MOSPO is also employed to solve a real-life problem. The experimental results suggest that the proposed algorithm is able to solve the multimodal multi-objective problems effectively and shows superior performance by finding more and better distributed Pareto solutions.
Boyang Qu; Chao Li; Jing Liang; Li Yan; Kunjie Yu; Yongsheng Zhu. A self-organized speciation based multi-objective particle swarm optimizer for multimodal multi-objective problems. Applied Soft Computing 2019, 86, 105886 .
AMA StyleBoyang Qu, Chao Li, Jing Liang, Li Yan, Kunjie Yu, Yongsheng Zhu. A self-organized speciation based multi-objective particle swarm optimizer for multimodal multi-objective problems. Applied Soft Computing. 2019; 86 ():105886.
Chicago/Turabian StyleBoyang Qu; Chao Li; Jing Liang; Li Yan; Kunjie Yu; Yongsheng Zhu. 2019. "A self-organized speciation based multi-objective particle swarm optimizer for multimodal multi-objective problems." Applied Soft Computing 86, no. : 105886.
Yanli Wang; Boyang Qu; Jing Liang; Yunpeng Wei; Caitong Yue; Yi Hu; Hui Song. Two-Stage Decomposition Method Based on Cooperation Coevolution for Feature Selection on High-Dimensional Classification. IEEE Access 2019, 7, 163191 -163201.
AMA StyleYanli Wang, Boyang Qu, Jing Liang, Yunpeng Wei, Caitong Yue, Yi Hu, Hui Song. Two-Stage Decomposition Method Based on Cooperation Coevolution for Feature Selection on High-Dimensional Classification. IEEE Access. 2019; 7 ():163191-163201.
Chicago/Turabian StyleYanli Wang; Boyang Qu; Jing Liang; Yunpeng Wei; Caitong Yue; Yi Hu; Hui Song. 2019. "Two-Stage Decomposition Method Based on Cooperation Coevolution for Feature Selection on High-Dimensional Classification." IEEE Access 7, no. : 163191-163201.
Zhongwen Li; Zhiping Cheng; Jing Liang; Jikai Si; Lianghui Dong; Shuhui Li. Distributed Event-Triggered Secondary Control for Economic Dispatch and Frequency Restoration Control of Droop-Controlled AC Microgrids. IEEE Transactions on Sustainable Energy 2019, 11, 1938 -1950.
AMA StyleZhongwen Li, Zhiping Cheng, Jing Liang, Jikai Si, Lianghui Dong, Shuhui Li. Distributed Event-Triggered Secondary Control for Economic Dispatch and Frequency Restoration Control of Droop-Controlled AC Microgrids. IEEE Transactions on Sustainable Energy. 2019; 11 (3):1938-1950.
Chicago/Turabian StyleZhongwen Li; Zhiping Cheng; Jing Liang; Jikai Si; Lianghui Dong; Shuhui Li. 2019. "Distributed Event-Triggered Secondary Control for Economic Dispatch and Frequency Restoration Control of Droop-Controlled AC Microgrids." IEEE Transactions on Sustainable Energy 11, no. 3: 1938-1950.
Sparsity and reconstruction error are two main objectives to be optimized in sparse signal reconstruction. In this paper, sparse signals are reconstructed by optimizing these two objectives simultaneously. This reconstruction method mainly consists of three steps. First, a one-dimension-dominated method is used to find a uniformly distributed optimal compromise solution set between these two objectives. Second, the Iterative Half Thresholding method is employed to improve the sparsity. Third, a robust selection method is proposed to choose a final solution from the solution set. The proposed method is compared with eight sparse reconstruction algorithms on twelve sparse test instances. Experimental results show that the proposed algorithm is able to reconstruct both noisy and noiseless sparse signals. In addition, the effectiveness of the proposed algorithm is demonstrated in practical application instances.
Caitong Yue; Jing Liang; Boyang Qu; Yuhong Han; Yongsheng Zhu; Oscar D. Crisalle. A novel multiobjective optimization algorithm for sparse signal reconstruction. Signal Processing 2019, 167, 107292 .
AMA StyleCaitong Yue, Jing Liang, Boyang Qu, Yuhong Han, Yongsheng Zhu, Oscar D. Crisalle. A novel multiobjective optimization algorithm for sparse signal reconstruction. Signal Processing. 2019; 167 ():107292.
Chicago/Turabian StyleCaitong Yue; Jing Liang; Boyang Qu; Yuhong Han; Yongsheng Zhu; Oscar D. Crisalle. 2019. "A novel multiobjective optimization algorithm for sparse signal reconstruction." Signal Processing 167, no. : 107292.
Google Ads is an advertising agency that provides ads to advertisers. Advertisers match the user’s search terms and push ads by selecting keywords related to their ad content. Keywords can determine the type of users an advertiser pushes, the effectiveness of the ad promotion, and the sales of the ad product. Automatically selecting keywords that are satisfactory to advertisers from a large number of keywords provided by Google Ads is the main task of this paper. But there is not too much time for the model to judge whether keywords are selected, choosing correct keywords in the shortest time is another task of this paper. Therefore, a structure of the model that can get some useful keywords for advertisers is designed and an improved multi-objective particle swarm optimization algorithm is proposed to achieve this multiobjective task. These are also the main contributions of this paper. To accomplish this multi-objective task, many technical issues need to be overcome, such as the mixed language problem, the imbalance problem, the problem of extracting features from corpora and so on. This paper proposes a corpus selection method to solve the mixed problem of Chinese and English in keywords, word embedding method to solve the representation of keywords, re-sampling to solve data imbalance problem, improved convolutional neural network (CNN) to solve classification problem, and a multi-objective particle swarm optimization algorithm (MOPSO) to achieve neural structure search of CNN so that the effect of the classification is improved and the training time is reduced. The keyword selection problem is solved with the combination of evolutionary computing, deep learning, machine learning, and text processing techniques. Experimental results show that the proposed algorithm greatly improved the accuracy of keyword selection and shortened the time of selecting keywords. Therefore, this algorithm has a good application value.
Jing Liang; Haotian Yang; Jiajia Gao; Caitong Yue; Shilei Ge; Boyang Qu. MOPSO-Based CNN for Keyword Selection on Google Ads. IEEE Access 2019, 7, 125387 -125400.
AMA StyleJing Liang, Haotian Yang, Jiajia Gao, Caitong Yue, Shilei Ge, Boyang Qu. MOPSO-Based CNN for Keyword Selection on Google Ads. IEEE Access. 2019; 7 (99):125387-125400.
Chicago/Turabian StyleJing Liang; Haotian Yang; Jiajia Gao; Caitong Yue; Shilei Ge; Boyang Qu. 2019. "MOPSO-Based CNN for Keyword Selection on Google Ads." IEEE Access 7, no. 99: 125387-125400.
In this paper, a self-adaptive differential evolution (DE) algorithm is designed to solve multi-objective flow shop scheduling problems with limited buffers (FSSPwLB). The makespan and the largest job delay are treated as two separate objectives which are optimized simultaneously. To improve the performance of the proposed algorithm and eliminate the difficulty of setting parameters, an adaptive mechanism is designed and incorporated into DE. Moreover, various local search and hybrid meta-heuristic methods are presented and compared to improve the convergence. Through the analysis of the experimental results, the proposed algorithm is able to tackle the FSSPwLB problems effectively by generating superior and stable scheduling strategies.
Jing Liang; Peng Wang; Li Guo; Boyang Qu; Caitong Yue; Kunjie Yu; Yachao Wang. Multi-objective flow shop scheduling with limited buffers using hybrid self-adaptive differential evolution. Memetic Computing 2019, 11, 407 -422.
AMA StyleJing Liang, Peng Wang, Li Guo, Boyang Qu, Caitong Yue, Kunjie Yu, Yachao Wang. Multi-objective flow shop scheduling with limited buffers using hybrid self-adaptive differential evolution. Memetic Computing. 2019; 11 (4):407-422.
Chicago/Turabian StyleJing Liang; Peng Wang; Li Guo; Boyang Qu; Caitong Yue; Kunjie Yu; Yachao Wang. 2019. "Multi-objective flow shop scheduling with limited buffers using hybrid self-adaptive differential evolution." Memetic Computing 11, no. 4: 407-422.