Dunwei Gong

China University of Mining Technology, Suchow, Jiangsu Sheng, China

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Publications (73)32.97 Total impact

  • Dunwei Gong, Jian Chen, Xiaoyan Sun, Jing Sun
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    ABSTRACT: User fatigue significantly restricts the practical applications of interactive genetic algorithms for complicated optimization problems. To alleviate user fatigue, an interactive genetic algorithm is presented in this study, where individuals are evaluated with varying accuracy. In the developed algorithm, multiple language sets with different granularities are proposed and applied to evaluate individuals. A subset of the whole language sets is chosen first to evaluate a population, adaptively to the convergence of the current population. For an individual in the current population, an appropriate language set is chosen from the subset to evaluate it according to the distance between the individual and the user’s preferred region. The proposed algorithm is compared with some other algorithms in literature on curtain design. Empirical results demonstrate that the developed algorithm can significantly alleviate user fatigue and improve search efficiency.
    Soft Computing 03/2015; 19(3). DOI:10.1007/s00500-014-1285-x · 1.30 Impact Factor
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    ABSTRACT: Multi-objective particle swarm optimization (MOPSO) has been well studied in recent years. However, existing MOPSO methods are not powerful enough when tackling optimization problems with more than three objectives, termed as many-objective optimization problems (MaOPs). In this study, an improved set evolution multi-objective particle swarm optimization (S-MOPSO, for short) is proposed for solving many-objective problems. According to the proposed framework of set evolution MOPSO (S-MOPSO), including quality indicators-based objective transformation, the Pareto dominance on sets, and the particle swarm operators for set evolution, an enhanced S-MOPSO method is developed by updating particles hierarchically, i.e., a set of solutions is first regarded as a particle to be updated and then the solutions in a selected set are further evolved by a modified PSO. In the set evolutionary stage, the strategy for efficiently updating the set particle is proposed. When further evolving a single solution in the initial decision space of the optimized MaOP, the global and local best particles are dynamically determined based on those ideal reference points. The performance of the proposed algorithm is empirically demonstrated by applying it to several scalable benchmark many-objective problems.
    Soft Computing 01/2015; DOI:10.1007/s00500-015-1637-1 · 1.30 Impact Factor
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    ABSTRACT: Feature selection is a useful pre-processing technique for solving classification problems. As an almost parameter-free optimization algorithm, the bare bones particle swarm optimization (BPSO) has been applied to the topic of optimization on continuous or integer spaces, but it has not been applied to feature selection problems with binary variables. In this paper, we propose a new method to find optimal feature subset by the BPSO, called the binary BPSO. In this algorithm, a reinforced memory strategy is designed to update the local leaders of particles for avoiding the degradation of outstanding genes in the particles, and a uniform combination is proposed to balance the local exploitation and the global exploration of algorithm. Moreover, the 1-nearest neighbor method is used as a classifier to evaluate the classification accuracy of a particle. Some international standard data sets are selected to evaluate the proposed algorithm. The experiments show that the proposed algorithm is competitive in terms of both classification accuracy and computational performance.
    Neurocomputing 01/2015; 148:150–157. DOI:10.1016/j.neucom.2012.09.049 · 2.01 Impact Factor
  • Yu-Yan Han, Dunwei Gong, Xiaoyan Sun
    Engineering Optimization 12/2014; DOI:10.1080/0305215X.2014.928817 · 1.23 Impact Factor
  • Xiangjuan Yao, Dunwei Gong, Yali Gu
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    ABSTRACT: Research on complex networks is becoming a very hot topic in recent years, among which node matching problem is an important issue. The aim of node matching problem is to find out the corresponding relations between the individuals of associated networks. Traditional node matching problem of networks always hypothesize that a proportion of matching nodes are known. However, if the ratio of matched nodes is very small, the matching accuracy of the remaining nodes cannot be evaluated accurately. What is more, we may have not any matched nodes for reference at all. In view of this, this paper established the mathematic model of node matching problem based on the adjacency matrixes of networks, and presented an evolutionary algorithm to solve it. The experimental results show that the proposed method can achieve satisfactory matching precision in the absence of any matched nodes.
    Physica A: Statistical Mechanics and its Applications 11/2014; 416. DOI:10.1016/j.physa.2014.08.070 · 1.72 Impact Factor
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    ABSTRACT: Interval multi-objective optimization problems (IMOPs), whose parameters are intervals, are considerably ubiquitous in real-world applications. Previous evolutionary algorithms (EAs) aim at finding the well-converged and evenly-distributed Pareto front. An EA incorporating with a decision-maker (DM)׳s preferences was presented in this study to obtain a Pareto-optimal subset that meets the DM׳s preferences. In this algorithm, the DM׳s preferences in terms of the relative importance of objectives were interactively input, and the corresponding preferred regions were then obtained. Based on these regions, solutions with the same rank were further distinguished to guide the search towards the DM׳s preferred region. The proposed method was empirically evaluated on four IMOPs and compared with other state-of-the-art methods. The experimental results demonstrated the simplicity and the effectiveness of the proposed method.
    Neurocomputing 08/2014; 137:241–251. DOI:10.1016/j.neucom.2013.04.052 · 2.01 Impact Factor
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    ABSTRACT: Many-objective optimization problems are common in real-world applications, few evolutionary optimization methods, however, are suitable for them up to date due to their difficulties. We proposed a reference points-based evolutionary algorithm (RPEA) to solve many-objective optimization problems in this study. In RPEA, a series of reference points with good performances in convergence and distribution are generated according to the current population to guide the evolution. Furthermore, superior individuals are selected based on the assessment of each individual by calculating the distances between the reference points and the individual in the objective space. The algorithm was applied to four benchmark optimization problems and compared with NSGA-II and HypE. The results experimentally demonstrate that the algorithm is strengthened in obtaining Pareto optimal set with high performances.
  • Jianhua Zhang, Dunwei Gong, Yong Zhang
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    ABSTRACT: Aiming at the problem of multiple odor sources localization, a multi-robot cooperation method based on niching particle swarm optimization is presented in this study. In this method, a robot is regarded as a particle, particles located at a neighbor form a niche, and different niches are employed to localize different odor sources synchronously. In order to localize more odor sources, the size of each niche is dynamically adjusted based on the aggregation degree of its elements. A niche merging strategy, based on the similarity of optimal particles found by niches, is proposed to prevent particles repeatedly searching for the same region. In addition, some real conditions such as the sampling/recovery time of a sensor and the velocity limit of a robot are considered when updating the position of a particle. Finally, the proposed method is applied to various scenarios of localizing multiple odor sources, and the experimental results confirm its effectiveness.
    Neurocomputing 01/2014; 123:308-317. DOI:10.1016/j.neucom.2013.07.025 · 2.01 Impact Factor
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    Xiangjuan Yao, Dunwei Gong
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    ABSTRACT: The application of genetic algorithms in automatically generating test data has aroused broad concerns and obtained delightful achievements in recent years. However, the efficiency of genetic algorithm-based test data generation for path testing needs to be further improved. In this paper, we establish a mathematical model of generating test data for multiple paths coverage. Then, a multipopulation genetic algorithm with individual sharing is presented to solve the established model. We not only analyzed the performance of the proposed method theoretically, but also applied it to various programs under test. The experimental results show that the proposed method can improve the efficiency of generating test data for many paths' coverage significantly.
    Computational Intelligence and Neuroscience 01/2014; 2014:591294. DOI:10.1155/2014/591294
  • Dunwei Gong, Yan Zhang
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    ABSTRACT: The aim of software testing is to find faults in a program under test, so generating test data that can expose the faults of a program is very important. To date, current studies on generating test data for path coverage do not perform well in detecting low probability faults on the covered path. The automatic generation of test data for both path coverage and fault detection using genetic algorithms is the focus of this study. To this end, the problem is first formulated as a bi-objective optimization problem with one constraint whose objectives are the number of faults detected in the traversed path and the risk level of these faults, and whose constraint is that the traversed path must be the target path. An evolutionary algorithmis employed to solve the formulatedmodel, and several types of fault detectionmethods are given. Finally, the proposed method is applied to several real-world programs, and compared with a random method and evolutionary optimization method in the following three aspects: the number of generations and the time consumption needed to generate desired test data, and the success rate of detecting faults. The experimental results confirm that the proposed method can effectively generate test data that not only traverse the target path but also detect faults lying in it.
    Frontiers of Computer Science (print) 12/2013; 7(6):822-837. DOI:10.1007/s11704-013-3024-3 · 0.41 Impact Factor
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    ABSTRACT: Interactive genetic algorithm (IGA), combining a user's intelligent evaluation with the traditional operators of genetic algorithms, are developed to optimize those problems with aesthetic indicators. The evaluation uncertainties and burden, however, greatly restrict the applications of IGA in complicated situations. Surrogate model approximating to the evaluation of the user has been generally applied to alleviate the evaluation burden of the user. The evaluation uncertainties, however, are not taken into account in existing research, therefore, a weighted multi-output gaussian process is here proposed to build the surrogate model by incorporating the uncertainty so as to enhance the performance of IGA. First, an IGA with interval fitness evaluation is adopted to depict the evaluation uncertainty, and the evaluation noise is defined based on the assignment. With the evaluation noise, the weight of each training sample is calculated and used to train a gaussian process which has two outputs to approximate the upper and lower values of the interval fitness, respectively. The trained gaussian process is treated as a fitness function and used to estimate the fitness of individuals generated in the subsequent evolutions. The proposed algorithm is applied to a benchmark function and a real-world fashion design to experimentally demonstrate its strength in searching.
    Computational Intelligence in Dynamic and Uncertain Environments (CIDUE), 2013 IEEE Symposium on; 01/2013
  • Dunwei Gong, Gengxing Wang, Xiaoyan Sun
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    ABSTRACT: Many-objective optimization problems are very common and important in real-world applications, and there exist few methods suitable for them. Therefore, many-objective optimization problems are focused on in this study, and a set-based genetic algorithm is presented to effectively solve them. First, each objective of the original optimization problem is transformed into a desirability function according to the preferred region defined by the decision-maker. Thereafter, the transformed problem is further converted to a bi-objective optimization one by taking hyper-volume and the decision-maker's satisfaction as the new objectives, and a set of solutions of the original optimization problem as the new decision variable. To tackle the converted bi-objective optimization problem by using genetic algorithms, the crossover operator inside a set is designed based on the simplex method by using solutions of the original optimization problem, and the crossover operator between sets is developed by using the entropy of sets. In addition, the mutation operator of a set is presented to obey the Gaussian distribution and change along with the decision-maker's preferences. The proposed method is applied to five benchmark many-objective optimization problems, and compared with other six methods. The experimental results empirically demonstrate its effectiveness.
    Computational Intelligence (UKCI), 2013 13th UK Workshop on; 01/2013
  • Na Geng, Dunwei Gong, Yong Zhang
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    ABSTRACT: In order to solve the problem of path planning in an environment with many terrains, we propose a method based on interval multi-objective Particle Swarm Optimization (PSO). First, the environment is modeled by the line partition method, and then, according to the distribution of the polygonal lines which form the robot path and taking the velocity's disturbance into consideration, robot's passing time is formulated as an interval by combining Local Optimal Criterion (LOC), and the path's danger degree is estimated through the area ratio between the robot path and the danger source. In addition, the path length is also calculated as an optimization objective. As a result, the robot path planning problem is modeled as an optimization problem with three objectives. Finally, the interval multiobjective PSO is employed to solve the problem above. Simulation and experimental results verify the effectiveness of the proposed method.
    Evolutionary Computation (CEC), 2013 IEEE Congress on; 01/2013
  • Dunwei Gong, Xinfang Ji
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    ABSTRACT: Multi-objective optimization problems with interval parameters are ubiquitous and important, yet not many effective methods are available for solving them. To solve these problems, we propose a set-based evolutionary algorithm incorporated with decision-maker (DM)’s preferences to obtain a Pareto solution set which satisfies DM’s preferences. In this algorithm, the original optimization problem is first transformed into a tri-objective deterministic optimization problem with three performance indicators: hyper-volume, uncertainty and DM satisfaction. To solve the transformed problem, we employ a set-based Pareto dominance relation to compare different individuals. Individuals with the same rank are distinguished by using a specially designed extension measure incorporating DM’s preferences. Additionally, a set-based mutation and recombination scheme is suggested to generate an offspring with high performance. Four benchmark multiobjective optimization problems and a car cab design problem are used to evaluate the proposed method. Results are compared with those from other three methods. Conclusions indicate that the proposed method can obtain a Pareto solution set with a desirable compromise among the convergence, extension, uncertainty and the DM’s satisfaction.
    Kongzhi Lilun Yu Yinyong/Control Theory and Applications 01/2013; 30(11). DOI:10.7641/CTA.2013.21178
  • Jing Sun, Dunwei Gong, Xinfang Ji
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    ABSTRACT: Interval multi-objective optimization problems are ubiquitous and important in real-world applications. An interactive evolutionary algorithm incorporating an optimization-cum-decision-making procedure is presented to obtain the most preferred solution that fits a decision-maker (DM)’s preferences. In this algorithm, a preference direction is elicited by requesting the DM to select the worst one from a part of non-dominated solutions. A metric based on the above direction, which reflects the approximation performance of a candidate solution, is designed to rank different solutions with the same rank and preference. The proposed method is applied to four interval bi-objective optimization problems, and compared with PPIMOEA as well as an a posteriori method. The experimental results show the effectiveness and high efficiency of the proposed method.
    Kongzhi yu Juece/Control and Decision 01/2013; 28(4).
  • Dunwei Gong, Jian Chen, Xiaoyan Sun
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    ABSTRACT: This paper proposes a novel interactive genetic algorithm. In this algorithm, the weight of each gene meaning unit is calculated based on the emergence frequency of the corresponding allele. By comparing the weights, we determine the similarity of two individuals. Individuals in the current generation are selected for evaluation by the user according to the similarities between them and the most preferred one in the former generation. The fitness of unevaluated individuals is estimated based on the information of all evaluated individuals in the current generation. The proposed algorithm is applied to a curtain evolutionary design system, and compared with existing typical methods. The experimental results validate that the proposed algorithm has advantages in reducing the user’s fatigue and improving the efficiency in exploration.
    Kongzhi Lilun Yu Yinyong/Control Theory and Applications 01/2013; 30(5). DOI:10.7641/CTA.2013.21164
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    ABSTRACT: An interactive genetic algorithm with evaluating individuals using variational granularity was presented in this study to effectively alleviate user fatigue. In this algorithm, multiple language sets with different evaluation granularities are provided. The diversity of a population described with the entropy of its gene meaning units is utilized to first choose parts of appropriate language sets to participate in evaluating the population. A specific language set for evaluating an individual is further selected from these sets according to the distance between the individual and the current preferred one. The proposed algorithm was applied to a curtain evolutionary design system and compared with previous typical ones. The empirical results demonstrate the strengths of the proposed algorithm in both alleviating user fatigue and improving the efficiency in search.
    Proceedings of the 19th international conference on Neural Information Processing - Volume Part III; 11/2012
  • Dunwei Gong, Tian Tian, Xiangjuan Yao
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    ABSTRACT: Generating test data covering multiple paths using multi-population parallel genetic algorithms is a considerable important method. The premise on which the method above is efficient is appropriately grouping target paths. Effective methods of grouping target paths, however, have been absent up to date. The problem of grouping target paths for generation of test data covering multiple paths is investigated, and a novel method of grouping target paths is presented. In this method, target paths are divided into several groups according to calculation resources available and similarities among target paths, making a small difference in the number of target paths belonging to different groups, and a great similarity among target paths in the same group. After grouping these target paths, a mathematical model is built for parallel generation of test data covering multiple paths, and a multi-population genetic algorithm is adopted to solve the model above. The proposed method is applied to several benchmark or industrial programs, and compared with a previous method. The experimental results show that the proposed method can make full use of calculation resources on the premise of meeting the requirement of path coverage, improving the efficiency of generating test data.
    Journal of Systems and Software 11/2012; 85(11):2531–2540. DOI:10.1016/j.jss.2012.05.071 · 1.25 Impact Factor
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    ABSTRACT: Surrogate-assisted interactive genetic algorithms (IGAs) are found to be very effective in reducing human fatigue. Different from models used in most surrogate-assisted evolutionary algorithms, surrogates in IGA must be able to handle the inherent uncertainties in fitness assignment by human users, where, e.g., interval-based fitness values are assigned to individuals. This poses another challenge to using surrogates for fitness approximation in evolutionary optimization, in addition to the lack of training data. In this paper, a new surrogate-assisted IGA has been proposed, where the uncertainty in subjective fitness evaluations is exploited both in training the surrogates and in managing surrogates. To enhance the approximation accuracy of the surrogates, an improved cotraining algorithm for semisupervised learning has been suggested, where the uncertainty in interval-based fitness values is taken into account in training and weighting the two cotrained models. Moreover, uncertainty in the interval-based fitness values is also considered in model management so that not only the best individuals but also the most uncertain individuals will be chosen to be re-evaluated by the human user. The effectiveness of the proposed algorithm is verified on two test problems as well as in fashion design, a typical application of IGA. Our results indicate that the new surrogate-assisted IGA can effectively alleviate user fatigue and is more likely to find acceptable solutions in solving complex design problems.
    IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics: a publication of the IEEE Systems, Man, and Cybernetics Society 09/2012; DOI:10.1109/TSMCB.2012.2214382 · 3.01 Impact Factor
  • Xiaoyan Sun, Dunwei Gong, Wei Zhang
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    ABSTRACT: Interactive genetic algorithms are effective methods of solving optimization problems with implicit (qualitative) criteria by incorporating a user's intelligent evaluation into traditional evolution mechanisms. The heavy evaluation burden of the user, however, is crucial and limits their applications in complex optimization problems. We focus on reducing the evaluation burden by presenting a semi-supervised learning assisted interactive genetic algorithm with large population. In this algorithm, a population with many individuals is adopted to efficiently explore the search space. A surrogate model built with an improved semi-supervised learning method is employed to evaluate a part of individuals instead of the user to alleviate his/her burden in evaluation. Incorporated with the principles of the improved semi-supervised learning, the opportunities of applying and updating the surrogate model are determined by its confidence degree in estimation, and the informative individuals reevaluated by the user are selected according to the concept of learning from mistakes. We quantitatively analyze the performance of the proposed algorithm and apply it to the design of sunglasses lenses, a representative optimization problem with one qualitative criterion. The empirical results demonstrate the strength of our algorithm in searching for satisfactory solutions and easing the evaluation burden of the user.
    Applied Soft Computing 09/2012; 12(9):3004–3013. DOI:10.1016/j.asoc.2012.04.021 · 2.68 Impact Factor