12 results on '"Cai, Xinye"'
Search Results
2. An adaptive memetic framework for multi-objective combinatorial optimization problems: studies on software next release and travelling salesman problems.
- Author
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Cai, Xinye, Cheng, Xin, Fan, Zhun, Goodman, Erik, and Wang, Lisong
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COMBINATORIAL optimization , *STOCHASTIC convergence , *MEMETICS , *MATHEMATICAL decomposition , *COMPUTER algorithms - Abstract
In this paper, we propose two multi-objective memetic algorithms (MOMAs) using two different adaptive mechanisms to address combinatorial optimization problems (COPs). One mechanism adaptively selects solutions for local search based on the solutions' convergence toward the Pareto front. The second adaptive mechanism uses the convergence and diversity information of an external set (dominance archive), to guide the selection of promising solutions for local search. In addition, simulated annealing is integrated in this framework as the local refinement process. The multi-objective memetic algorithms with the two adaptive schemes (called uMOMA-SA and aMOMA-SA) are tested on two COPs and compared with some well-known multi-objective evolutionary algorithms. Experimental results suggest that uMOMA-SA and aMOMA-SA outperform the other algorithms with which they are compared. The effects of the two adaptive mechanisms are also investigated in the paper. In addition, uMOMA-SA and aMOMA-SA are compared with three single-objective and three multi-objective optimization approaches on software next release problems using real instances mined from bug repositories (Xuan et al. IEEE Trans Softw Eng 38(5):1195-1212, 2012). The results show that these multi-objective optimization approaches perform better than these single-objective ones, in general, and that aMOMA-SA has the best performance among all the approaches compared. [ABSTRACT FROM AUTHOR]
- Published
- 2017
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3. A diversity indicator based on reference vectors for many-objective optimization.
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Cai, Xinye, Sun, Haoran, and Fan, Zhun
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MULTIDISCIPLINARY design optimization , *APPROXIMATION theory , *EVOLUTIONARY computation , *ESTIMATION theory , *ALGORITHMS , *ONLINE data processing - Abstract
Diversity estimation of Pareto front (PF) approximations is a critical issue in the field of evolutionary multiobjective optimization. However, the existing diversity indicators are usually inappropriate for PF approximations with more than three objectives. Many of them can be utilized only when compared with approximations obtained by multiple multiobjective optimizers, which makes them difficult to use online. In this paper, we propose a unary diversity indicator based on reference vectors (DIR) to estimate the diversity of PF approximations for many-objective optimization. In DIR, a set of uniform and widespread reference vectors are generated. The coverage of each solution in the objective space is evaluated by the number of representative reference vectors it is associated with. The diversity (both spread and uniformity) is determined by the standard deviation of the coverage for all the solutions. The smaller value of DIR, the better the diversity of a PF approximation is. DIR can be applied to a unary approximation without any compared approximations needed. Thus, DIR is easy to use as either an offline indicator to estimate the performance of an optimizer or an online indicator for the selection of solutions in a MOEA. In the experimental studies, both the artificial and the real PF approximations generated by seven different many-objective algorithms are used to verify DIR as an offline indicator. The effects of the number of reference vectors on DIR are also investigated. In addition, as an online indicator, DIR is integrated into a Pareto-dominance-based evolutionary multiobjective optimizer, NSGA-II. The experimental studies show it has the significant performance enhancements over the original NSGA-II on many-objective optimization problems. [ABSTRACT FROM AUTHOR]
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- 2018
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4. An improved memetic algorithm using ring neighborhood topology for constrained optimization.
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Hu, Zhenzhou, Cai, Xinye, and Fan, Zhun
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MEMETICS , *ALGORITHMS , *MATHEMATICAL models , *MATHEMATICAL optimization , *TOPOLOGY , *NOXIOUS weeds - Abstract
This paper proposes an improved memetic algorithm relying on ring neighborhood topology for constrained optimization problems based on our previous work in Cai et al. (Soft Comput (in press), ). The main motivation of using ring neighborhood topology is to provide a good balance between effective exploration and efficient exploitation, which is a very important design issue for memetic algorithms. More specifically, a novel variant of invasive weed optimization (IWO) as the local refinement procedure is proposed in this paper. The proposed IWO variant adopts a neighborhood-based dispersal operator to achieve more fine-grained local search through the estimation of neighborhood fitness information relying on the ring neighborhood topology. Furthermore, a modified version of differential evolution (DE), known as 'DE/current-to-best/1', is integrated into the improved memetic algorithm with the aim of providing a more effective exploration. Performance of the improved memetic algorithm has been comprehensively tested on 13 well-known benchmark test functions and four engineering constrained optimization problems. The experimental results show that the improved memetic algorithm obtains greater competitiveness when compared with the original memetic approach Cai et al. in (Soft Comput (in press), ) and other state-of-the-art algorithms. The effectiveness of the modification of each component in the proposed approach is also discussed in the paper. [ABSTRACT FROM AUTHOR]
- Published
- 2014
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5. Fuzzy clustering optimal k selection method based on multi-objective optimization.
- Author
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Wang, Lisong, Cui, Guonan, and Cai, Xinye
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MATHEMATICAL optimization , *FUZZY algorithms , *EVOLUTIONARY algorithms , *FUZZY numbers , *SEARCH algorithms - Abstract
Because of the complexity of data sets from the real world, it is difficult to classify the data sets clearly and effectively, thus we prefer to adopt fuzzy clustering approaches to analyze the data sets. However, due to the variety of fuzzy clustering algorithms, the different number of clusters will lead to different clustering results. The number of clusters is closely related to the clustering division, so how to determine the number of fuzzy clustering (k) has become a problem. Until now, many researchers have proposed utilizing fuzzy clustering validity indexes to deal with this kind of problem. However, the effectiveness index of fuzzy clustering can only be evaluated on the basis of the fuzzy clustering algorithm FCM to divide the clusters. When the range of k value is too large, FCM's clustering for different k values is quite time-consuming. From this perspective, this paper proposes a fuzzy clustering optimal k selection method based on multi-objective optimization (FMOEA-K). Different from the traditional methods, this method combines the fuzzy clustering effectiveness index with multi-objective optimization algorithm (MOEA), and uses multi-objective optimization algorithm to search the appropriate cluster center concurrently. Because of the concurrency of the multi-objective optimization algorithm, the calculation time is shortened. The experimental results show that compared with the traditional method, the FMOEA-K can shorten the calculation time and improve the accuracy of calculating the optimal k value. [ABSTRACT FROM AUTHOR]
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- 2023
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6. A novel memetic algorithm based on invasive weed optimization and differential evolution for constrained optimization.
- Author
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Cai, Xinye, Hu, Zhenzhou, and Fan, Zhun
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MEMETICS , *COMPUTER algorithms , *COMPUTER programming , *DIFFERENTIAL evolution , *CONSTRAINED optimization , *POLYNOMIAL time algorithms - Abstract
This paper presents a novel memetic algorithm, named as IWO_DE, to tackle constrained numerical and engineering optimization problems. In the proposed method, invasive weed optimization (IWO), which possesses the characteristics of adaptation required in memetic algorithm, is firstly considered as a local refinement procedure to adaptively exploit local regions around solutions with high fitness. On the other hand, differential evolution (DE) is introduced as the global search model to explore more promising global area. To accommodate the hybrid method with the task of constrained optimization, an adaptive weighted sum fitness assignment and polynomial distribution are adopted for the reproduction and the local dispersal process of IWO, respectively. The efficiency and effectiveness of the proposed approach are tested on 13 well-known benchmark test functions. Besides, our proposed IWO_DE is applied to four well-known engineering optimization problems. Experimental results suggest that IWO_DE can successfully achieve optimal results and is very competitive compared with other state-of-art algorithms. [ABSTRACT FROM AUTHOR]
- Published
- 2013
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7. Noisy Optimization by Evolution Strategies With Online Population Size Learning.
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Li, Zhenhua, Zhang, Shuo, Cai, Xinye, Zhang, Qingfu, Zhu, Xiaomin, Fan, Zhun, and Jia, Xiuyi
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RANDOM noise theory , *PERFORMANCE standards , *COVARIANCE matrices , *MATHEMATICAL optimization , *NOISE measurement - Abstract
Optimization modeling of real-world application problems usually involves noise from various sources. Noisy optimization imposes challenges to optimization methods since the objective values can be different for multiple evaluations. In this article, we propose a novel online population size learning (OPL) technique of evolution strategies for handling noisy optimization problems. By re-evaluating a fraction of the candidates, we measure the strength of noise level of the re-evaluated candidate solutions and adapt the population size according to the noise level. The proposed OPL combines the advantages of both explicit averaging by re-evaluations and the implicit averaging by large population size and overcomes their limitations. We incorporate it with the covariance matrix adaptation evolution strategy (CMA-ES) and obtain OPL-CMA-ES. Compared with the existing noise handling technique, the proposed OPL is much simpler in both concepts and computation. We conduct comprehensive experiments to evaluate the algorithm’s performance on standard problems with Gaussian noise. We further evaluate the performance of OPL-CMA-ES on the black-box optimization benchmarks (BBOBs) noisy testbed, which is a standard platform for comparing black-box optimization algorithms, compared with the state-of-the-art noise-handling algorithms. The experimental results show that OPL-CMA-ES achieves remarkable performance and outperforms the compared variants. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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8. Difficulty Adjustable and Scalable Constrained Multiobjective Test Problem Toolkit.
- Author
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Fan, Zhun, Li, Wenji, Cai, Xinye, Li, Hui, Wei, Caimin, Zhang, Qingfu, Deb, Kalyanmoy, and Goodman, Erik
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CONSTRAINED optimization , *STOCHASTIC dominance - Abstract
Multiobjective evolutionary algorithms (MOEAs) have progressed significantly in recent decades, but most of them are designed to solve unconstrained multiobjective optimization problems. In fact, many real-world multiobjective problems contain a number of constraints. To promote research on constrained multiobjective optimization, we first propose a problem classification scheme with three primary types of difficulty, which reflect various types of challenges presented by real-world optimization problems, in order to characterize the constraint functions in constrained multiobjective optimization problems (CMOPs). These are feasibility-hardness, convergence-hardness, and diversity-hardness. We then develop a general toolkit to construct difficulty adjustable and scalable CMOPs (DAS-CMOPs, or DAS-CMaOPs when the number of objectives is greater than three) with three types of parameterized constraint functions developed to capture the three proposed types of difficulty. In fact, the combination of the three primary constraint functions with different parameters allows the construction of a large variety of CMOPs, with difficulty that can be defined by a triplet, with each of its parameters specifying the level of one of the types of primary difficulty. Furthermore, the number of objectives in this toolkit can be scaled beyond three. Based on this toolkit, we suggest nine difficulty adjustable and scalable CMOPs and nine CMaOPs, to be called DAS-CMOP1-9 and DAS-CMaOP1-9, respectively. To evaluate the proposed test problems, two popular CMOEAs—MOEA/D-CDP (MOEA/D with constraint dominance principle) and NSGA-II-CDP (NSGA-II with constraint dominance principle) and two popular constrained many-objective evolutionary algorithms (CMaOEAs)—C-MOEA/DD and C-NSGA-III—are used to compare performance on DAS-CMOP1-9 and DAS-CMaOP1-9 with a variety of difficulty triplets, respectively. The experimental results reveal that mechanisms in MOEA/D-CDP may be more effective in solving convergence-hard DAS-CMOPs, while mechanisms of NSGA-II-CDP may be more effective in solving DAS-CMOPs with simultaneous diversity-, feasibility-, and convergence-hardness. Mechanisms in C-NSGA-III may be more effective in solving feasibility-hard CMaOPs, while mechanisms of C-MOEA/DD may be more effective in solving CMaOPs with convergence-hardness. In addition, none of them can solve these problems efficiently, which stimulates us to continue to develop new CMOEAs and CMaOEAs to solve the suggested DAS-CMOPs and DAS-CMaOPs. [ABSTRACT FROM AUTHOR]
- Published
- 2020
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9. An improved epsilon constraint-handling method in MOEA/D for CMOPs with large infeasible regions.
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Fan, Zhun, Li, Wenji, Cai, Xinye, Huang, Han, Fang, Yi, You, Yugen, Mo, Jiajie, Wei, Caimin, and Goodman, Erik
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CONSTRAINED optimization , *TEST interpretation - Abstract
This paper proposes an improved epsilon constraint-handling mechanism and combines it with a decomposition-based multi-objective evolutionary algorithm (MOEA/D) to solve constrained multi-objective optimization problems (CMOPs). The proposed constrained multi-objective evolutionary algorithm (CMOEA) is named MOEA/D-IEpsilon. It adjusts the epsilon level dynamically according to the ratio of feasible to total solutions in the current population. In order to evaluate the performance of MOEA/D-IEpsilon, a new set of CMOPs with two and three objectives is designed, having large infeasible regions (relative to the feasible regions), and they are called LIR-CMOPs. Then, the 14 benchmarks, including LIR-CMOP1-14, are used to test MOEA/D-IEpsilon and four other decomposition-based CMOEAs, including MOEA/D-Epsilon, MOEA/D-SR, MOEA/D-CDP and CMOEA/D. The experimental results indicate that MOEA/D-IEpsilon is significantly better than the other four CMOEAs on all of the test instances, which shows that MOEA/D-IEpsilon is more suitable for solving CMOPs with large infeasible regions. Furthermore, a real-world problem, namely the robot gripper optimization problem, is used to test the five CMOEAs. The experimental results demonstrate that MOEA/D-IEpsilon also outperforms the other four CMOEAs on this problem. [ABSTRACT FROM AUTHOR]
- Published
- 2019
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10. Competition-Driven Multimodal Multiobjective Optimization and Its Application to Feature Selection for Credit Card Fraud Detection.
- Author
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Han, Shoufei, Zhu, Kun, Zhou, Mengchu, and Cai, Xinye
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CREDIT card fraud , *FRAUD investigation , *FEATURE selection , *EVOLUTIONARY algorithms , *PARETO optimum - Abstract
Feature selection has been considered as an effective method to solve imbalanced classification problems. It can be formulated as a multiobjective optimization problem (MOP) aiming to find a small feature subset while achieving a high classification accuracy. With traditional MOP, the focus is on deriving an optimal solution (i.e., a feature subset), while ignoring the diversity in solution space (e.g., there could exist multiple feature subsets achieving the same accuracy). Providing more options for feature selection would be beneficial since some features can be more difficult to obtain than others. In this work, we treat feature selection as a multimodal MOP (MMOP) whose goals are to find an excellent Pareto front in objective space and as many equivalent Pareto optimal solutions (feature subsets) as possible in feature space. Note that though several multimodal multiobjective evolutionary algorithms (MMEAs) have been proposed, their use of a convergence-first selection criterion could cause the loss of solution diversity in an objective and feature space. To address the issue, a novel competition-driven mechanism is designed to assist the existing multimodal MMEAs in locating more equivalent feature subsets and a desired Pareto front. The effectiveness of the proposed mechanism is first verified on all 22 MMOPs from CEC2019. Then, the proposed method is applied to feature selection in imbalanced classification problems and a real-world application, i.e., credit card fraud detection. Experimental results show that the proposed mechanism can not only provide more equivalent feature subsets but also improve classification accuracy. [ABSTRACT FROM AUTHOR]
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- 2022
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11. Preparation of twisted organic–inorganic hybrid silica bundles with circularly polarized luminescence by supramolecular templating polymerization.
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Li, Min, Wang, Yong, Cai, Xinye, Li, Yi, Li, Hongkun, Yang, Yonggang, and Li, Yongfang
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POLYMERIZATION , *LUMINESCENCE , *SILICA , *THERMAL stability , *CHIRALITY - Abstract
Single-handed twisted organic–inorganic hybrid silica bundles with circularly polarized luminescence property were prepared by supramolecular templating polymerization of tetraethoxysilane and TPE-containing bis(triethoxysilane). [Display omitted] • Twisted hybrid silicas were prepared by supramolecular templating polymerization. • Chirality was transferred from supramolecular templates to hybrid silicas. • Luminescent hybrid silicas possess high thermal stabilities. • Hybrid silicas exhibit circularly polarized luminescence activity. Circularly polarized luminescence (CPL) materials have attracted increasing attention because of their promising applications in optoelectronics and chiroptical sensing. However, silica-based organic–inorganic hybrid nanomaterials with CPL properties have been rarely reported. In this work, twisted hybrid silica bundles were prepared by supramolecular templating polymerization of tetraphenylethene-containing bis(triethoxysilane) and tetraethoxysilane using the self-assemblies of chiral low-molecular-weight gelators as templates. It was found that the chirality of the supramolecular templates was transferred to the hybrid silicas. The hybrid silicas emitted green CPL with the luminescence dissymmetry factor up to 1.2 × 10−3 and high fluorescence efficiency. This work presents a facile method to fabricate silica-based hybrid nanomaterials with CPL activity. [ABSTRACT FROM AUTHOR]
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- 2021
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12. Neighborhood based decision-theoretic rough set models.
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Li, Weiwei, Huang, Zhiqiu, Jia, Xiuyi, and Cai, Xinye
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SET theory , *ROUGH sets , *DATA mining , *PROBABILITY theory , *APPROXIMATE reasoning , *DECISION theory - Abstract
As an extension of Pawlak rough set model, decision-theoretic rough set model (DTRS) adopts the Bayesian decision theory to compute the required thresholds in probabilistic rough set models. It gives a new semantic interpretation of the positive, boundary and negative regions by using three-way decisions. DTRS has been widely discussed and applied in data mining and decision making. However, one limitation of DTRS is its lack of ability to deal with numerical data directly. In order to overcome this disadvantage and extend the theory of DTRS, this paper proposes a neighborhood based decision-theoretic rough set model (NDTRS) under the framework of DTRS. Basic concepts of NDTRS are introduced. A positive region related attribute reduct and a minimum cost attribute reduct in the proposed model are defined and analyzed. Experimental results show that our methods can get a short reduct. Furthermore, a new neighborhood classifier based on three-way decisions is constructed and compared with other classifiers. Comparison experiments show that the proposed classifier can get a high accuracy and a low misclassification cost. [ABSTRACT FROM AUTHOR]
- Published
- 2016
- Full Text
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