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The double row layout problem is to arrange a number of machines on both sides of a straight aisle so as to minimize the total material handling cost. Aiming at the random distribution of product demands, we study a stochastic robust double row layout problem (SR-DRLP). A mixed integer programming (MIP) model is established for SR-DRLP. A surrogate model is used to linearize the nonlinear term in the MIP to achieve a mixed integer linear programming model, which can be readily solved by an exact method to yield high-quality solutions (layouts) for small-scale SR-DRLPs. Furthermore, we propose a hybrid approach combining a local search and an exact approach (LS-EA) to solve large-scale SR-DRLPs. Firstly, a local search is designed to optimize the machine sequences on two rows and the clearance from the most left machine on row 1 to the left boundary. Then, the exact location of each machine is further optimized by an exact approach. The LS-EA is applied to six problem instances ranging from 8 to 50 machines. Experimental results show that the surrogate model is effective and LS-EA outperforms the comparison approaches.
Xing Wan; Xing-Quan Zuo; Xin-Chao Zhao. A Surrogate Model-Based Hybrid Approach for Stochastic Robust Double Row Layout Problem. Mathematics 2021, 9, 1711 .
AMA StyleXing Wan, Xing-Quan Zuo, Xin-Chao Zhao. A Surrogate Model-Based Hybrid Approach for Stochastic Robust Double Row Layout Problem. Mathematics. 2021; 9 (15):1711.
Chicago/Turabian StyleXing Wan; Xing-Quan Zuo; Xin-Chao Zhao. 2021. "A Surrogate Model-Based Hybrid Approach for Stochastic Robust Double Row Layout Problem." Mathematics 9, no. 15: 1711.
The capacitated arc routing problem (CARP) has attracted much attention during last decades due to its wide applications. However, the existing research methods still have a little potential to make full use of the characteristics of CARP. This paper aims to mine the essential characteristics of arc routing problem that node routing problem does not have. Based on the observation on characteristics of arc routing instances, smooth condition is proposed and constructed as a rule to divide the link between two tasks into smooth link and non-smooth link. Then smooth degree is defined to measure the influence of non-smooth links on solution and a small smooth degree means the better quality for a solution. The effect of smooth degree is verified through simulation comparison on several instance sets, which indicates that there is a positive correlation between smooth degree and the total cost of a solution. Non-smooth penalty is used to drive the non-smooth solution to smooth and to improve its total cost. Then non-smooth penalty is inserted into path-scanning variants and new construction algorithms are obtained. A partial reconstruction method (PRM) is designed using these construction algorithms as an alternative kernel method. In order to further reduce the routes number, a reinsert method (RiM) is proposed. Combining these two methods with traditional local search algorithms, a memetic algorithm with non-smooth penalty (MANSP) is proposed which is originated from the initial observation on the essential characteristics of arc routing problem. Extensive experiments on smooth degree, penalty factor, and comparison with state-of-the-art algorithms show that the proposed strategies are effective and the proposed algorithm MANSP performs very competitive.
Rui Li; Xinchao Zhao; Xingquan Zuo; Jianmei Yuan; Xin Yao. Memetic algorithm with non-smooth penalty for capacitated arc routing problem. Knowledge-Based Systems 2021, 220, 106957 .
AMA StyleRui Li, Xinchao Zhao, Xingquan Zuo, Jianmei Yuan, Xin Yao. Memetic algorithm with non-smooth penalty for capacitated arc routing problem. Knowledge-Based Systems. 2021; 220 ():106957.
Chicago/Turabian StyleRui Li; Xinchao Zhao; Xingquan Zuo; Jianmei Yuan; Xin Yao. 2021. "Memetic algorithm with non-smooth penalty for capacitated arc routing problem." Knowledge-Based Systems 220, no. : 106957.
Opposition-based learning (OBL) is an effective strategy to enhance many optimization methods among which opposition-based differential evolution (ODE) is one of the successful variants. However, ODE is a strict point-to-point algorithm, which may cause those opposite solutions to be ignored who are close to, however, have a gap to more promising solutions in the neighborhood. It usually provides a relatively narrow search channel for the candidate solutions and cannot maintain well population diversity. Hence, it is necessary to broaden the search neighborhood of the opposite solutions to increase the possibility of seeking out an even better solution. Thus, a new approach, GODE, is proposed to implement a Gaussian perturbation operation around the opposite point to expand its search neighborhood. Three different self-adaptive standard deviation models are then proposed and compared in the Gaussian perturbation strategy. Subsequently, a multi-stage perturbation strategy with different sized neighborhood is adopted to balance exploration and exploitation during different evolutionary stages. GODE is firstly compared with DE and ODE on CEC2014 benchmark suite with dimension of 30, 50 and 100. Many recent state-of-the-art algorithms using OBL strategy are further conducted comparison with GODE. The experimental results and statistical comparison analysis demonstrated that GODE has better or equal competitive performance against the classical and recent competitors.
Xinchao Zhao; Shuai Feng; Junling Hao; Xingquan Zuo; Yong Zhang. Neighborhood opposition-based differential evolution with Gaussian perturbation. Soft Computing 2021, 25, 27 -46.
AMA StyleXinchao Zhao, Shuai Feng, Junling Hao, Xingquan Zuo, Yong Zhang. Neighborhood opposition-based differential evolution with Gaussian perturbation. Soft Computing. 2021; 25 (1):27-46.
Chicago/Turabian StyleXinchao Zhao; Shuai Feng; Junling Hao; Xingquan Zuo; Yong Zhang. 2021. "Neighborhood opposition-based differential evolution with Gaussian perturbation." Soft Computing 25, no. 1: 27-46.
Biogeography-based optimization (BBO) algorithm is not good at dealing with regions where function values change dramatically or barely. A novel biogeography-based optimization algorithm is proposed in this paper based on Momentum migration and taxonomic mutation. The momentum item is added to the original migration operation of BBO. It makes the algorithm more advantageous in dealing with regions where function values change dramatically or barely. At the same time, taxonomic mutation strategy divides the solutions into three categories: promising class, middle class and inferior class. Promising solutions do not take part in this mutation operation. Solutions of middle class use balanced differential mutation, and inferior solutions adopt exploration-biased random mutation. This strategy further increases the diversity of population. The simulation experiments are carried out with different types of CEC2014 benchmark functions. The proposed algorithm is compared with other algorithms and shows stronger global search ability, faster convergence speed and higher convergence accuracy.
Xinchao Zhao; Yisheng Ji; Junling Hao. A Novel Biogeography-Based Optimization Algorithm with Momentum Migration and Taxonomic Mutation. Transactions on Petri Nets and Other Models of Concurrency XV 2020, 12145, 83 -93.
AMA StyleXinchao Zhao, Yisheng Ji, Junling Hao. A Novel Biogeography-Based Optimization Algorithm with Momentum Migration and Taxonomic Mutation. Transactions on Petri Nets and Other Models of Concurrency XV. 2020; 12145 ():83-93.
Chicago/Turabian StyleXinchao Zhao; Yisheng Ji; Junling Hao. 2020. "A Novel Biogeography-Based Optimization Algorithm with Momentum Migration and Taxonomic Mutation." Transactions on Petri Nets and Other Models of Concurrency XV 12145, no. : 83-93.
The canonical harmony search (HS) algorithm generates a new solution by using random adjustment. However, the beneficial effects of harmony memory are not well considered. In order to make full use of harmony memory to generate new solutions, this paper proposes a new adaptive harmony search algorithm (aHSDE) with a differential mutation, periodic learning and linear population size reduction strategy for global optimization. Differential mutation is used for pitch adjustment, which provides a promising direction guidance to adjust the bandwidth. To balance the diversity and convergence of harmony memory, a linear reducing strategy of harmony memory is proposed with iterations. Meanwhile, periodic learning is used to adaptively modify the pitch adjusting rate and the scaling factor to improve the adaptability of the algorithm. The effects and the cooperation of the proposed strategies and the key parameters are analyzed in detail. Experimental comparison among well-known HS variants and several state-of-the-art evolutionary algorithms on CEC 2014 benchmark indicates that the aHSDE has a very competitive performance.
Xinchao Zhao; Rui Li; Junling Hao; Zhaohua Liu; Jianmei Yuan. A New Differential Mutation Based Adaptive Harmony Search Algorithm for Global Optimization. Applied Sciences 2020, 10, 2916 .
AMA StyleXinchao Zhao, Rui Li, Junling Hao, Zhaohua Liu, Jianmei Yuan. A New Differential Mutation Based Adaptive Harmony Search Algorithm for Global Optimization. Applied Sciences. 2020; 10 (8):2916.
Chicago/Turabian StyleXinchao Zhao; Rui Li; Junling Hao; Zhaohua Liu; Jianmei Yuan. 2020. "A New Differential Mutation Based Adaptive Harmony Search Algorithm for Global Optimization." Applied Sciences 10, no. 8: 2916.
In this paper, second order differential evolution (SODE) algorithm is considered to solve the constrained optimization problems. After offspring are generated by the second order differential evolution, the ε constrained method is chosen for selection in this paper. In order to show that second order differential vector is better than differential vector in solving constrained optimization problems, differential evolution (DE) with the ε constrained method is used for performance comparison. The experiments on 12 test functions from IEEE CEC 2006 demonstrate that second order differential evolution shows better or at least competitive performance against DE when dealing with constrained optimization problems.
Xinchao Zhao; Jia Liu; Junling Hao; Jiaqi Chen; Xingquan Zuo. Second Order Differential Evolution for Constrained Optimization. Algorithms and Data Structures 2019, 384 -394.
AMA StyleXinchao Zhao, Jia Liu, Junling Hao, Jiaqi Chen, Xingquan Zuo. Second Order Differential Evolution for Constrained Optimization. Algorithms and Data Structures. 2019; ():384-394.
Chicago/Turabian StyleXinchao Zhao; Jia Liu; Junling Hao; Jiaqi Chen; Xingquan Zuo. 2019. "Second Order Differential Evolution for Constrained Optimization." Algorithms and Data Structures , no. : 384-394.
Since different features may require different costs, the cost-sensitive feature selection problem become more and more important in real-world applications. Generally, it includes two main conflicting objectives, i.e., maximizing the classification performance and minimizing the feature cost. However, most existing approaches treat this task as a single-objective optimization problem. To satisfy various requirements of decision-makers, this paper studies a multi-objective feature selection approach, called two-archive multi-objective artificial bee colony algorithm (TMABC-FS). Two new operators, i.e., convergence-guiding search for employed bees and diversity-guiding search for onlooker bees, are proposed for obtaining a group of non-dominated feature subsets with good distribution and convergence. And two archives, i.e., the leader archive and the external archive are employed to enhance the search capability of different kinds of bees. The proposed TMABC-FS is validated on several datasets from UCI, and is compared with two traditional algorithms and three multi-objective methods. Results have shown that TMABC-FS is an efficient and robust optimization method for solving cost-sensitive feature selection problems.
Yong Zhang; Shi Cheng; Yuhui Shi; Dun-Wei Gong; Xinchao Zhao. Cost-sensitive feature selection using two-archive multi-objective artificial bee colony algorithm. Expert Systems with Applications 2019, 137, 46 -58.
AMA StyleYong Zhang, Shi Cheng, Yuhui Shi, Dun-Wei Gong, Xinchao Zhao. Cost-sensitive feature selection using two-archive multi-objective artificial bee colony algorithm. Expert Systems with Applications. 2019; 137 ():46-58.
Chicago/Turabian StyleYong Zhang; Shi Cheng; Yuhui Shi; Dun-Wei Gong; Xinchao Zhao. 2019. "Cost-sensitive feature selection using two-archive multi-objective artificial bee colony algorithm." Expert Systems with Applications 137, no. : 46-58.
Hospital department layout problems (HDLPs) are significant in enhancing service quality and reducing patients' travel distance and time. Their studies are scarce in comparison with those for facility layout problems in manufacturing systems. Existing approaches to HDLPs usually adopt simplified models and thus gain very limited applications in a real world. HDLPs typically involve multiple objectives that may conflict with each other. There have been no studies on their multiobjective heuristic approaches to our best knowledge. In this paper, we propose multiobjective tabu search (MTS) for a real-world HDLP. Beside the frequently used objective of flow cost, ensuring the closeness among certain departments is introduced as another one. A solution coding scheme is designed to represent a solution. A penalty function is devised to handle infeasible solutions. Local search is integrated into tabu search to optimize the assignment of departments. Experiment results show that MTS is able to produce Pareto solutions that outperform those of the comparative method. Compared to the actually implemented layout, solutions produced by MTS can save about 5%-15% patients' travel time (distance).
Xingquan Zuo; Bin Li; Xuewen Huang; Mengchu Zhou; Chunyang Cheng; Xinchao Zhao; Zhishuo Liu. Optimizing Hospital Emergency Department Layout via Multiobjective Tabu Search. IEEE Transactions on Automation Science and Engineering 2019, 16, 1137 -1147.
AMA StyleXingquan Zuo, Bin Li, Xuewen Huang, Mengchu Zhou, Chunyang Cheng, Xinchao Zhao, Zhishuo Liu. Optimizing Hospital Emergency Department Layout via Multiobjective Tabu Search. IEEE Transactions on Automation Science and Engineering. 2019; 16 (3):1137-1147.
Chicago/Turabian StyleXingquan Zuo; Bin Li; Xuewen Huang; Mengchu Zhou; Chunyang Cheng; Xinchao Zhao; Zhishuo Liu. 2019. "Optimizing Hospital Emergency Department Layout via Multiobjective Tabu Search." IEEE Transactions on Automation Science and Engineering 16, no. 3: 1137-1147.
Differential evolution (DE) has attracted more and more attention. However, the neighborhood and direction information has not been fully utilized in exploration and exploitation stages. A failure remember-driven self-adaptive differential evolution algorithm, ATBDE, is proposed in this paper, which uses “Top-Bottom” strategy with optional archive and a parameter self-adapting strategy driven by “Failure Remember” operation. “Top-Bottom” strategy utilizes historical heuristic information obtained from the successful and failed individuals, respectively, to guide individuals toward the potential more promising regions in an optional archive manner. This strategy is also theoretically analyzed for the best implementation. The failure remember-driven parameter adaption strategy shares the positive search experience from the successful individuals and abandons the negative search experience for those successive failing individuals. Comprehensive experiments show that ATBDE is better than, or at least comparable to, other DE algorithms in terms of convergence performance and accuracy.
Xinchao Zhao; Guanzhi Xu; Li Rui; Dongyue Liu; Huiping Liu; Jianhua Yuan. A failure remember-driven self-adaptive differential evolution with top-bottom strategy. Swarm and Evolutionary Computation 2018, 45, 1 -14.
AMA StyleXinchao Zhao, Guanzhi Xu, Li Rui, Dongyue Liu, Huiping Liu, Jianhua Yuan. A failure remember-driven self-adaptive differential evolution with top-bottom strategy. Swarm and Evolutionary Computation. 2018; 45 ():1-14.
Chicago/Turabian StyleXinchao Zhao; Guanzhi Xu; Li Rui; Dongyue Liu; Huiping Liu; Jianhua Yuan. 2018. "A failure remember-driven self-adaptive differential evolution with top-bottom strategy." Swarm and Evolutionary Computation 45, no. : 1-14.
Particle swarm optimization (PSO) is an evolutionary algorithm that is well known for its simplicity and effectiveness. It usually has strong global search capability but has the drawback of being easily trapped by local optima. A scaling mutation strategy and an elitist learning strategy are presented in this paper. Based on these strategies, an improved PSO variant (LSERPSO) is developed through a local search and ring topology strategy. The new scaling mutation strategy involved an exploration and exploitation balance focusing on mutation operation. A collection of elite individuals is maintained such that an array of current particles can learn from them. A ring topology-based neighborhood structure is adopted to maintain the population diversity and to reduce the possibility of particles being trapped in local optima. Finally, a quasi-Newton-based local search is incorporated to enhance the fine-grained capability. The effects of these proposed strategies and their cooperation are verified step by step. The performance of LSERPSO is comprehensively studied using IEEE CEC2015 benchmark functions.
Guangzhi Xu; Xinchao Zhao; Tong Wu; Rui Li; Xingmei Li. An Elitist Learning Particle Swarm Optimization With Scaling Mutation and Ring Topology. IEEE Access 2018, 6, 78453 -78470.
AMA StyleGuangzhi Xu, Xinchao Zhao, Tong Wu, Rui Li, Xingmei Li. An Elitist Learning Particle Swarm Optimization With Scaling Mutation and Ring Topology. IEEE Access. 2018; 6 ():78453-78470.
Chicago/Turabian StyleGuangzhi Xu; Xinchao Zhao; Tong Wu; Rui Li; Xingmei Li. 2018. "An Elitist Learning Particle Swarm Optimization With Scaling Mutation and Ring Topology." IEEE Access 6, no. : 78453-78470.
Facility layout is vital to save operational cost and enhance production efficiency. Multirow layout is a common pattern in practical manufacturing environment. Although parallel machines are frequently implemented in practice to enhance productivity, there lacks any in-depth study on multirow layout problem with parallel machines. In this paper, its mathematical programming formulation is established to minimize material flow cost. A three-stage approach is proposed to solve it. First, a Monte Carlo heuristic is devised to optimize the sequence of machines on multiple rows. Second, a linear program is used to determine the optimal exact location of each machine. Finally, an exchange heuristic is adopted to reassign material flows among parallel machines in different machine groups. An iterative optimization strategy is suggested to execute the three stages repeatedly to improve the solution quality. This approach is applied to a number of problem instances and compared against others. The experimental results show that it is able to effectively solve this new problem and significantly decrease material flow cost.
Xingquan Zuo; Shubing Gao; Mengchu Zhou; Xin Yang; Xinchao Zhao. A Three-Stage Approach to a Multirow Parallel Machine Layout Problem. IEEE Transactions on Automation Science and Engineering 2018, 16, 433 -447.
AMA StyleXingquan Zuo, Shubing Gao, Mengchu Zhou, Xin Yang, Xinchao Zhao. A Three-Stage Approach to a Multirow Parallel Machine Layout Problem. IEEE Transactions on Automation Science and Engineering. 2018; 16 (1):433-447.
Chicago/Turabian StyleXingquan Zuo; Shubing Gao; Mengchu Zhou; Xin Yang; Xinchao Zhao. 2018. "A Three-Stage Approach to a Multirow Parallel Machine Layout Problem." IEEE Transactions on Automation Science and Engineering 16, no. 1: 433-447.
Most of existing swarm intelligence (SI) algorithms is modeling based on natural phenomena. Firstly, different from the previous practices, this paper constructs a mathematical model based on the traditional optimization algorithms. To simplify this model, a new algorithm Linear Transformation and Elitist Selection algorithm (LTES) is proposed. Experiment shows that the algorithm has origin illusion phenomenon. Then, this paper observes origin illusion phenomenon for the population-based optimization algorithm, and experiments shows that crossover operator is an effective way for LTES’ origin illusion problem. Finally, another algorithm Contraction and Guidance Algorithm (CGA) is proposed to prove that elitist selection is not necessary. The experimental results show that both algorithms are effective.
Rui Li; Guangzhi Xu; Xinchao Zhao; Dunwei Gong. Origin Illusion, Elitist Selection and Contraction Guidance. Communications in Computer and Information Science 2018, 401 -410.
AMA StyleRui Li, Guangzhi Xu, Xinchao Zhao, Dunwei Gong. Origin Illusion, Elitist Selection and Contraction Guidance. Communications in Computer and Information Science. 2018; ():401-410.
Chicago/Turabian StyleRui Li; Guangzhi Xu; Xinchao Zhao; Dunwei Gong. 2018. "Origin Illusion, Elitist Selection and Contraction Guidance." Communications in Computer and Information Science , no. : 401-410.
Hongyi Shi; Chunlu Wang; Xingquan Zuo; Xinchao Zhao. A Multiobjective Genetic Algorithm Based Dynamic Bus Vehicle Scheduling Approach. Communications in Computer and Information Science 2018, 152 -161.
AMA StyleHongyi Shi, Chunlu Wang, Xingquan Zuo, Xinchao Zhao. A Multiobjective Genetic Algorithm Based Dynamic Bus Vehicle Scheduling Approach. Communications in Computer and Information Science. 2018; ():152-161.
Chicago/Turabian StyleHongyi Shi; Chunlu Wang; Xingquan Zuo; Xinchao Zhao. 2018. "A Multiobjective Genetic Algorithm Based Dynamic Bus Vehicle Scheduling Approach." Communications in Computer and Information Science , no. : 152-161.
Guangzhi Xu; Xinchao Zhao; Rui Li. Cooperative Co-evolution with Principal Component Analysis for Large Scale Optimization. Communications in Computer and Information Science 2018, 426 -434.
AMA StyleGuangzhi Xu, Xinchao Zhao, Rui Li. Cooperative Co-evolution with Principal Component Analysis for Large Scale Optimization. Communications in Computer and Information Science. 2018; ():426-434.
Chicago/Turabian StyleGuangzhi Xu; Xinchao Zhao; Rui Li. 2018. "Cooperative Co-evolution with Principal Component Analysis for Large Scale Optimization." Communications in Computer and Information Science , no. : 426-434.
According to the inherent feature of knapsack problem, a multi-parent multi-point crossover operation (MP2X) is proposed, which is implanted with orthogonal experimental design method. The aim of implementing orthogonal experimental design method to MP2X operation is to fully utilizing the inherent information from multiple component of multiple individuals. Based on MP2X operation and orthogonal design method, a genetic algorithm variant (MPXOGA) is proposed in this paper. The simulation results on classic knapsack instances show that MPXOGA is better than several other solvers, including Hybrid Genetic Algorithm (HGA), Greedy Genetic Algorithm (GGA), Greedy Binary Particle Swarm Optimization Algorithm (GBPSOA) and Very Greedy PSO (VGPSO) in the ability of finding optimal solution, the efficiency and the robustness.
Xinchao Zhao; Jiaqi Chen; Rui Li; Dunwei Gong; Xingmei Li. An Orthogonal Genetic Algorithm with Multi-parent Multi-point Crossover for Knapsack Problem. Programmieren für Ingenieure und Naturwissenschaftler 2018, 415 -425.
AMA StyleXinchao Zhao, Jiaqi Chen, Rui Li, Dunwei Gong, Xingmei Li. An Orthogonal Genetic Algorithm with Multi-parent Multi-point Crossover for Knapsack Problem. Programmieren für Ingenieure und Naturwissenschaftler. 2018; ():415-425.
Chicago/Turabian StyleXinchao Zhao; Jiaqi Chen; Rui Li; Dunwei Gong; Xingmei Li. 2018. "An Orthogonal Genetic Algorithm with Multi-parent Multi-point Crossover for Knapsack Problem." Programmieren für Ingenieure und Naturwissenschaftler , no. : 415-425.
Single row layout problem is widely implemented in manufacturing systems. In this paper, we study a single row layout problem with consideration of additional clearances between adjacent facilities. The additional clearance is used to store work in process and allow technician access to the side of the facility and may be shared by any two adjacent facilities. A hybrid solution approach that integrates a tabu search with a mathematical programming is proposed to address it. The purpose of tabu search is to optimize the sequence of facilities and mathematical programming is used to find the optimal additional clearances. The hybrid approach is applied to a number of problem instances involving a range of facilities. Experimental results show that the hybrid approach can find the optimal solutions for most of small-size problem instances and good quality solutions for large-size problem instances.
Meng Yu; Xingquan Zuo; Xinchao Zhao; Chunlu Wang. Hybridizing tabu search with mathematical programming for solving a single row layout problem. 2018 IEEE 14th International Conference on Automation Science and Engineering (CASE) 2018, 974 -980.
AMA StyleMeng Yu, Xingquan Zuo, Xinchao Zhao, Chunlu Wang. Hybridizing tabu search with mathematical programming for solving a single row layout problem. 2018 IEEE 14th International Conference on Automation Science and Engineering (CASE). 2018; ():974-980.
Chicago/Turabian StyleMeng Yu; Xingquan Zuo; Xinchao Zhao; Chunlu Wang. 2018. "Hybridizing tabu search with mathematical programming for solving a single row layout problem." 2018 IEEE 14th International Conference on Automation Science and Engineering (CASE) , no. : 974-980.
A new multi-stage perturbed differential evolution (MPDE) is proposed in this paper. A new mutation strategy “multi-stage perturbation” is implemented with directivity difference information strategy and multiple parameters adaption. The DE/current-to-pbest is introduced to increase the population diversity while remaining its elitist learning behavior in this architecture. The multi-stage perturbation-based mutation operation utilizes the Normal random distribution with adjustable variance to perturb the chosen solutions. Multiple parameters are adaptively adjusted to appropriate values to match the current search status of algorithm. It is thus helpful to enhance the performance and the robustness of algorithm. Simulation results show that the newly proposed MPDE is better than, or at least comparable to CLPSO, SPSO2011, NGHS, jDE, CoDE, SaDE and JADE algorithms in terms of optimization performance based on CEC2015 benchmark function.
Guangzhi Xu; Rui Li; Junling Hao; Xinchao Zhao; Ying Tan. A new multi-stage perturbed differential evolution with multi-parameter adaption and directional difference. Natural Computing 2018, 19, 683 -698.
AMA StyleGuangzhi Xu, Rui Li, Junling Hao, Xinchao Zhao, Ying Tan. A new multi-stage perturbed differential evolution with multi-parameter adaption and directional difference. Natural Computing. 2018; 19 (4):683-698.
Chicago/Turabian StyleGuangzhi Xu; Rui Li; Junling Hao; Xinchao Zhao; Ying Tan. 2018. "A new multi-stage perturbed differential evolution with multi-parameter adaption and directional difference." Natural Computing 19, no. 4: 683-698.
The project portfolio selection problem considering divisibility is a new research problem rising in recent years. However, two deficiencies are discovered in current divisible project portfolio selection research, one is that researchers always ignore the already started exiting projects when selecting a project portfolio, and the other is that the project parameters are all considered as exact values which are not consistent with practice situation. Under this circumstance, the paper first discusses the dynamic project portfolio selection problem with project divisibility. Meanwhile, due to the lack of correlative historical data, some project parameters are given by experts’ estimates and are treated as uncertain variables. Therefore, a mean-variance mixed integer nonlinear optimal selection model is first developed in this paper to deal with the uncertain dynamic project portfolio selection problem with divisibility. For the convenience of computations, an equivalent mixed integer linear programming representation is proposed. Numerical examples with two scenarios are presented to shed light on the characteristics of the proposed model.
Xingmei Li; Yaxian Wang; QingYou Yan; Xinchao Zhao. Uncertain mean-variance model for dynamic project portfolio selection problem with divisibility. Fuzzy Optimization and Decision Making 2018, 18, 37 -56.
AMA StyleXingmei Li, Yaxian Wang, QingYou Yan, Xinchao Zhao. Uncertain mean-variance model for dynamic project portfolio selection problem with divisibility. Fuzzy Optimization and Decision Making. 2018; 18 (1):37-56.
Chicago/Turabian StyleXingmei Li; Yaxian Wang; QingYou Yan; Xinchao Zhao. 2018. "Uncertain mean-variance model for dynamic project portfolio selection problem with divisibility." Fuzzy Optimization and Decision Making 18, no. 1: 37-56.
Recommender systems have received much attention due to their wide applications. Current recommender approaches typically recommend items to user based on the rating prediction. However, the predicted ratings cannot truly reflect users interests on items because the rating prediction is usually based on history data and does not consider the effect of time factor on uses interests (behaviors). In this paper, we propose a recommendation approach combining the matrix factorization and a recurrent neural network. In this approach, all the items rated by a user are considered as time series data. The matrix factorization is used to obtain latent vectors of those items. The recurrent neural network is taken as a time series prediction model and trained by the latent vectors of historical items, and then the trained model is used to predict the latent vector of the item to be recommended. Finally, a recommendation list is formed by mapping the latent vector into a set of items. Experimental results show that the proposed approach is able to produce an effective recommend list and outperforms those comparative approaches.
Ruihong Li; Xingquan Zuo; Pan Wang; Xinchao Zhao. A Recommendation Approach Based on Latent Factors Prediction of Recurrent Neural Network. Programmieren für Ingenieure und Naturwissenschaftler 2017, 446 -455.
AMA StyleRuihong Li, Xingquan Zuo, Pan Wang, Xinchao Zhao. A Recommendation Approach Based on Latent Factors Prediction of Recurrent Neural Network. Programmieren für Ingenieure und Naturwissenschaftler. 2017; ():446-455.
Chicago/Turabian StyleRuihong Li; Xingquan Zuo; Pan Wang; Xinchao Zhao. 2017. "A Recommendation Approach Based on Latent Factors Prediction of Recurrent Neural Network." Programmieren für Ingenieure und Naturwissenschaftler , no. : 446-455.
Personalized recommendation approaches have received much attention over the years. In this paper, we propose a hybrid recommendation approach that integrates an item-based collaborative filtering, a user-based collaborative filtering and a matrix factorization method. The approach considers the two objectives of recommendation's accuracy and diversity simultaneously. First, a set of items is created separately by each of the three methods. Then, items produced by the three methods are combined into a set of candidate items. Finally, a multiobjective genetic algorithm is adopted to choose a set of Pareto recommendation lists from the set. Experimental results show that the proposed approach is very effective and is able to produce better Pareto solutions than those comparative approaches.
Pan Wang; Xingquan Zuo; Congcong Guo; Ruihong Li; Xinchao Zhao; Chaomin Luo. A multiobjective genetic algorithm based hybrid recommendation approach. 2017 IEEE Symposium Series on Computational Intelligence (SSCI) 2017, 1 -6.
AMA StylePan Wang, Xingquan Zuo, Congcong Guo, Ruihong Li, Xinchao Zhao, Chaomin Luo. A multiobjective genetic algorithm based hybrid recommendation approach. 2017 IEEE Symposium Series on Computational Intelligence (SSCI). 2017; ():1-6.
Chicago/Turabian StylePan Wang; Xingquan Zuo; Congcong Guo; Ruihong Li; Xinchao Zhao; Chaomin Luo. 2017. "A multiobjective genetic algorithm based hybrid recommendation approach." 2017 IEEE Symposium Series on Computational Intelligence (SSCI) , no. : 1-6.