The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
L I Yanfei - One of the best experts on this subject based on the ideXlab platform.
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a wind speed forecasting optimization model for wind farms based on time series analysis and kalman filter Algorithm
Power system technology, 2008Co-Authors: L I YanfeiAbstract:To improve the wind speed forecasting accuracy for wind farm and solve the problem of time delay of forecasting by time series model, the authors propose a Hybrid Algorithm integrating time series analysis with Kalman filter. The basic thinking of this Algorithm is as following: firstly, by use of time series analysis theory the non-stationary modeling for wind speed signals of wind farm is proceeded to obtain the model equation conforming to its variation law; secondly, by means of the obtained model equation the state equation and observational equation for Kalman filter are deduced; thirdly, the wind speed is forecasted by Kalman forecasting recurrence equation; finally, the forecasting for varying wind speed measured in a certain wind farm is conducted to validate the proposed Hybrid Algorithm. Case study results show that by using this Hybrid Algorithm the forecasting accuracy of wind speed can be improved and the time delay in the forecasting is well solved.
Pascal Van Hentenryck - One of the best experts on this subject based on the ideXlab platform.
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a two stage Hybrid Algorithm for pickup and delivery vehicle routing problems with time windows
Computers & Operations Research, 2006Co-Authors: Russell Bent, Pascal Van HentenryckAbstract:This paper presents a two-stage Hybrid Algorithm for pickup and delivery vehicle routing problems with time windows and multiple vehicles (PDPTW). The first stage uses a simple simulated annealing Algorithm to decrease the number of routes, while the second stage uses Large neighborhood search (LNS) to decrease total travel cost. Experimental results show the effectiveness of the Algorithm which has produced many new best solutions on problems with 100, 200, and 600 customers. In particular, it has improved 47% and 76% of the best solutions on the 200 and 600-customer benchmarks, sometimes by as much as 3 vehicles. These results further confirm the benefits of two-stage approaches in vehicle routing. They also answer positively the open issue in the original LNS paper, which advocated the use of LNS for the PDPTW and argue for the robustness of LNS with respect to side-constraints.
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a two stage Hybrid Algorithm for pickup and delivery vehicle routing problems with time windows
Principles and Practice of Constraint Programming, 2003Co-Authors: Russell Bent, Pascal Van HentenryckAbstract:This paper presents a two-stage Hybrid Algorithm for pickup and delivery vehicle routing problems with time windows and multiple vehicles (PDPTW). The first stage uses a simple simulated annealing Algorithm to decrease the number of routes, while the second stage uses LNS to decrease total travel cost. Experimental results show the effectiveness of the Algorithm which has produced many new best solutions on problems with 100, 200, and 600 customers. In particular, it has improved 47% and 76% of the best solutions on the 200 and 600-customer benchmarks, sometimes by as much as 3 vehicles. These results further confirm the benefits of two-stage approaches in vehicle routing. They also answer positively the open issue in the original LNS paper, which advocated the use of LNS for the PDPTW and argue for the robustness of LNS with respect to side-constraints.
Qingqiang Guo - One of the best experts on this subject based on the ideXlab platform.
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a Hybrid Algorithm based on tabu search and ant colony optimization for k minimum spanning tree problems
Expert Systems With Applications, 2012Co-Authors: Hideki Katagiri, Tomohiro Hayashida, Ichiro Nishizaki, Qingqiang GuoAbstract:This paper considers an efficient approximate Algorithm for solving k-minimum spanning tree problems which is one of the combinatorial optimization in networks. A new Hybrid Algorithm based on tabu search and ant colony optimization is provided. Results of numerical experiments show that the proposed method updates some of the best known values with very short time and that the proposed method provides a better performance with solution accuracy over existing Algorithms.
Ling Wang - One of the best experts on this subject based on the ideXlab platform.
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efficient two level Hybrid Algorithm for the refinery production scheduling problem involving operational transitions
Industrial & Engineering Chemistry Research, 2016Co-Authors: Lu Zhang, Yongheng Jiang, Dexian Huang, Ling WangAbstract:An overall refinery production scheduling problem involving operational transitions was studied by Shi et al. (Ind. Eng. Chem. Res. 2014, 53 (19), 8155–8170), which is an intractable large-scale mixed-integer linear programming (MILP) problem. To deal with this challenge, an efficient two-level Hybrid Algorithm based on the hierarchy of decisions is proposed. In the outer level, the key techniques of a discrete particle swarm optimization Algorithm are designed to search for the operating states assignment of production units. A queue-based solution representation is offered by an elaborate encoding scheme for the outer-level problem so that much fewer discrete decision variables are involved. All the constraints on operating states assignment are represented with the encoding scheme and can be satisfied by all the particles throughout the evolutionary process. In the nested inner level, under the assignment specified in the outer level, the detailed production process is optimized by dual simplex method,...
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efficient two level Hybrid Algorithm for the refinery production scheduling problem involving operational transitions
Industrial & Engineering Chemistry Research, 2016Co-Authors: Lu Zhang, Yongheng Jiang, Xiaoyong Gao, Dexian Huang, Ling WangAbstract:An overall refinery production scheduling problem involving operational transitions was studied by Shi et al. (Ind. Eng. Chem. Res. 2014, 53 (19), 8155–8170), which is an intractable large-scale mixed-integer linear programming (MILP) problem. To deal with this challenge, an efficient two-level Hybrid Algorithm based on the hierarchy of decisions is proposed. In the outer level, the key techniques of a discrete particle swarm optimization Algorithm are designed to search for the operating states assignment of production units. A queue-based solution representation is offered by an elaborate encoding scheme for the outer-level problem so that much fewer discrete decision variables are involved. All the constraints on operating states assignment are represented with the encoding scheme and can be satisfied by all the particles throughout the evolutionary process. In the nested inner level, under the assignment specified in the outer level, the detailed production process is optimized by dual simplex method,...
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an effective Hybrid biogeography based optimization Algorithm for parameter estimation of chaotic systems
Expert Systems With Applications, 2011Co-Authors: Ling Wang, Ye XuAbstract:Parameter estimation of chaotic systems is an important issue in the fields of computational mathematics and nonlinear science, which has gained increasing research and applications. In this paper, biogeography-based optimization (BBO), a new effective optimization Algorithm based on the biogeography theory of the geographical distribution of biological organisms, is reasonably combined with differential evolution and simplex search to develop an effective Hybrid Algorithm for solving parameter estimation problem that is formulated as a multi-dimensional optimization problem. By suitably fusing several optimization methods with different searching mechanisms and features, the exploration and exploitation abilities of the Hybrid Algorithm can be enhanced and well balanced. Numerical simulation based on several typical chaotic systems and comparisons with some existing methods demonstrate the effectiveness of the proposed Algorithm. In addition, the effects of population size and noise on the performances of the Hybrid Algorithm are investigated.
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an effective Hybrid Algorithm based on simplex search and differential evolution for global optimization
International Conference on Intelligent Computing, 2009Co-Authors: Ling WangAbstract:In this paper, an effective Hybrid NM-DE Algorithm is proposed for global optimization by merging the searching mechanisms of Nelder-Mead (NM) simplex method and differential evolution (DE). First a reasonable framework is proposed to Hybridize the NM simplex-based geometric search and the DE-based evolutionary search. Second, the NM simplex search is modified to further improve the quality of solutions obtained by DE. By interactively using these two searching approaches with different mechanisms, the searching behavior can be enriched and the exploration and exploitation abilities can be well balanced. Based on a set of benchmark functions, numerical simulation and statistical comparison are carried out. The comparative results show that the proposed Hybrid Algorithm outperforms some existing Algorithms including Hybrid DE and Hybrid NM Algorithms in terms of solution quality, convergence rate and robustness.
Akbar Maleki - One of the best experts on this subject based on the ideXlab platform.
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a Hybrid Algorithm based optimization on modeling of grid independent biodiesel based Hybrid solar wind systems
Renewable Energy, 2018Co-Authors: Du Guangqian, Kaveh Bekhrad, Pouria Azarikhah, Akbar MalekiAbstract:Abstract The main contribution of this research is formulating the size optimization of grid-independent Hybrid wind/photovoltaic/biodiesel/battery systems and proposing a Hybrid Algorithm on this optimization problem. There are many investigations based on Hybrid wind and PV power systems but the investigation on the Hybrid wind/photovoltaic/biodiesel/battery system is rarely found. Here, the optimal design of a biodiesel/wind/photovoltaic/battery energy system for a stand-alone application in Iran is studied. The objective of the optimum design problem is to minimize the life cycle cost of the wind/photovoltaic/biodiesel/battery system subject to some constraints by adjusting four decision variables, namely, number of batteries, photovoltaic area, the swept area of wind turbines, and fuel consumption of the biodiesel generator. To solve the optimization problem, initially, we investigate the performance of two popular metaheuristic Algorithms, namely, harmony search and simulated annealing. Moreover, this article proposes a Hybrid harmony search-simulated annealing method that combines the advantages of each one of the above-mentioned metaheuristic Algorithms. Simulation results show that the proposed Hybrid harmony search-simulated annealing improves the obtained solutions, in terms of quality, compared to the solutions provided by individual harmony search or individual simulated annealing Algorithms. Moreover, the Hybrid photovoltaic/biodiesel/battery system is the best choice to supply the electrical load.
