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Application of a novel discrete grey model for forecasting natural gas consumption: A case study of Jiangsu Province in China

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ENERGY 2020年 200卷 117443-117443页

作者： Zhou, Weijie Wu, Xiaoli Ding, Song Pan, Jiao Changzhou Univ Business Coll Changzhou 213164 Jiangsu Peoples R China Zhejiang Univ Finance & Econ Sch Econ Hangzhou 310018 Peoples R China Zhejiang Univ Finance & Econ Res Ctr Reg Econ & Integrated Dev Hangzhou 310018 Peoples R China

Natural gas increasingly has become an alternative low-carbon energy source for governments to modify the energy mix and fulfill the commitments that mitigate greenhouse gas emissions. Predicting natural gas consumption therefore is becoming crucial in such situations. In order to obtain accurate forecasts of natural gas consumption, this study has designed a novel discrete grey model considering nonlinearity and fluctuation, which can overcome the inherent drawbacks of the traditional discrete grey model and its optimized variants. Besides, to further enhance the forecasting performance of this proposed model, the cultural algorithm (CA) is employed to optimally determine the emerging parameters of this model. Subsequently, two empirical examples are provided for verifying the efficacy and reliability of the new model by comparing with other existing grey models and statistical models. Lastly, based on the original observations from 2005 to 2017, the novel model is built for predicting the total natural gas demand in Jiangsu province in China. The results indicate that the new model is much superior to other competitors, offering more accurate and reliable performances in the aspect of lower errors in both in-sample and out-of-sample forecasts. Then, based on the above projections, several main reasons for low gas consumption and reasonable suggestions are put forward for Jiangsu's government, which has high potential to boost gas demand in the coming future. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Natural gas consumption prediction Grey prediction model cultural algorithm

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cultural algorithm with Improved Local Search for Optimization Problems

Cultural Algorithm with Improved Local Search for Optimizati...

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IEEE Congress on Evolutionary Computation

作者： Awad, Noor H. Ali, Mostafa Z. Duwairi, Rehab M. Jordan Univ Sci & Technol Irbid 22110 Jordan

ISBN: (纸本)9781479904549;9781479904532

In this paper we propose an optimization algorithm for global optimization problems. The proposed algorithm is named (CA-ImLS) and is based on cultural algorithms and an improved local search approach for optimization over large-scale continuous spaces. In this paper, cultural algorithm and an improved sub-regional local search method are hybridized to form CA-ImLS. The original cultural algorithm is extended to have five parallel local searches that are rooted to its knowledge sources in the belief space component. This directs the search in multi-directions and improves the capability of its problem solvers in obtaining better-quality solutions. The distribution of new search agents is based on the success of the knowledge sources in which each knowledge source has its own local search for generating new agents with better fitness values and enhanced diversity to avoid stagnation. Experimental results are given for a set of benchmark optimization functions. Results indicate an average improvement of 2%-83% over the basic cultural algorithm framework.

关键词： cultural algorithm Local search knowledge source Hybrid algorithm Optimization problem

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Economic environmental dispatch using an enhanced multi-objective cultural algorithm

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ELECTRIC POWER SYSTEMS RESEARCH 2013年 99卷 18-29页

作者： Zhang, Rui Zhou, Jianzhong Mo, Li Ouyang, Shuo Liao, Xiang Huazhong Univ Sci & Technol Sch Hydropower & Informat Engn Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Hubei Key Lab Digital Valley Sci & Technol Wuhan 430074 Peoples R China

Economic environment dispatch (EED) is a significant optimization problem in conventional fossil fuel fired power system to distribute load demand reasonably and scientifically so that fuel cost and emission issues are optimized simultaneously while satisfying various constraints. Valve-point effect, prohibited operation zones (POZs) and transmission losses make the LED a non-smooth and non-convex optimization problem. In order to solve this constrained multi-objective optimization problem including competing objectives as well as complex constraints, an enhanced multi-objective cultural algorithm (EMOCA) is proposed in this paper. The proposed method combines cultural algorithm framework with particle swarm optimization (PSO) to carry though the evolution of population space. Besides, two knowledge structures of belief space in CA are redefined according to the features of EED problem. Moreover, an effective constrain handling method is proposed to handle the equality and inequality constraints of LED problem. Simulation results of test systems demonstrate the capability of the proposed algorithm to generate well-distributed Pareto optimal solutions in a single run. Compared with some current methods, EMOCA has a good performance in finding a diverse set of solutions and in converging near the true Pareto optimal front with lower fuel cost and emission issues synthetically. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Economic environmental dispatch Prohibited operation zones (POZs) Multi-objective optimization cultural algorithm Constraint handle

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cultural coyote optimization algorithm applied to a heavy duty gas turbine operation

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ENERGY CONVERSION AND MANAGEMENT 2019年 199卷 111932-000页

作者： Pierezan, Juliano Maidl, Gabriel Yamao, Eduardo Massashi Coelho, Leandro dos Santos Mariani, Viviana Cocco LACTEC Mech Syst Div Prefeito Lothario Meissner Ave 1 BR-80210170 Curitiba PR Brazil Univ Fed Parana Dept Elect Engn Cel Francisco H dos Santos Ave 100 Curitiba PR Brazil Pontifical Catholic Univ Parana PUCPR Ind & Syst Engn Grad Program PPGEPS Imaculada Conceicao BR-1155 Curitiba PR Brazil Pontifical Catholic Univ Parana PUCPR Dept Mech Engn Imaculada Conceicao BR-1155 Curitiba PR Brazil

In the past decades, the quantity of researches regarding industrial gas turbines (GT) has increased exponentially in terms of number of publications and diversity of applications. The GTs offer high power output along with a high combined cycle efficiency and high fuel flexibility. As consequence, the energy efficiency, the pressure oscillations, the pollutant emissions and the fault diagnosis have become some of the recent concerns related to this type of equipment. In order to solve these GTs related problems and many other real-world engineering and industry 4.0 issues, a set of new technological approaches have been tested, such as the combination of Artificial Neural Networks (ANN) and metaheuristics for global optimization. In this paper, the recently proposed metaheuristic denoted Coyote Optimization algorithm (COA) is applied to the operation optimization of a heavy duty gas turbine placed in Brazil and used in power generation. The global goal is to find the best valves setup to reduce the fuel consumption while coping with environmental and physical constraints from its operation. In order to treat it as an optimization problem, an integrated simulation model is implemented from original data driven models and others previously proposed in literature. Moreover, a new version of the COA that links some concepts from cultural algorithms (CA) is proposed, which is validated under a set of benchmarks functions from the Institute of Electrical and Electronics Engineers (IEEE) Congress on Evolutionary Computation (CEC) 2017 and tested to the GT problem. The results show that the proposed cultural Coyote Optimization algorithm (CCOA) outperforms its counterpart for benchmark functions. Further, non-parametric statistical significance tests prove that the CCOA's performance is competitive when compared to other state-of-the-art metaheuristics after a set of experiments for five case studies. In addition, the convergence analysis shows that the cultural mechanism

关键词： Gas turbines Power generation Constrained global optimization Coyote optimization algorithm cultural algorithm

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The Application of An Improved cultural algorithm in Grid Computing

The Application of An Improved Cultural Algorithm in Grid Co...

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第25届中国控制与决策会议

作者： ZHOU Wei BU Yan-ping ZHOU Ye-qing School of Business East China University of Science and Technology School of Technology Shanghai Jiaotong University School of Mathematical sciences Fudan University

ISBN: (纸本)9781467355339

A cultural algorithm, rooting from simulation of evolution of human being society, provides a new computable framework of evolution algorithms. A novel cultural algorithm based on particle swarm optimization （PSO） algorithm was proposed in this paper. With the advent of the grid, task scheduling in heterogeneous environments becomes more and more important. After analyzing the model of grid scheduling problem, the CPSO algorithm was presented to solve the resource scheduling problem in grid computing. The optimal objective is to minimize the total completing time. The improved algorithm can keep all the advantages of the standard PSO, such as implementation simplicity, low computational burden, and few control parameters, etc. Simulation results demonstrate that it can be superior to the regular PSO. We also tested the CPSO algorithm with the Max-Min method to show the algorithm’s efficiency.

关键词： cultural algorithm particle swarm optimization algorithm grid task scheduling

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Hierarchical Flood Operation Rules Optimization Using Multi-Objective Cultured Evolutionary algorithm Based on Decomposition

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WATER RESOURCES MANAGEMENT 2019年第1期33卷 337-354页

作者： Liu, Yongqi Qin, Hui Mo, Li Wang, Yongqiang Chen, Duan Pang, Shusen Yin, Xingli Huazhong Univ Sci & Technol Sch Hydropower & Informat Engn Wuhan 430074 Hubei Peoples R China Changjiang River Sci Res Inst Wuhan Hubei Peoples R China Three Gorges Cascade Dispatch & Commun Ctr Yichang Hubei Peoples R China

The operation of a reservoir system for flood resources utilization is a complex problem as it involves many variables, a large number of constraints and multiple objectives. In this paper, a new algorithm named multi-objective cultured evolutionary algorithm based on decomposition (MOCEA/D) is proposed for optimizing the hierarchical flood operation rules (HFORs) with four objectives: upstream flood control, downstream flood control, power generation and navigation. The performance of MOCEA/D is validated through some well-known benchmark problems. On achieving satisfactory performance, MOCEA/D is applied to a case study of HFORs optimization for Three Gorges Project (TGP). The experimental results show that MOCEA/D obtains a uniform non-dominated schemes set. The optimized HFORs can improve the power generation and navigation rate as much as possible under the premise of ensuring flood control safety for small and medium floods (smaller than 1% frequency flood). The obtained results show that MOCEA/D can be a viable alternative for generating multi-objective HFORs for water resources planning and management.

关键词： Operation rules Flood resources utilization Multi-objective optimization Decomposition approach cultural algorithm

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Multi-objective Quantum cultural algorithm and Its Application in the Wireless Sensor Networks' Energy-Efficient Coverage Optimization

Multi-objective Quantum Cultural Algorithm and Its Applicati...

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14th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL)

作者： Guo, Yi-nan Chen, Meirong Wang, Chun China Univ Min & Technol Xuzhou Jiangsu Peoples R China

ISBN: (纸本)9783642412783;9783642412776

Whether wireless sensor network covers the target field availably is measured by the network cover rate and the node redundancy rate. To solve this multi-objective optimization problem, multi-objective quantum cultural algorithm is proposed. It effectively utilizes the implicit knowledge extracted from the non-domination individuals to promote more efficient search. Two highlights include: 1. The rectangle's height of each allele is calculated in accordant with the non-dominated sort among individuals. 2. The update operation of quantum individuals and the mutation operator are directed by the implicit knowledge. Taken a typical wireless sensor network with 25 sensor nodes as an example, simulation results indicate that the layout of wireless sensor network obtained by the proposed algorithm have larger network cover rate and lower node redundancy rate.

关键词： Wireless sensor network cultural algorithm knowledge real-coded quantum evolutionary algorithm multi-objective optimization

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The Coverage Optimization for Wireless Sensor Networks Based on Quantum-Inspired cultural algorithm 1

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Chinese Intelligent Automation Conference (CIAC)

作者： Guo, Yi-nan Liu, Dandan Liu, Yun Chen, Meirong China Univ Min & Technol Coll Informat & Elect Engn Xuzhou 221116 Jiangsu Peoples R China

ISBN: (数字)9783642385247

ISBN: (纸本)9783642385247;9783642385230

In order to properly distribute sensor nodes in wireless sensor networks, a coverage model considering maximum coverage ratio and minimum redundancy is given and the optimization strategy based on quantum-inspired cultural algorithm is proposed. In population space, quantum-inspired evolutionary algorithm is used to increase the observed probability. In belief space, the implicit knowledge embodied in the evolution is extracted from the better individuals chosen from the population. It is used to guide the search direction of evolutionary population and influence the update of quantum individuals. Simulation results indicate that the algorithm is superior to other algorithms in coverage optimization and effectively improve the coverage performance of wireless sensor networks.

关键词： Wireless sensor networks Quantum-inspired algorithm cultural algorithm Coverage optimization

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A Hybrid cultural algorithm Based on Clonal Selection Principle for Optimal Generation Scheduling of Cascaded Hydropower Stations

A Hybrid Cultural Algorithm Based on Clonal Selection Princi...

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International Conference on Mechatronic Sciences, Electric Engineering and Computer (MEC)

作者： Qin, Hui Li, Qingqing Hong, Xiaofeng Changjiang River Sci Res Inst Hubei Key Lab Water Resources & Ecoenvironm Sci Wuhan 430010 Peoples R China

ISBN: (纸本)9781479925650

To solve optimal generation scheduling problem of cascaded hydropower stations, a hybrid cultural algorithm based on clonal selection principle (HCA-CSA) is presented. HCA-CSA uses cultural algorithm (CA) as its framework and clonal selection algorithm (CSA) in population space. Considering the characteristics of CSA, three knowledge structures are redefined in belief space to improve the search purposefulness and directivity of CSA, so as to improve the searching convergence rate and precision. In addition, a recombination and a chaos search operation are adopted in belief space to accelerate convergence rate and precision of the proposed algorithm. HCA-CSA is first tested by several benchmark problems and then it is applied to a case study of optimal generation scheduling of the Three Gorges Cascaded Hydropower Stations. The results obtained show its efficiency on solving complex optimization problems, and it can be an alternative for optimal generation scheduling of cascaded hydropower stations.

关键词： cultural algorithm knowledge structure clonal selection principle cascaded hydropower stations optimal generation scheduling

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Hybrid Niche cultural algorithm for Numerical Global Optimization

Hybrid Niche Cultural Algorithm for Numerical Global Optimiz...

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IEEE Congress on Evolutionary Computation

作者： Ali, Mostafa Z. Awad, Noor H. Reynolds, Robert G. Jordan Univ Sci & Engn Comp Informat Syst Irbid Jordan Jordan Univ Sci & Engn Dept Comp Engn Irbid Jordan Wayne State Univ Comp Sci Detroit MI 48202 USA

ISBN: (纸本)9781479904549;9781479904532

Many evolutionary computational models have been introduced for solving engineering optimization problems that usually intend to find the global optimum solution. These methods, however, expose high computational effort and lack the diversity of the population and hence remain trapped in a local optimum. In this paper, we propose new hybrid optimization model, where a version of niche cultural algorithm is integrated with Tabu Search to guide the fittest individuals to new promising areas, aiming to escape local optima. The proposed approach significantly improves the performance of cultural algorithm by maintaining a high diversity among the population of problem solvers. This helps avoid premature and enhances located solutions. The technique is tested using a set of real-parameter optimization benchmark problems. The results in all cases indicate that the proposed method is capable of obtaining the optimal solutions with small number of function evaluations.

关键词： cultural algorithm Niching method Tabu search Hybrid algorithm Global Numerical Optimization

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