In the agricultural water resources system, the regional yield is hard to be simulated accurately under the impacts of spatial heterogeneities of soil, weather, and crop types. The optimal water allocation schemes may...
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In the agricultural water resources system, the regional yield is hard to be simulated accurately under the impacts of spatial heterogeneities of soil, weather, and crop types. The optimal water allocation schemes may be not in accordance with actual conditions due to neglecting the actual crop growth process. Meanwhile, the uncertainties in the simulation-optimization models are not easy to be addressed. To deal with the above problems, this paper develops a framework of the distributed AquaCrop simulation nonlinear multi-objective dependent chance programming (distributed AquaCrop NMOFDCP) for irrigation water resources management under uncertainties. This developed model is applied to a case study of irrigation water resources management in the middle reaches of the Heihe River Basin in China. The 134 homogeneous decision-making units (DMUs) are divided to depict the spatial heterogeneities of the study area, and 472 decision variables (irrigation amount) for 134 DMUs are optimized. Moreover, the model deals with uncertainties expressed as fuzzy goals and tradeoffs relationships between objectives of the yield and water productivity, and measures the satisfactory degrees between objectives and their fuzzy goals. Two groups of Pareto solutions corresponding to the maximum satisfactory degree of the yield, and the maximum satisfactory degree of the water productivity are obtained by the parallel genetic algorithm (PGA) method respectively. In addition, water allocation, satisfactory degree of the yield, and satisfactory degree of water productivity are analyzed at the decision-making unit scale, crop scale, and irrigation-district scale separately. Besides the effects of three weather conditions and four soil types on the system's outputs are conducted. The results show that weather conditions and soil types have obvious effects on the system's outputs at three analysis scales, and different water allocation patterns at the growth period affect the yield and water
Uncertainties from neighboring rival's reservoirs challenge hydropower companies in participating in competitive markets. Cooperative behaviors are generally impractical due to stakeholders' self-interest and ...
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Uncertainties from neighboring rival's reservoirs challenge hydropower companies in participating in competitive markets. Cooperative behaviors are generally impractical due to stakeholders' self-interest and regulatory requirements. Considering this obstacle, this paper proposes a data-driven bilevel model, in a competitive context, to estimate the operational information of the neighboring rival's reservoir, including its historical operating states and operational functions. The proposed bilevel model is an inverse problem of the conventional hydropower scheduling model. The upper-level model is designed to find the most appropriate operational parameters of the estimated reservoir that fit its historical generation volumes. The lower-level model simulates the profit-maxing operation of the estimated reservoir. Since the lower simulating model is nonconvex, an Enhanced parallel genetic algorithm (EPGA) is proposed. It avoids infeasible situations through several strategies and uses multiple CPU threads simultaneously in solving. A case study in China's market demonstrates that the proposed model and solving method can efficiently obtain accurate state series and (near-)optimal operational parameters. More experiments are also taken to validate the parallel design.
Integrated energy system (IES) coupled with advanced adiabatic compressed air energy storage (AA-CAES) and organic Rankine cycle (ORC) has the superiority to peak-load regulation and improve system performance. Theref...
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Integrated energy system (IES) coupled with advanced adiabatic compressed air energy storage (AA-CAES) and organic Rankine cycle (ORC) has the superiority to peak-load regulation and improve system performance. Therefore, a novel IES-ORC-CAES system, which includes AA-CAES, ORC and ICE, is proposed in this paper. In this system, the user return water is used to recover the compression heat generated by AA-CAES. The ICE exhaust smoke flows through three parts successively, the heat exchangers of AA-CAES energy release side, the ORC and absorption heat pump arranged in parallel and the tail heat exchanger, realizing the cascade utilization of energy and meeting the diversified energy demand of users. Considering the time of use electricity price and aiming at economy, environmental protection and energy efficiency, a collaborative optimization method that can dynamically adjust the energy output of the system is proposed. The results indicate that compared with the IES-ORC system (without AA-CAES), the annual operation cost, CO2 emission and primary energy consumption of the IES ORC-CAES system can be reduced by 14.84%, 13.06% and 11.69% under the economic, environmental and energy efficiency objectives. Furthermore, by coordinating the regulation of ICE, AA-CAES and ORC, the flexibility of the system energy supply is realized, and the matching of the electric-heat ratio on the source and load side is improved. Finally, the thermodynamic analysis of the IES-ORC-CAES system is carried out through parameter analysis. The research results can provide the reference for the operation optimization of the complex IES.
When using multi-core systems, it is necessary to effectively use the mechanisms of parallelization the processes for the more productive using of computing power. Unlike other optimization technologies, genetic algor...
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ISBN:
(纸本)9781728167602
When using multi-core systems, it is necessary to effectively use the mechanisms of parallelization the processes for the more productive using of computing power. Unlike other optimization technologies, geneticalgorithms contains a population of trial solutions that are competitively managed using specific operators. geneticalgorithm is characterized by iterative training of a population of individuals. The power of geneticalgorithms is enhanced using distributed computing. Such algorithms are called parallel genetic algorithms. The paper suggests a mechanism that allows increasing the productivity of the method by using the criteria selection and the multi-parent crossover. This will reduce memory consumption and speed up the work. Thus, the use of multi-core systems will be more rational.
The aim of test paper composing is to compose an optimization test paper that satisfies the parameters which the user inputs, so the test paper composing problem is a classical multi-objective linear programming probl...
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ISBN:
(纸本)9783642253485;9783642253492
The aim of test paper composing is to compose an optimization test paper that satisfies the parameters which the user inputs, so the test paper composing problem is a classical multi-objective linear programming problem. This paper proposes an intelligent algorithm to generating test paper based on parallel genetic algorithm, and provides a set of schemes of making papers of different degree of difficulties display in normal distribution. The algorithm adopts a new decimal system of subsection code, improves the traditional method of initializing the population and optimizes course of search. The experiment proves that this algorithm has better performance thus is more practical.
Because the existed approaches to harden networks have an unavoidable exponential worse-case complexity, and are not scalable to large networks, this paper proposes an optimal network hardening model (ONHM) based on p...
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ISBN:
(纸本)9780769547923
Because the existed approaches to harden networks have an unavoidable exponential worse-case complexity, and are not scalable to large networks, this paper proposes an optimal network hardening model (ONHM) based on parallel genetic algorithm by combining attack graphs and parallel genetic algorithm. Firstly, we describe the optimal network hardening problem;secondly, we establish a corresponding mathematical model, which converts the optimal network hardening problem to a non-restraint optimization problem with penalty. Through a large number of repeated laboratory tests, the experimental results show ONHM can find the optimal network hardening, and can be applied to large-scale networks.
In multimodal optimization, maintaining population diversity is one of the most critical issues in geneticalgorithm design. A number of niching techniques have been developed and successfully applied to cope with thi...
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ISBN:
(纸本)9781467315098
In multimodal optimization, maintaining population diversity is one of the most critical issues in geneticalgorithm design. A number of niching techniques have been developed and successfully applied to cope with this problem. For multi-population based parallel genetic algorithms, nevertheless, these approaches are obviously inapplicable, since it is very difficult to obtain global information about entire population during parallel evolution procedure. In the present study, a new island model is proposed to overcome this problem. The new method indiscriminately directs local GAs search with considering the topological information of island model. It only uses local information obtained from a few neighbouring subpopulations to achieve a global population diversification. In the new island model, subpopulations are automatically allocated to different regions of searching space so that they could locate multiple optima including both global optima and local optima, simultaneously orders these found optima according to the connection topology of islands, and keeps them until the end of evolution. In addition, through using the proposed method, the performance of PGA is also improved and displays an enhanced global searching capability. Finally, experimental studies, in both unconstrained optimization and combinatorial optimization, are employed to demonstrate the performance of the new island model.
This paper presents a parallel genetic algorithm for the job shop scheduling problem (JSP). There are following innovations in this new algorithm: active schedules are created by the priority rules of Giffler and Thom...
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ISBN:
(纸本)9781467356046
This paper presents a parallel genetic algorithm for the job shop scheduling problem (JSP). There are following innovations in this new algorithm: active schedules are created by the priority rules of Giffler and Thompson [1];the mutation uses neighborhood searching techniques;the crossover uses GT algorithm and is performed on 3 parents. We illustrate this new method on the parameters of Muth and Thompson's benchmark problems. it can produce optimal solutions at a high percentage of accuracy. Our proposed method is preeminent in comparison with other methods on both the calculation time and the speed of finding optimal solutions.
Cloud computing is a novel parallel platform, this paper proposed a kind of simple parallel genetic algorithm (PGA) using Cloud computing called SMRPGA. Comparing with the traditional PGAs using high performance compu...
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ISBN:
(纸本)9783037852828
Cloud computing is a novel parallel platform, this paper proposed a kind of simple parallel genetic algorithm (PGA) using Cloud computing called SMRPGA. Comparing with the traditional PGAs using high performance computers (HPC), cluster or Grid, SMRPGA is simple and easy to be implemented. Another advantage is that PGA using Cloud computing is easy to be extend to larger-scale, which is very useful for solving the time-consuming problems. A prototype is implemented based on Hadoop, which is an open source Cloud computing. The result of running two benchmark functions showed that the speed-up of PGA using Cloud Computing is not obvious considering the long communication time and it is suitable to solve the time-consuming problems.
A phylogenetic tree represents the evolutionary relationships among biological species. Although parallel computation is essential for the phylogenetic tree searches, it is not easy to maintain the diversity of popula...
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ISBN:
(纸本)9781467315098
A phylogenetic tree represents the evolutionary relationships among biological species. Although parallel computation is essential for the phylogenetic tree searches, it is not easy to maintain the diversity of population in a parallel genetic algorithm. In this paper, we design a new asynchronous parallel genetic algorithm for tree optimization which maintain the diversity of population without any communication or synchronization.
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